Music Genre Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VTVD7-Z_zdPG"
   },
   "source": [
    "Music genre Prediction using different models. We first use Genre dataframe created using spotipy api"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "awoBnMPrzXqH"
   },
   "source": [
    "KNN Model \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yvYb_ue2QGMf"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 267
    },
    "colab_type": "code",
    "id": "B8f91u0BRKo-",
    "outputId": "38ea5ae0-618e-4a5d-fc1b-567e432617fe"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>artist_name</th>\n",
       "      <th>track_name</th>\n",
       "      <th>track_id</th>\n",
       "      <th>artist_href</th>\n",
       "      <th>track_href</th>\n",
       "      <th>genre_name</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jason mraz</td>\n",
       "      <td>i'm yours</td>\n",
       "      <td>1EzrEOXmMH3G43AXT1y7pA</td>\n",
       "      <td>https://open.spotify.com/artist/4phGZZrJZRo4El...</td>\n",
       "      <td>https://open.spotify.com/track/1EzrEOXmMH3G43A...</td>\n",
       "      <td>acoustic</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.444</td>\n",
       "      <td>11</td>\n",
       "      <td>-9.331</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0417</td>\n",
       "      <td>0.5590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0973</td>\n",
       "      <td>0.712</td>\n",
       "      <td>150.960</td>\n",
       "      <td>242947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chord overstreet</td>\n",
       "      <td>hold on</td>\n",
       "      <td>5vjLSffimiIP26QG5WcN2K</td>\n",
       "      <td>https://open.spotify.com/artist/5D3muNJhYYunbR...</td>\n",
       "      <td>https://open.spotify.com/track/5vjLSffimiIP26Q...</td>\n",
       "      <td>acoustic</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.443</td>\n",
       "      <td>2</td>\n",
       "      <td>-9.681</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0526</td>\n",
       "      <td>0.4690</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0829</td>\n",
       "      <td>0.167</td>\n",
       "      <td>119.949</td>\n",
       "      <td>198853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>the paper kites</td>\n",
       "      <td>bloom - bonus track</td>\n",
       "      <td>1HMQmOWrkieKYWlFsjUP3D</td>\n",
       "      <td>https://open.spotify.com/artist/79hrYiudVcFyyx...</td>\n",
       "      <td>https://open.spotify.com/track/1HMQmOWrkieKYWl...</td>\n",
       "      <td>acoustic</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.416</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.794</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0246</td>\n",
       "      <td>0.8820</td>\n",
       "      <td>0.000641</td>\n",
       "      <td>0.1120</td>\n",
       "      <td>0.415</td>\n",
       "      <td>96.003</td>\n",
       "      <td>210080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sara bareilles</td>\n",
       "      <td>love song</td>\n",
       "      <td>4E6cwWJWZw2zWf7VFbH7wf</td>\n",
       "      <td>https://open.spotify.com/artist/2Sqr0DXoaYABbj...</td>\n",
       "      <td>https://open.spotify.com/track/4E6cwWJWZw2zWf7...</td>\n",
       "      <td>acoustic</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.786</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.142</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0301</td>\n",
       "      <td>0.0208</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1880</td>\n",
       "      <td>0.573</td>\n",
       "      <td>123.055</td>\n",
       "      <td>258827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>the paper kites</td>\n",
       "      <td>paint</td>\n",
       "      <td>6uHvbKL0Yi37AuvNRmUfMw</td>\n",
       "      <td>https://open.spotify.com/artist/79hrYiudVcFyyx...</td>\n",
       "      <td>https://open.spotify.com/track/6uHvbKL0Yi37Auv...</td>\n",
       "      <td>acoustic</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.308</td>\n",
       "      <td>4</td>\n",
       "      <td>-13.052</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0402</td>\n",
       "      <td>0.8950</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>0.387</td>\n",
       "      <td>73.328</td>\n",
       "      <td>191773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        artist_name           track_name                track_id   \n",
       "0        jason mraz            i'm yours  1EzrEOXmMH3G43AXT1y7pA  \\\n",
       "1  chord overstreet              hold on  5vjLSffimiIP26QG5WcN2K   \n",
       "2   the paper kites  bloom - bonus track  1HMQmOWrkieKYWlFsjUP3D   \n",
       "3    sara bareilles            love song  4E6cwWJWZw2zWf7VFbH7wf   \n",
       "4   the paper kites                paint  6uHvbKL0Yi37AuvNRmUfMw   \n",
       "\n",
       "                                         artist_href   \n",
       "0  https://open.spotify.com/artist/4phGZZrJZRo4El...  \\\n",
       "1  https://open.spotify.com/artist/5D3muNJhYYunbR...   \n",
       "2  https://open.spotify.com/artist/79hrYiudVcFyyx...   \n",
       "3  https://open.spotify.com/artist/2Sqr0DXoaYABbj...   \n",
       "4  https://open.spotify.com/artist/79hrYiudVcFyyx...   \n",
       "\n",
       "                                          track_href genre_name  danceability   \n",
       "0  https://open.spotify.com/track/1EzrEOXmMH3G43A...   acoustic         0.703  \\\n",
       "1  https://open.spotify.com/track/5vjLSffimiIP26Q...   acoustic         0.618   \n",
       "2  https://open.spotify.com/track/1HMQmOWrkieKYWl...   acoustic         0.591   \n",
       "3  https://open.spotify.com/track/4E6cwWJWZw2zWf7...   acoustic         0.583   \n",
       "4  https://open.spotify.com/track/6uHvbKL0Yi37Auv...   acoustic         0.364   \n",
       "\n",
       "   energy  key  loudness  mode  speechiness  acousticness  instrumentalness   \n",
       "0   0.444   11    -9.331     1       0.0417        0.5590          0.000000  \\\n",
       "1   0.443    2    -9.681     1       0.0526        0.4690          0.000000   \n",
       "2   0.416    1    -9.794     1       0.0246        0.8820          0.000641   \n",
       "3   0.786    2    -3.142     0       0.0301        0.0208          0.000000   \n",
       "4   0.308    4   -13.052     1       0.0402        0.8950          0.002300   \n",
       "\n",
       "   liveness  valence    tempo  duration_ms  \n",
       "0    0.0973    0.712  150.960       242947  \n",
       "1    0.0829    0.167  119.949       198853  \n",
       "2    0.1120    0.415   96.003       210080  \n",
       "3    0.1880    0.573  123.055       258827  \n",
       "4    0.1230    0.387   73.328       191773  "
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join(\"Downloads/spotify_df1.csv\"))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
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   "outputs": [
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       "      <td>1</td>\n",
       "      <td>-9.794</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.1120</td>\n",
       "      <td>0.415</td>\n",
       "      <td>96.003</td>\n",
       "      <td>210080</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>acoustic</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.786</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.142</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0301</td>\n",
       "      <td>0.0208</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1880</td>\n",
       "      <td>0.573</td>\n",
       "      <td>123.055</td>\n",
       "      <td>258827</td>\n",
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       "      <th>4</th>\n",
       "      <td>acoustic</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.308</td>\n",
       "      <td>4</td>\n",
       "      <td>-13.052</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0402</td>\n",
       "      <td>0.8950</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>0.387</td>\n",
       "      <td>73.328</td>\n",
       "      <td>191773</td>\n",
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      ],
      "text/plain": [
       "  genre_name  danceability  energy  key  loudness  mode  speechiness   \n",
       "0   acoustic         0.703   0.444   11    -9.331     1       0.0417  \\\n",
       "1   acoustic         0.618   0.443    2    -9.681     1       0.0526   \n",
       "2   acoustic         0.591   0.416    1    -9.794     1       0.0246   \n",
       "3   acoustic         0.583   0.786    2    -3.142     0       0.0301   \n",
       "4   acoustic         0.364   0.308    4   -13.052     1       0.0402   \n",
       "\n",
       "   acousticness  instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "0        0.5590          0.000000    0.0973    0.712  150.960       242947  \n",
       "1        0.4690          0.000000    0.0829    0.167  119.949       198853  \n",
       "2        0.8820          0.000641    0.1120    0.415   96.003       210080  \n",
       "3        0.0208          0.000000    0.1880    0.573  123.055       258827  \n",
       "4        0.8950          0.002300    0.1230    0.387   73.328       191773  "
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ML_df = df.drop(['artist_name', 'track_name', 'track_id', 'artist_href', 'track_href'], axis=1)\n",
    "ML_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c-hNmZBTRR9k"
   },
   "outputs": [],
   "source": [
    "y = ML_df[\"genre_name\"]"
   ]
  },
  {
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   "execution_count": 305,
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       "      <td>0.1230</td>\n",
       "      <td>0.387</td>\n",
       "      <td>73.328</td>\n",
       "      <td>191773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   danceability  energy  key  loudness  mode  speechiness  acousticness   \n",
       "0         0.703   0.444   11    -9.331     1       0.0417        0.5590  \\\n",
       "1         0.618   0.443    2    -9.681     1       0.0526        0.4690   \n",
       "2         0.591   0.416    1    -9.794     1       0.0246        0.8820   \n",
       "3         0.583   0.786    2    -3.142     0       0.0301        0.0208   \n",
       "4         0.364   0.308    4   -13.052     1       0.0402        0.8950   \n",
       "\n",
       "   instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "0          0.000000    0.0973    0.712  150.960       242947  \n",
       "1          0.000000    0.0829    0.167  119.949       198853  \n",
       "2          0.000641    0.1120    0.415   96.003       210080  \n",
       "3          0.000000    0.1880    0.573  123.055       258827  \n",
       "4          0.002300    0.1230    0.387   73.328       191773  "
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = ML_df.drop(\"genre_name\", axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mZnGHyseR1CX"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-adDp5hvdlbw"
   },
   "source": [
    "Baseline Accuracy\n",
    "DummyClassifier model is to establish a baseline performance metric for comparison with other, more sophisticated models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "cJ3DI-erc_8O",
    "outputId": "3b6a7412-57a5-4748-f1ed-fce8a4a53f19"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DummyClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyClassifier</label><div class=\"sk-toggleable__content\"><pre>DummyClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DummyClassifier()"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "dummy_clf = DummyClassifier()\n",
    "dummy_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "QMUUEppsdAO9",
    "outputId": "1ad16da1-7c3c-4768-c380-19dc471c9155"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False]\n"
     ]
    }
   ],
   "source": [
    "y_pred = dummy_clf.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "588bctWlc_zr",
    "outputId": "6784b5b6-0034-49ff-87e8-9426740dd3e0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8695652173913043\n"
     ]
    }
   ],
   "source": [
    "score = dummy_clf.score(X_test, y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K value: 1, Train/Test Accuracy: 1.000/0.752, Overall Accuracy: 1.752\n",
      "K value: 3, Train/Test Accuracy: 0.875/0.815, Overall Accuracy: 1.690\n",
      "K value: 5, Train/Test Accuracy: 0.866/0.852, Overall Accuracy: 1.718\n",
      "K value: 7, Train/Test Accuracy: 0.848/0.861, Overall Accuracy: 1.709\n",
      "K value: 9, Train/Test Accuracy: 0.851/0.870, Overall Accuracy: 1.721\n",
      "K value: 11, Train/Test Accuracy: 0.847/0.870, Overall Accuracy: 1.717\n",
      "K value: 13, Train/Test Accuracy: 0.848/0.870, Overall Accuracy: 1.718\n",
      "K value: 15, Train/Test Accuracy: 0.848/0.870, Overall Accuracy: 1.718\n",
      "K value: 17, Train/Test Accuracy: 0.848/0.870, Overall Accuracy: 1.718\n",
      "K value: 19, Train/Test Accuracy: 0.848/0.870, Overall Accuracy: 1.718\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_accuracy = []\n",
    "test_accuracy = []\n",
    "for k in range(1, 20, 2):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    train_acc = knn.score(X_train, y_train)\n",
    "    test_acc = knn.score(X_test, y_test)\n",
    "    overall_acc = train_acc + test_acc\n",
    "    train_accuracy.append(train_acc)\n",
    "    test_accuracy.append(test_acc)\n",
    "    print(f\"K value: {k}, Train/Test Accuracy: {train_acc:.3f}/{test_acc:.3f}, Overall Accuracy: {overall_acc:.3f}\")\n",
    "    \n",
    "plt.plot(range(1, 20, 2), train_accuracy, marker='o')\n",
    "plt.plot(range(1, 20, 2), test_accuracy, marker=\"x\")\n",
    "plt.xlabel(\"Number of Neighbors (k)\")\n",
    "plt.ylabel(\"Accuracy Score\")\n",
    "plt.title(\"KNN Classifier Accuracy\")\n",
    "plt.legend([\"Training Accuracy\", \"Testing Accuracy\"])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "colab_type": "code",
    "id": "nEV-ftNYSqGS",
    "outputId": "44ddce97-0997-437f-f93d-cedc7edf908e"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0-AWLZcJzsr3"
   },
   "source": [
    "SGD - Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "klr70cyLSzr9"
   },
   "outputs": [],
   "source": [
    "#Importing necessary Libraries\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L9ItQG-DVLsR"
   },
   "outputs": [],
   "source": [
    "X = ML_df[\"genre_name\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r1Iw7tqXVoOG"
   },
   "outputs": [],
   "source": [
    "y = ML_df.drop(\"genre_name\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VWjaTgSSuUvu"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I7xgcdDuuj1S"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vQcus92nup5N"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EyOCh5fgz6ll"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 450
    },
    "colab_type": "code",
    "id": "07Q7gFNx0y9q",
    "outputId": "654c154a-64f0-4915-bd51-ea7ececb5e0f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>track_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genre_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>acoustic</th>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>afrobeat</th>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alt-rock</th>\n",
       "      <td>296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alternative</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ambient</th>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kids</th>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latin</th>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latino</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malay</th>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mandopop</th>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             track_name\n",
       "genre_name             \n",
       "acoustic            280\n",
       "afrobeat            218\n",
       "alt-rock            296\n",
       "alternative          75\n",
       "ambient             291\n",
       "...                 ...\n",
       "kids                290\n",
       "latin               222\n",
       "latino               24\n",
       "malay               278\n",
       "mandopop            264\n",
       "\n",
       "[70 rows x 1 columns]"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_genre = df.groupby(\"genre_name\")[\"track_name\"].count()\n",
    "by_genre = pd.DataFrame(by_genre)\n",
    "by_genre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yCX25zbx0BFN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16421, 12) (16421, 1)\n"
     ]
    }
   ],
   "source": [
    "# Assign the data to X and y\n",
    "X = df[[\"danceability\", \"energy\", \"key\", \"loudness\", \"mode\", \"speechiness\", \"acousticness\", \"instrumentalness\", \"liveness\", \"valence\", \"tempo\", \"duration_ms\"]]\n",
    "y = df[\"genre_name\"].values.reshape(-1, 1)\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DUrqJaPT2twn"
   },
   "source": [
    "We shall try Several Linear Models now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241
    },
    "colab_type": "code",
    "id": "8igKI2O92vEt",
    "outputId": "a9c51acb-9cbe-402c-b4c9-b31efee64526"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>...</th>\n",
       "      <th>genre_name_j-idol</th>\n",
       "      <th>genre_name_j-pop</th>\n",
       "      <th>genre_name_j-rock</th>\n",
       "      <th>genre_name_jazz</th>\n",
       "      <th>genre_name_k-pop</th>\n",
       "      <th>genre_name_kids</th>\n",
       "      <th>genre_name_latin</th>\n",
       "      <th>genre_name_latino</th>\n",
       "      <th>genre_name_malay</th>\n",
       "      <th>genre_name_mandopop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.703</td>\n",
       "      <td>0.444</td>\n",
       "      <td>11</td>\n",
       "      <td>-9.331</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0417</td>\n",
       "      <td>0.5590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0973</td>\n",
       "      <td>0.712</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.618</td>\n",
       "      <td>0.443</td>\n",
       "      <td>2</td>\n",
       "      <td>-9.681</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0526</td>\n",
       "      <td>0.4690</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0829</td>\n",
       "      <td>0.167</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.591</td>\n",
       "      <td>0.416</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.794</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0246</td>\n",
       "      <td>0.8820</td>\n",
       "      <td>0.000641</td>\n",
       "      <td>0.1120</td>\n",
       "      <td>0.415</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.583</td>\n",
       "      <td>0.786</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.142</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0301</td>\n",
       "      <td>0.0208</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1880</td>\n",
       "      <td>0.573</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.364</td>\n",
       "      <td>0.308</td>\n",
       "      <td>4</td>\n",
       "      <td>-13.052</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0402</td>\n",
       "      <td>0.8950</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>0.387</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   danceability  energy  key  loudness  mode  speechiness  acousticness   \n",
       "0         0.703   0.444   11    -9.331     1       0.0417        0.5590  \\\n",
       "1         0.618   0.443    2    -9.681     1       0.0526        0.4690   \n",
       "2         0.591   0.416    1    -9.794     1       0.0246        0.8820   \n",
       "3         0.583   0.786    2    -3.142     0       0.0301        0.0208   \n",
       "4         0.364   0.308    4   -13.052     1       0.0402        0.8950   \n",
       "\n",
       "   instrumentalness  liveness  valence  ...  genre_name_j-idol   \n",
       "0          0.000000    0.0973    0.712  ...              False  \\\n",
       "1          0.000000    0.0829    0.167  ...              False   \n",
       "2          0.000641    0.1120    0.415  ...              False   \n",
       "3          0.000000    0.1880    0.573  ...              False   \n",
       "4          0.002300    0.1230    0.387  ...              False   \n",
       "\n",
       "   genre_name_j-pop  genre_name_j-rock  genre_name_jazz  genre_name_k-pop   \n",
       "0             False              False            False             False  \\\n",
       "1             False              False            False             False   \n",
       "2             False              False            False             False   \n",
       "3             False              False            False             False   \n",
       "4             False              False            False             False   \n",
       "\n",
       "   genre_name_kids  genre_name_latin  genre_name_latino  genre_name_malay   \n",
       "0            False             False              False             False  \\\n",
       "1            False             False              False             False   \n",
       "2            False             False              False             False   \n",
       "3            False             False              False             False   \n",
       "4            False             False              False             False   \n",
       "\n",
       "   genre_name_mandopop  \n",
       "0                False  \n",
       "1                False  \n",
       "2                False  \n",
       "3                False  \n",
       "4                False  \n",
       "\n",
       "[5 rows x 82 columns]"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas get_dummies to convert categorical data\n",
    "\n",
    "genretable = pd.get_dummies(ML_df)\n",
    "genretable.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 698
    },
    "colab_type": "code",
    "id": "dkw9IR1r3LRk",
    "outputId": "2e9f910f-fc9a-4ab2-ef60-f2f64acd8a6b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16421, 1) (16421, 1)\n"
     ]
    }
   ],
   "source": [
    "X = genretable[[\"danceability\"]]\n",
    "y = genretable[\"genre_name_dance\"].values.reshape(-1, 1)\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DFQJk-wz9dns"
   },
   "outputs": [],
   "source": [
    "# Split the data into training and testing\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QxNXFwLz-Djs"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Creating a StandardScater model and fit it to the training data\n",
    "X_scaler = StandardScaler().fit(X_train)\n",
    "y_scaler = StandardScaler().fit(y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dltwFrxS-FhV"
   },
   "outputs": [],
   "source": [
    "# Transforming the training and testing data using the X_scaler and y_scaler models\n",
    "\n",
    "# Importing the StandardScaler class from sklearn.preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Creating an instance of the StandardScaler class\n",
    "scaler_X = StandardScaler()\n",
    "scaler_y = StandardScaler()\n",
    "\n",
    "# Fitting the scaler to the training data and transforming both training and testing data\n",
    "X_train_scaled = scaler_X.fit_transform(X_train)\n",
    "X_test_scaled = scaler_X.transform(X_test)\n",
    "y_train_scaled = scaler_y.fit_transform(y_train)\n",
    "y_test_scaled = scaler_y.transform(y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Rpurwnfq-HDY",
    "outputId": "4a13f001-c818-43d6-e262-43308cd7b9b3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a LinearRegression model and fit it to the scaled training data\n",
    "\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "models = []\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(X_train_scaled, y_train_scaled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "bBdsJ91r-KzX",
    "outputId": "48a716ef-6635-4f86-9859-281c8c454260"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 1.8290078285065003, R2: 0.008449752623810536\n"
     ]
    }
   ],
   "source": [
    "# Using X_test_scaled, y_test_scaled, and model.predict(X_test_scaled) to calculate MSE and R2\n",
    "\n",
    "# Importing the mean_square_erro class from sklearn.metrics\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "MSE = mean_squared_error(y_test_scaled,X_test_scaled )\n",
    "r2 = model.score(X_test_scaled, y_test_scaled)\n",
    "\n",
    "print(f\"MSE: {MSE}, R2: {r2}\")\n",
    "models.append({\"model\": \"LinearRegression\",\n",
    "            \"MSE\": MSE,\n",
    "            \"R2\":r2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "DO9LIokm-Ogx",
    "outputId": "cdb38d14-c212-43da-80d9-34bd6b6dfcd0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LASSO MSE: 1.0250738687278018, R2: 0.008367392955300423\n"
     ]
    }
   ],
   "source": [
    "# Lasso model\n",
    "from sklearn.linear_model import Lasso\n",
    "alpha = 0.01\n",
    "lasso = Lasso(alpha=alpha)\n",
    "lasso.fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "lasso_predictions = lasso.predict(X_test_scaled)\n",
    "\n",
    "lasso_mse = mean_squared_error(y_test_scaled, lasso_predictions)\n",
    "lasso_r2 = lasso.score(X_test_scaled, y_test_scaled)\n",
    "\n",
    "print(f\"LASSO MSE: {lasso_mse}, R2: {lasso_r2}\")\n",
    "models.append({\"model\": \"Lasso\", \"MSE\": lasso_mse, \"R2\": lasso_r2})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ponyikm9-t8N",
    "outputId": "9ccccc78-6a58-4ec6-f07f-043aa9d0059c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge model MSE: 1.02498873150826, R2 score: 0.008449752720334214\n"
     ]
    }
   ],
   "source": [
    "##Ridge Model\n",
    "# Define Ridge model with alpha value of 0.01\n",
    "from sklearn.linear_model import Ridge\n",
    "ridge_model = Ridge(alpha=0.01)\n",
    "\n",
    "# Fit the scaled training data to the Ridge model\n",
    "X_train_scaled_ridge = X_scaler.transform(X_train)\n",
    "y_train_scaled_ridge = y_scaler.transform(y_train)\n",
    "ridge_model.fit(X_train_scaled_ridge, y_train_scaled_ridge)\n",
    "\n",
    "# Use the Ridge model to predict values for the scaled test data\n",
    "X_test_scaled_ridge = X_scaler.transform(X_test)\n",
    "y_test_scaled_ridge = y_scaler.transform(y_test)\n",
    "predictions_ridge = ridge_model.predict(X_test_scaled_ridge)\n",
    "\n",
    "# Calculate the mean squared error and R2 score for the Ridge model predictions\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "mse_ridge = mean_squared_error(y_test_scaled_ridge, predictions_ridge)\n",
    "r2_ridge = r2_score(y_test_scaled_ridge, predictions_ridge)\n",
    "\n",
    "# Print the MSE and R2 score for the Ridge model predictions\n",
    "print(f\"Ridge model MSE: {mse_ridge}, R2 score: {r2_ridge}\")\n",
    "\n",
    "# Append the Ridge model name, MSE, and R2 score to the models list\n",
    "models.append({\"model\": \"Ridge\",\n",
    "               \"MSE\": mse_ridge,\n",
    "               \"R2\": r2_ridge})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "9-DW7_AQ-wEd",
    "outputId": "61bd82da-17d8-4870-a218-a06c87aff5a7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ElasticNet model MSE: 1.0250108015878807, R2 score: 0.008428402638881782\n"
     ]
    }
   ],
   "source": [
    "# # ElasticNet model\n",
    "\n",
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "# create an ElasticNet model with alpha=0.01\n",
    "en_model = ElasticNet(alpha=0.01)\n",
    "\n",
    "# fit the model to the scaled training data\n",
    "en_model.fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "# use the model to predict the output on the scaled test data\n",
    "y_pred_scaled = en_model.predict(X_test_scaled)\n",
    "\n",
    "# calculate the mean squared error (MSE) and R-squared (R2) score\n",
    "mse = mean_squared_error(y_test_scaled, y_pred_scaled)\n",
    "r2 = en_model.score(X_test_scaled, y_test_scaled)\n",
    "\n",
    "# Print the MSE and R2 score for the Ridge model predictions\n",
    "print(f\"ElasticNet model MSE: {mse}, R2 score: {r2}\")\n",
    "# add the model results to a list\n",
    "models.append({\n",
    "    \"model\": \"ElasticNet\",\n",
    "    \"MSE\": mse,\n",
    "    \"R2\": r2\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "colab_type": "code",
    "id": "Sg9Y-GcX-x1C",
    "outputId": "6eb9c1c4-e855-4302-c5c8-caf91fd4a93a"
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>MSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>model_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1.024989</td>\n",
       "      <td>0.00845</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1.024989</td>\n",
       "      <td>0.00845</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   model       MSE       R2 model_name\n",
       "3  Ridge  1.024989  0.00845        NaN\n",
       "8  Ridge  1.024989  0.00845        NaN"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.DataFrame(models)\n",
    "a[a[\"R2\"]==a[\"R2\"].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "colab_type": "code",
    "id": "maH_rVle-zwb",
    "outputId": "1e70ff53-73b9-49a4-a111-f59844b9b41e"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>MSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>model_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>1.829008</td>\n",
       "      <td>0.008450</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1.025074</td>\n",
       "      <td>0.008367</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1.025074</td>\n",
       "      <td>0.008367</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1.024989</td>\n",
       "      <td>0.008450</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.025011</td>\n",
       "      <td>0.008428</td>\n",
       "      <td>ElasticNet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.025011</td>\n",
       "      <td>0.008428</td>\n",
       "      <td>ElasticNet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>1.829008</td>\n",
       "      <td>0.008450</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1.025074</td>\n",
       "      <td>0.008367</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>1.024989</td>\n",
       "      <td>0.008450</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ElasticNet</td>\n",
       "      <td>1.025011</td>\n",
       "      <td>0.008428</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              model       MSE        R2  model_name\n",
       "0  LinearRegression  1.829008  0.008450         NaN\n",
       "1             Lasso  1.025074  0.008367         NaN\n",
       "2             Lasso  1.025074  0.008367         NaN\n",
       "3             Ridge  1.024989  0.008450         NaN\n",
       "4               NaN  1.025011  0.008428  ElasticNet\n",
       "5               NaN  1.025011  0.008428  ElasticNet\n",
       "6  LinearRegression  1.829008  0.008450         NaN\n",
       "7             Lasso  1.025074  0.008367         NaN\n",
       "8             Ridge  1.024989  0.008450         NaN\n",
       "9        ElasticNet  1.025011  0.008428         NaN"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NH-iOEqq-1hj"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6UWVBurR_o3h"
   },
   "source": [
    "Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fop-RhEG_8PP"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "id": "QrGVNRaOAqu9",
    "outputId": "ded10524-e26e-4760-c9e9-739a03f66fd8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_name</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>acoustic</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.444</td>\n",
       "      <td>11</td>\n",
       "      <td>-9.331</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0417</td>\n",
       "      <td>0.5590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0973</td>\n",
       "      <td>0.712</td>\n",
       "      <td>150.960</td>\n",
       "      <td>242947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>acoustic</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.443</td>\n",
       "      <td>2</td>\n",
       "      <td>-9.681</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0526</td>\n",
       "      <td>0.4690</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0829</td>\n",
       "      <td>0.167</td>\n",
       "      <td>119.949</td>\n",
       "      <td>198853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>acoustic</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.416</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.794</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0246</td>\n",
       "      <td>0.8820</td>\n",
       "      <td>0.000641</td>\n",
       "      <td>0.1120</td>\n",
       "      <td>0.415</td>\n",
       "      <td>96.003</td>\n",
       "      <td>210080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>acoustic</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.786</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.142</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0301</td>\n",
       "      <td>0.0208</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1880</td>\n",
       "      <td>0.573</td>\n",
       "      <td>123.055</td>\n",
       "      <td>258827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>acoustic</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.308</td>\n",
       "      <td>4</td>\n",
       "      <td>-13.052</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0402</td>\n",
       "      <td>0.8950</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>0.387</td>\n",
       "      <td>73.328</td>\n",
       "      <td>191773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16416</th>\n",
       "      <td>mandopop</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.297</td>\n",
       "      <td>9</td>\n",
       "      <td>-8.785</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0309</td>\n",
       "      <td>0.6920</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.1070</td>\n",
       "      <td>0.125</td>\n",
       "      <td>180.095</td>\n",
       "      <td>357094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16417</th>\n",
       "      <td>mandopop</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.529</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.928</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0406</td>\n",
       "      <td>0.5590</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.1630</td>\n",
       "      <td>0.267</td>\n",
       "      <td>133.967</td>\n",
       "      <td>242293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16418</th>\n",
       "      <td>mandopop</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.304</td>\n",
       "      <td>10</td>\n",
       "      <td>-9.115</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0335</td>\n",
       "      <td>0.8340</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1170</td>\n",
       "      <td>0.413</td>\n",
       "      <td>124.781</td>\n",
       "      <td>223570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16419</th>\n",
       "      <td>mandopop</td>\n",
       "      <td>0.605</td>\n",
       "      <td>0.310</td>\n",
       "      <td>7</td>\n",
       "      <td>-10.276</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0390</td>\n",
       "      <td>0.8490</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1360</td>\n",
       "      <td>0.292</td>\n",
       "      <td>118.028</td>\n",
       "      <td>245521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16420</th>\n",
       "      <td>mandopop</td>\n",
       "      <td>0.588</td>\n",
       "      <td>0.405</td>\n",
       "      <td>9</td>\n",
       "      <td>-7.328</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0265</td>\n",
       "      <td>0.5790</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.3090</td>\n",
       "      <td>0.167</td>\n",
       "      <td>111.760</td>\n",
       "      <td>280524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16421 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      genre_name  danceability  energy  key  loudness  mode  speechiness   \n",
       "0       acoustic         0.703   0.444   11    -9.331     1       0.0417  \\\n",
       "1       acoustic         0.618   0.443    2    -9.681     1       0.0526   \n",
       "2       acoustic         0.591   0.416    1    -9.794     1       0.0246   \n",
       "3       acoustic         0.583   0.786    2    -3.142     0       0.0301   \n",
       "4       acoustic         0.364   0.308    4   -13.052     1       0.0402   \n",
       "...          ...           ...     ...  ...       ...   ...          ...   \n",
       "16416   mandopop         0.460   0.297    9    -8.785     1       0.0309   \n",
       "16417   mandopop         0.720   0.529   10    -8.928     1       0.0406   \n",
       "16418   mandopop         0.657   0.304   10    -9.115     1       0.0335   \n",
       "16419   mandopop         0.605   0.310    7   -10.276     1       0.0390   \n",
       "16420   mandopop         0.588   0.405    9    -7.328     0       0.0265   \n",
       "\n",
       "       acousticness  instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "0            0.5590          0.000000    0.0973    0.712  150.960       242947  \n",
       "1            0.4690          0.000000    0.0829    0.167  119.949       198853  \n",
       "2            0.8820          0.000641    0.1120    0.415   96.003       210080  \n",
       "3            0.0208          0.000000    0.1880    0.573  123.055       258827  \n",
       "4            0.8950          0.002300    0.1230    0.387   73.328       191773  \n",
       "...             ...               ...       ...      ...      ...          ...  \n",
       "16416        0.6920          0.000318    0.1070    0.125  180.095       357094  \n",
       "16417        0.5590          0.000021    0.1630    0.267  133.967       242293  \n",
       "16418        0.8340          0.000000    0.1170    0.413  124.781       223570  \n",
       "16419        0.8490          0.000000    0.1360    0.292  118.028       245521  \n",
       "16420        0.5790          0.000000    0.3090    0.167  111.760       280524  \n",
       "\n",
       "[16421 rows x 13 columns]"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ML_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "0LGW8cHUAEEG",
    "outputId": "7eec8b8a-4ffc-4639-9e46-eb200348d714"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16421, 12) (16421,)\n"
     ]
    }
   ],
   "source": [
    "# Assign X (data) and y (target)\n",
    "X = ML_df.drop(\"genre_name\", axis=1)\n",
    "y = ML_df[\"genre_name\"]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SGkwfVNpAnwj"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "iXGsnBX3Ax0n",
    "outputId": "9f23e4f6-2ce2-4eff-cbbd-e1b85ea81cbb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-15 {color: black;background-color: white;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-15 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\n",
    "classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "p238xVSIAznc",
    "outputId": "e9bad55f-79cd-4688-f212-63eca8a4d724"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6155         deep-house\n",
       "10412            groove\n",
       "6452     detroit-techno\n",
       "12358           hip-hop\n",
       "2198              blues\n",
       "              ...      \n",
       "10921             happy\n",
       "2117              blues\n",
       "6367     detroit-techno\n",
       "5750          dancehall\n",
       "15256             k-pop\n",
       "Name: genre_name, Length: 12315, dtype: object"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "8XcuM1BFA1Ss",
    "outputId": "64d7a561-b9f3-45c8-e32d-7c99f8e63639"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "HGLc4W3RA3sa",
    "outputId": "1140c176-8095-4187-8840-817b77e400fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score: 0.04734064149411287\n",
      "Testing Data Score: 0.04870920603994155\n"
     ]
    }
   ],
   "source": [
    "print(f\"Training Data Score: {classifier.score(X_train, y_train)}\")\n",
    "print(f\"Testing Data Score: {classifier.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "rIM1YnKLBS8D",
    "outputId": "c24594ac-b192-4635-d8f6-1fa3ac97c65f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 Predictions:   ['drum-and-bass' 'children' 'drum-and-bass' 'drum-and-bass'\n",
      " 'chicago-house' 'happy' 'drum-and-bass' 'drum-and-bass' 'chicago-house'\n",
      " 'hardstyle']\n",
      "First 10 Actual labels: ['hardstyle', 'classical', 'drum-and-bass', 'folk', 'british', 'alt-rock', 'black-metal', 'k-pop', 'j-rock', 'honky-tonk']\n"
     ]
    }
   ],
   "source": [
    "predictions = classifier.predict(X_test)\n",
    "print(f\"First 10 Predictions:   {predictions[:10]}\")\n",
    "print(f\"First 10 Actual labels: {y_test[:10].tolist()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "id": "Te9gjW-pBYF7",
    "outputId": "d69ced4b-cfa0-425c-a7f7-be3a0c11601f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>drum-and-bass</td>\n",
       "      <td>hardstyle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>children</td>\n",
       "      <td>classical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>drum-and-bass</td>\n",
       "      <td>drum-and-bass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>drum-and-bass</td>\n",
       "      <td>folk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chicago-house</td>\n",
       "      <td>british</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4101</th>\n",
       "      <td>drum-and-bass</td>\n",
       "      <td>indie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4102</th>\n",
       "      <td>breakbeat</td>\n",
       "      <td>disco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4103</th>\n",
       "      <td>cantopop</td>\n",
       "      <td>acoustic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4104</th>\n",
       "      <td>chicago-house</td>\n",
       "      <td>heavy-metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4105</th>\n",
       "      <td>chicago-house</td>\n",
       "      <td>funk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4106 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Prediction         Actual\n",
       "0     drum-and-bass      hardstyle\n",
       "1          children      classical\n",
       "2     drum-and-bass  drum-and-bass\n",
       "3     drum-and-bass           folk\n",
       "4     chicago-house        british\n",
       "...             ...            ...\n",
       "4101  drum-and-bass          indie\n",
       "4102      breakbeat          disco\n",
       "4103       cantopop       acoustic\n",
       "4104  chicago-house    heavy-metal\n",
       "4105  chicago-house           funk\n",
       "\n",
       "[4106 rows x 2 columns]"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Prediction\": predictions, \"Actual\": y_test}).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rfQdA7p1Ftf0"
   },
   "source": [
    "Trying with Neural Network Deep Learning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing necessary libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "SrWTPqMfF17I",
    "outputId": "3e9e3c2b-c322-4773-faf5-2215e482e839"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16421, 12) (16421,)\n"
     ]
    }
   ],
   "source": [
    "# Assign X (data) and y (target)\n",
    "X = ML_df.drop(\"genre_name\", axis=1)\n",
    "y = ML_df[\"genre_name\"]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ltHyZdvpF8B8"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "niKGMmGsF9u9"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xnLoTwoBGABc"
   },
   "outputs": [],
   "source": [
    "X_scaler = MinMaxScaler().fit(X_train)\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jUN3MoEYGDUV"
   },
   "outputs": [],
   "source": [
    "# Step 1: Label-encode data set\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoder.fit(y_train)\n",
    "encoded_y_train = label_encoder.transform(y_train)\n",
    "encoded_y_test = label_encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AQfXLbPLGE7o"
   },
   "outputs": [],
   "source": [
    "# Step 2: Convert encoded labels to one-hot-encoding\n",
    "y_train_categorical = to_categorical(encoded_y_train)\n",
    "y_test_categorical = to_categorical(encoded_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "J5nTitbMGGWb"
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ayQk0qIlGH0y"
   },
   "outputs": [],
   "source": [
    "# Create model and add layers\n",
    "model = Sequential()\n",
    "model.add(Dense(units=70, activation='relu', input_dim=12))\n",
    "model.add(Dense(units=70, activation='relu'))\n",
    "model.add(Dense(units=70, activation='relu'))\n",
    "model.add(Dense(units=70, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dStjnWMSGJrH"
   },
   "outputs": [],
   "source": [
    "# Compile and fit the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "JCe13BDbGLmW",
    "outputId": "29293215-a115-4540-a3a6-b9b0b1bdded5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_26\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_109 (Dense)           (None, 70)                910       \n",
      "                                                                 \n",
      " dense_110 (Dense)           (None, 70)                4970      \n",
      "                                                                 \n",
      " dense_111 (Dense)           (None, 70)                4970      \n",
      "                                                                 \n",
      " dense_112 (Dense)           (None, 70)                4970      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 15,820\n",
      "Trainable params: 15,820\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "YylEIRSgGM-I",
    "outputId": "8d876036-b12d-44c8-efff-11cc59024067"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "385/385 - 1s - loss: 3.7595 - accuracy: 0.0833 - 1s/epoch - 3ms/step\n",
      "Epoch 2/10\n",
      "385/385 - 0s - loss: 3.3426 - accuracy: 0.1429 - 367ms/epoch - 953us/step\n",
      "Epoch 3/10\n",
      "385/385 - 0s - loss: 3.2227 - accuracy: 0.1667 - 351ms/epoch - 911us/step\n",
      "Epoch 4/10\n",
      "385/385 - 0s - loss: 3.1498 - accuracy: 0.1839 - 367ms/epoch - 953us/step\n",
      "Epoch 5/10\n",
      "385/385 - 0s - loss: 3.0987 - accuracy: 0.1975 - 358ms/epoch - 931us/step\n",
      "Epoch 6/10\n",
      "385/385 - 0s - loss: 3.0686 - accuracy: 0.2017 - 350ms/epoch - 910us/step\n",
      "Epoch 7/10\n",
      "385/385 - 0s - loss: 3.0387 - accuracy: 0.2072 - 373ms/epoch - 969us/step\n",
      "Epoch 8/10\n",
      "385/385 - 0s - loss: 3.0128 - accuracy: 0.2149 - 352ms/epoch - 913us/step\n",
      "Epoch 9/10\n",
      "385/385 - 0s - loss: 2.9788 - accuracy: 0.2191 - 358ms/epoch - 931us/step\n",
      "Epoch 10/10\n",
      "385/385 - 0s - loss: 2.9605 - accuracy: 0.2216 - 366ms/epoch - 950us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x279a96dc490>"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    X_train_scaled,\n",
    "    y_train_categorical,\n",
    "    epochs=10,\n",
    "    shuffle=True,\n",
    "    verbose=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "BOZi5OzpGQSY",
    "outputId": "59eb9070-3418-4f53-efee-915fcc232e72"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "129/129 - 0s - loss: 3.0211 - accuracy: 0.2094 - 213ms/epoch - 2ms/step\n",
      "Normal Neural Network - Loss: 3.02108097076416, Accuracy: 0.20944958925247192\n"
     ]
    }
   ],
   "source": [
    "model_loss, model_accuracy = model.evaluate(\n",
    "    X_test_scaled, y_test_categorical, verbose=2)\n",
    "print(\n",
    "    f\"Normal Neural Network - Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zrX_gPNVP39d"
   },
   "outputs": [],
   "source": [
    "# Create model and add layers\n",
    "model = Sequential()\n",
    "model.add(Dense(units=70, activation='relu', input_dim=12))\n",
    "\n",
    "model.add(Dense(units=70, activation='relu'))\n",
    "# model.add(Dense(units=70, activation='relu'))\n",
    "# model.add(Dense(units=70, activation='relu'))\n",
    "model.add(Dense(units=70, activation='sigmoid'))\n",
    "# model.add(Dense(units=70, activation='softmax'))\n",
    "model.add(Dense(units=70, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7pdbsu5bpLxK"
   },
   "outputs": [],
   "source": [
    "# Compile and fit the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "Z882fMm9pRT5",
    "outputId": "a01f61f2-4a1f-4d51-81a7-e45f48a5f6d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_27\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_113 (Dense)           (None, 70)                910       \n",
      "                                                                 \n",
      " dense_114 (Dense)           (None, 70)                4970      \n",
      "                                                                 \n",
      " dense_115 (Dense)           (None, 70)                4970      \n",
      "                                                                 \n",
      " dense_116 (Dense)           (None, 70)                4970      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 15,820\n",
      "Trainable params: 15,820\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "ZnNqGxE4pUwH",
    "outputId": "f3dfcab1-4b85-455f-e191-8fc0d5bada5c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "385/385 - 1s - loss: 4.0174 - accuracy: 0.0542 - 925ms/epoch - 2ms/step\n",
      "Epoch 2/10\n",
      "385/385 - 0s - loss: 3.5759 - accuracy: 0.1117 - 383ms/epoch - 996us/step\n",
      "Epoch 3/10\n",
      "385/385 - 0s - loss: 3.4134 - accuracy: 0.1336 - 383ms/epoch - 994us/step\n",
      "Epoch 4/10\n",
      "385/385 - 0s - loss: 3.2924 - accuracy: 0.1622 - 343ms/epoch - 890us/step\n",
      "Epoch 5/10\n",
      "385/385 - 0s - loss: 3.2168 - accuracy: 0.1726 - 351ms/epoch - 911us/step\n",
      "Epoch 6/10\n",
      "385/385 - 0s - loss: 3.1615 - accuracy: 0.1861 - 359ms/epoch - 933us/step\n",
      "Epoch 7/10\n",
      "385/385 - 0s - loss: 3.1179 - accuracy: 0.1942 - 357ms/epoch - 928us/step\n",
      "Epoch 8/10\n",
      "385/385 - 0s - loss: 3.0880 - accuracy: 0.2006 - 352ms/epoch - 913us/step\n",
      "Epoch 9/10\n",
      "385/385 - 0s - loss: 3.0575 - accuracy: 0.2056 - 355ms/epoch - 923us/step\n",
      "Epoch 10/10\n",
      "385/385 - 0s - loss: 3.0367 - accuracy: 0.2095 - 361ms/epoch - 937us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x279aad78750>"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    X_train_scaled,\n",
    "    y_train_categorical,\n",
    "    epochs=10,\n",
    "    shuffle=True,\n",
    "    verbose=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "BOdr8eSipYTj",
    "outputId": "d00cdf92-47e3-40e4-9de3-448f51741815"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "129/129 - 0s - loss: 3.0972 - accuracy: 0.2046 - 225ms/epoch - 2ms/step\n",
      "Normal Neural Network - Loss: 3.09723162651062, Accuracy: 0.20457866787910461\n"
     ]
    }
   ],
   "source": [
    "model_loss, model_accuracy = model.evaluate(\n",
    "    X_test_scaled, y_test_categorical, verbose=2)\n",
    "print(\n",
    "    f\"Normal Neural Network - Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "colab_type": "code",
    "id": "tzI_azQX7lzR",
    "outputId": "8b3e7f00-c001-44e4-a9cf-3a85bb226fab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 56ms/step\n",
      "Predicted classes: ['grindcore' 'classical' 'forro' 'country' 'detroit-techno']\n",
      "Actual Labels: ['heavy-metal', 'classical', 'industrial', 'dub', 'detroit-techno']\n"
     ]
    }
   ],
   "source": [
    "predicted_probabilities = model.predict(X_test_scaled[:5])\n",
    "predicted_labels = label_encoder.inverse_transform(predicted_probabilities.argmax(axis=-1))\n",
    "\n",
    "print(f\"Predicted classes: {predicted_labels}\")\n",
    "print(f\"Actual Labels: {list(y_test[:5])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "IuWQoQJc7ltb",
    "outputId": "e4cd945c-c756-440e-f24b-c2aac905a779"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2YqzhcXo7lqr"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CqRkshkNvhE4"
   },
   "source": [
    "Trying it with lesser features now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "id": "LtQsdWk5vsm3",
    "outputId": "c7b54a87-8cb8-4e45-ea04-ef3323da3e6e"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_name</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
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       "      <th>duration_ms</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>acoustic</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.416</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.794</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0246</td>\n",
       "      <td>0.8820</td>\n",
       "      <td>0.000641</td>\n",
       "      <td>0.1120</td>\n",
       "      <td>0.415</td>\n",
       "      <td>96.003</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.308</td>\n",
       "      <td>4</td>\n",
       "      <td>-13.052</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0402</td>\n",
       "      <td>0.8950</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>0.387</td>\n",
       "      <td>73.328</td>\n",
       "      <td>191773</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>16416</th>\n",
       "      <td>mandopop</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.297</td>\n",
       "      <td>9</td>\n",
       "      <td>-8.785</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0309</td>\n",
       "      <td>0.6920</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.1070</td>\n",
       "      <td>0.125</td>\n",
       "      <td>180.095</td>\n",
       "      <td>357094</td>\n",
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       "    <tr>\n",
       "      <th>16417</th>\n",
       "      <td>mandopop</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.529</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.928</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0406</td>\n",
       "      <td>0.5590</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.1630</td>\n",
       "      <td>0.267</td>\n",
       "      <td>133.967</td>\n",
       "      <td>242293</td>\n",
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       "    <tr>\n",
       "      <th>16418</th>\n",
       "      <td>mandopop</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.304</td>\n",
       "      <td>10</td>\n",
       "      <td>-9.115</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0335</td>\n",
       "      <td>0.8340</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1170</td>\n",
       "      <td>0.413</td>\n",
       "      <td>124.781</td>\n",
       "      <td>223570</td>\n",
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       "    <tr>\n",
       "      <th>16419</th>\n",
       "      <td>mandopop</td>\n",
       "      <td>0.605</td>\n",
       "      <td>0.310</td>\n",
       "      <td>7</td>\n",
       "      <td>-10.276</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0390</td>\n",
       "      <td>0.8490</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1360</td>\n",
       "      <td>0.292</td>\n",
       "      <td>118.028</td>\n",
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       "      <td>0.405</td>\n",
       "      <td>9</td>\n",
       "      <td>-7.328</td>\n",
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       "      <td>0.0265</td>\n",
       "      <td>0.5790</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.3090</td>\n",
       "      <td>0.167</td>\n",
       "      <td>111.760</td>\n",
       "      <td>280524</td>\n",
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       "</table>\n",
       "<p>16421 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      genre_name  danceability  energy  key  loudness  mode  speechiness   \n",
       "0       acoustic         0.703   0.444   11    -9.331     1       0.0417  \\\n",
       "1       acoustic         0.618   0.443    2    -9.681     1       0.0526   \n",
       "2       acoustic         0.591   0.416    1    -9.794     1       0.0246   \n",
       "3       acoustic         0.583   0.786    2    -3.142     0       0.0301   \n",
       "4       acoustic         0.364   0.308    4   -13.052     1       0.0402   \n",
       "...          ...           ...     ...  ...       ...   ...          ...   \n",
       "16416   mandopop         0.460   0.297    9    -8.785     1       0.0309   \n",
       "16417   mandopop         0.720   0.529   10    -8.928     1       0.0406   \n",
       "16418   mandopop         0.657   0.304   10    -9.115     1       0.0335   \n",
       "16419   mandopop         0.605   0.310    7   -10.276     1       0.0390   \n",
       "16420   mandopop         0.588   0.405    9    -7.328     0       0.0265   \n",
       "\n",
       "       acousticness  instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "0            0.5590          0.000000    0.0973    0.712  150.960       242947  \n",
       "1            0.4690          0.000000    0.0829    0.167  119.949       198853  \n",
       "2            0.8820          0.000641    0.1120    0.415   96.003       210080  \n",
       "3            0.0208          0.000000    0.1880    0.573  123.055       258827  \n",
       "4            0.8950          0.002300    0.1230    0.387   73.328       191773  \n",
       "...             ...               ...       ...      ...      ...          ...  \n",
       "16416        0.6920          0.000318    0.1070    0.125  180.095       357094  \n",
       "16417        0.5590          0.000021    0.1630    0.267  133.967       242293  \n",
       "16418        0.8340          0.000000    0.1170    0.413  124.781       223570  \n",
       "16419        0.8490          0.000000    0.1360    0.292  118.028       245521  \n",
       "16420        0.5790          0.000000    0.3090    0.167  111.760       280524  \n",
       "\n",
       "[16421 rows x 13 columns]"
      ]
     },
     "execution_count": 393,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ML_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "Nx5mgOjUvnl9",
    "outputId": "0dd31bb5-181c-4891-e294-446d0c3b2038"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
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       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>acoustic</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>acoustic</td>\n",
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       "      <td>0.443</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>acoustic</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.416</td>\n",
       "      <td>1</td>\n",
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       "      <td>96.003</td>\n",
       "      <td>210080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>acoustic</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.786</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.142</td>\n",
       "      <td>0.0301</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>acoustic</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.308</td>\n",
       "      <td>4</td>\n",
       "      <td>-13.052</td>\n",
       "      <td>0.0402</td>\n",
       "      <td>0.8950</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>0.387</td>\n",
       "      <td>73.328</td>\n",
       "      <td>191773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  genre_name  danceability  energy  key  loudness  speechiness  acousticness   \n",
       "0   acoustic         0.703   0.444   11    -9.331       0.0417        0.5590  \\\n",
       "1   acoustic         0.618   0.443    2    -9.681       0.0526        0.4690   \n",
       "2   acoustic         0.591   0.416    1    -9.794       0.0246        0.8820   \n",
       "3   acoustic         0.583   0.786    2    -3.142       0.0301        0.0208   \n",
       "4   acoustic         0.364   0.308    4   -13.052       0.0402        0.8950   \n",
       "\n",
       "   instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "0          0.000000    0.0973    0.712  150.960       242947  \n",
       "1          0.000000    0.0829    0.167  119.949       198853  \n",
       "2          0.000641    0.1120    0.415   96.003       210080  \n",
       "3          0.000000    0.1880    0.573  123.055       258827  \n",
       "4          0.002300    0.1230    0.387   73.328       191773  "
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_ML_df = ML_df.drop(['mode'], axis=1)\n",
    "new_ML_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "0j_44yN4vlt3",
    "outputId": "d509ec04-d092-4313-8a28-e880368da012"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16421, 11) (16421,)\n"
     ]
    }
   ],
   "source": [
    "# Assign X (data) and y (target)\n",
    "X = new_ML_df.drop(\"genre_name\", axis=1)\n",
    "y = new_ML_df[\"genre_name\"]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QwzficeSwFT_"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EUlB4hJ4wM5H"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mnVfOplfwNdm"
   },
   "outputs": [],
   "source": [
    "X_scaler = MinMaxScaler().fit(X_train)\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DTEh3VUSwNqw"
   },
   "outputs": [],
   "source": [
    "# Step 1: Label-encode data set\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoder.fit(y_train)\n",
    "encoded_y_train = label_encoder.transform(y_train)\n",
    "encoded_y_test = label_encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "afbt3aWUwNSp"
   },
   "outputs": [],
   "source": [
    "# Step 2: Convert encoded labels to one-hot-encoding\n",
    "y_train_categorical = to_categorical(encoded_y_train)\n",
    "y_test_categorical = to_categorical(encoded_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0ps9n71uwMvI"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DqtG55X1wiIb"
   },
   "outputs": [],
   "source": [
    "# Create model and add layers\n",
    "model = Sequential()\n",
    "model.add(Dense(units=70, activation='relu', input_dim=11))\n",
    "# model.add(Dense(units=70, activation='sigmoid'))\n",
    "# model.add(Dense(units=70, activation='sigmoid'))\n",
    "# model.add(Dense(units=70, activation='sigmoid'))\n",
    "model.add(Dense(units=70, activation='relu'))\n",
    "model.add(Dense(units=70, activation='relu'))\n",
    "model.add(Dense(units=70, activation='relu'))\n",
    "# model.add(Dense(units=70, activation='softmax'))\n",
    "model.add(Dense(units=70, activation='softplus'))\n",
    "# model.add(Dense(units=70, activation='softsign'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ucmlrhH3wiZl"
   },
   "outputs": [],
   "source": [
    "# Compile and fit the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "colab_type": "code",
    "id": "jFIxfxO8wh7K",
    "outputId": "de2883f4-e4bc-4fc1-ecaf-bcf6aad76e9a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_28\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_117 (Dense)           (None, 70)                840       \n",
      "                                                                 \n",
      " dense_118 (Dense)           (None, 70)                4970      \n",
      "                                                                 \n",
      " dense_119 (Dense)           (None, 70)                4970      \n",
      "                                                                 \n",
      " dense_120 (Dense)           (None, 70)                4970      \n",
      "                                                                 \n",
      " dense_121 (Dense)           (None, 70)                4970      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 20,720\n",
      "Trainable params: 20,720\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "fzxKcfWvwno4",
    "outputId": "dc65919a-a60a-4290-fc69-5134636adad6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "385/385 - 1s - loss: 3.8197 - accuracy: 0.0656 - 1s/epoch - 3ms/step\n",
      "Epoch 2/100\n",
      "385/385 - 0s - loss: 3.4117 - accuracy: 0.1271 - 442ms/epoch - 1ms/step\n",
      "Epoch 3/100\n",
      "385/385 - 0s - loss: 3.2297 - accuracy: 0.1633 - 397ms/epoch - 1ms/step\n",
      "Epoch 4/100\n",
      "385/385 - 0s - loss: 3.1483 - accuracy: 0.1851 - 403ms/epoch - 1ms/step\n",
      "Epoch 5/100\n",
      "385/385 - 0s - loss: 3.1055 - accuracy: 0.1918 - 401ms/epoch - 1ms/step\n",
      "Epoch 6/100\n",
      "385/385 - 0s - loss: 3.0751 - accuracy: 0.1989 - 416ms/epoch - 1ms/step\n",
      "Epoch 7/100\n",
      "385/385 - 0s - loss: 3.0388 - accuracy: 0.2028 - 424ms/epoch - 1ms/step\n",
      "Epoch 8/100\n",
      "385/385 - 0s - loss: 3.0067 - accuracy: 0.2074 - 408ms/epoch - 1ms/step\n",
      "Epoch 9/100\n",
      "385/385 - 0s - loss: 2.9836 - accuracy: 0.2143 - 407ms/epoch - 1ms/step\n",
      "Epoch 10/100\n",
      "385/385 - 0s - loss: 2.9559 - accuracy: 0.2176 - 441ms/epoch - 1ms/step\n",
      "Epoch 11/100\n",
      "385/385 - 0s - loss: 2.9347 - accuracy: 0.2215 - 441ms/epoch - 1ms/step\n",
      "Epoch 12/100\n",
      "385/385 - 0s - loss: 2.9207 - accuracy: 0.2277 - 462ms/epoch - 1ms/step\n",
      "Epoch 13/100\n",
      "385/385 - 0s - loss: 2.9014 - accuracy: 0.2287 - 459ms/epoch - 1ms/step\n",
      "Epoch 14/100\n",
      "385/385 - 0s - loss: 2.8801 - accuracy: 0.2333 - 481ms/epoch - 1ms/step\n",
      "Epoch 15/100\n",
      "385/385 - 0s - loss: 2.8663 - accuracy: 0.2374 - 459ms/epoch - 1ms/step\n",
      "Epoch 16/100\n",
      "385/385 - 0s - loss: 2.8444 - accuracy: 0.2446 - 436ms/epoch - 1ms/step\n",
      "Epoch 17/100\n",
      "385/385 - 0s - loss: 2.8345 - accuracy: 0.2451 - 474ms/epoch - 1ms/step\n",
      "Epoch 18/100\n",
      "385/385 - 0s - loss: 2.8241 - accuracy: 0.2455 - 474ms/epoch - 1ms/step\n",
      "Epoch 19/100\n",
      "385/385 - 1s - loss: 2.8047 - accuracy: 0.2504 - 504ms/epoch - 1ms/step\n",
      "Epoch 20/100\n",
      "385/385 - 0s - loss: 2.7969 - accuracy: 0.2516 - 428ms/epoch - 1ms/step\n",
      "Epoch 21/100\n",
      "385/385 - 0s - loss: 2.7807 - accuracy: 0.2559 - 404ms/epoch - 1ms/step\n",
      "Epoch 22/100\n",
      "385/385 - 0s - loss: 2.7649 - accuracy: 0.2555 - 401ms/epoch - 1ms/step\n",
      "Epoch 23/100\n",
      "385/385 - 0s - loss: 2.7630 - accuracy: 0.2654 - 401ms/epoch - 1ms/step\n",
      "Epoch 24/100\n",
      "385/385 - 0s - loss: 2.7561 - accuracy: 0.2597 - 416ms/epoch - 1ms/step\n",
      "Epoch 25/100\n",
      "385/385 - 0s - loss: 2.7363 - accuracy: 0.2632 - 401ms/epoch - 1ms/step\n",
      "Epoch 26/100\n",
      "385/385 - 0s - loss: 2.7263 - accuracy: 0.2682 - 463ms/epoch - 1ms/step\n",
      "Epoch 27/100\n",
      "385/385 - 0s - loss: 2.7170 - accuracy: 0.2675 - 415ms/epoch - 1ms/step\n",
      "Epoch 28/100\n",
      "385/385 - 0s - loss: 2.7091 - accuracy: 0.2710 - 413ms/epoch - 1ms/step\n",
      "Epoch 29/100\n",
      "385/385 - 0s - loss: 2.6972 - accuracy: 0.2747 - 395ms/epoch - 1ms/step\n",
      "Epoch 30/100\n",
      "385/385 - 0s - loss: 2.6824 - accuracy: 0.2748 - 414ms/epoch - 1ms/step\n",
      "Epoch 31/100\n",
      "385/385 - 0s - loss: 2.6792 - accuracy: 0.2733 - 401ms/epoch - 1ms/step\n",
      "Epoch 32/100\n",
      "385/385 - 0s - loss: 2.6637 - accuracy: 0.2818 - 464ms/epoch - 1ms/step\n",
      "Epoch 33/100\n",
      "385/385 - 0s - loss: 2.6598 - accuracy: 0.2804 - 471ms/epoch - 1ms/step\n",
      "Epoch 34/100\n",
      "385/385 - 0s - loss: 2.6477 - accuracy: 0.2848 - 405ms/epoch - 1ms/step\n",
      "Epoch 35/100\n",
      "385/385 - 0s - loss: 2.6378 - accuracy: 0.2860 - 428ms/epoch - 1ms/step\n",
      "Epoch 36/100\n",
      "385/385 - 0s - loss: 2.6290 - accuracy: 0.2905 - 432ms/epoch - 1ms/step\n",
      "Epoch 37/100\n",
      "385/385 - 0s - loss: 2.6225 - accuracy: 0.2919 - 439ms/epoch - 1ms/step\n",
      "Epoch 38/100\n",
      "385/385 - 0s - loss: 2.6149 - accuracy: 0.2896 - 443ms/epoch - 1ms/step\n",
      "Epoch 39/100\n",
      "385/385 - 0s - loss: 2.6019 - accuracy: 0.2947 - 426ms/epoch - 1ms/step\n",
      "Epoch 40/100\n",
      "385/385 - 0s - loss: 2.5937 - accuracy: 0.2979 - 440ms/epoch - 1ms/step\n",
      "Epoch 41/100\n",
      "385/385 - 0s - loss: 2.5878 - accuracy: 0.2972 - 401ms/epoch - 1ms/step\n",
      "Epoch 42/100\n",
      "385/385 - 0s - loss: 2.5734 - accuracy: 0.2993 - 402ms/epoch - 1ms/step\n",
      "Epoch 43/100\n",
      "385/385 - 0s - loss: 2.5689 - accuracy: 0.3039 - 414ms/epoch - 1ms/step\n",
      "Epoch 44/100\n",
      "385/385 - 0s - loss: 2.5573 - accuracy: 0.3016 - 419ms/epoch - 1ms/step\n",
      "Epoch 45/100\n",
      "385/385 - 0s - loss: 2.5520 - accuracy: 0.3062 - 417ms/epoch - 1ms/step\n",
      "Epoch 46/100\n",
      "385/385 - 0s - loss: 2.5396 - accuracy: 0.3093 - 405ms/epoch - 1ms/step\n",
      "Epoch 47/100\n",
      "385/385 - 0s - loss: 2.5359 - accuracy: 0.3087 - 412ms/epoch - 1ms/step\n",
      "Epoch 48/100\n",
      "385/385 - 0s - loss: 2.5244 - accuracy: 0.3091 - 409ms/epoch - 1ms/step\n",
      "Epoch 49/100\n",
      "385/385 - 0s - loss: 2.5255 - accuracy: 0.3108 - 471ms/epoch - 1ms/step\n",
      "Epoch 50/100\n",
      "385/385 - 0s - loss: 2.5114 - accuracy: 0.3158 - 457ms/epoch - 1ms/step\n",
      "Epoch 51/100\n",
      "385/385 - 0s - loss: 2.5071 - accuracy: 0.3136 - 469ms/epoch - 1ms/step\n",
      "Epoch 52/100\n",
      "385/385 - 0s - loss: 2.4976 - accuracy: 0.3130 - 459ms/epoch - 1ms/step\n",
      "Epoch 53/100\n",
      "385/385 - 0s - loss: 2.4879 - accuracy: 0.3193 - 435ms/epoch - 1ms/step\n",
      "Epoch 54/100\n",
      "385/385 - 0s - loss: 2.4811 - accuracy: 0.3218 - 475ms/epoch - 1ms/step\n",
      "Epoch 55/100\n",
      "385/385 - 0s - loss: 2.4754 - accuracy: 0.3223 - 456ms/epoch - 1ms/step\n",
      "Epoch 56/100\n",
      "385/385 - 0s - loss: 2.4679 - accuracy: 0.3208 - 404ms/epoch - 1ms/step\n",
      "Epoch 57/100\n",
      "385/385 - 0s - loss: 2.4600 - accuracy: 0.3259 - 409ms/epoch - 1ms/step\n",
      "Epoch 58/100\n",
      "385/385 - 0s - loss: 2.4546 - accuracy: 0.3283 - 395ms/epoch - 1ms/step\n",
      "Epoch 59/100\n",
      "385/385 - 0s - loss: 2.4456 - accuracy: 0.3283 - 412ms/epoch - 1ms/step\n",
      "Epoch 60/100\n",
      "385/385 - 0s - loss: 2.4386 - accuracy: 0.3276 - 389ms/epoch - 1ms/step\n",
      "Epoch 61/100\n",
      "385/385 - 0s - loss: 2.4302 - accuracy: 0.3345 - 411ms/epoch - 1ms/step\n",
      "Epoch 62/100\n",
      "385/385 - 0s - loss: 2.4295 - accuracy: 0.3326 - 404ms/epoch - 1ms/step\n",
      "Epoch 63/100\n",
      "385/385 - 0s - loss: 2.4250 - accuracy: 0.3355 - 405ms/epoch - 1ms/step\n",
      "Epoch 64/100\n",
      "385/385 - 0s - loss: 2.4143 - accuracy: 0.3370 - 391ms/epoch - 1ms/step\n",
      "Epoch 65/100\n",
      "385/385 - 0s - loss: 2.4115 - accuracy: 0.3417 - 384ms/epoch - 998us/step\n",
      "Epoch 66/100\n",
      "385/385 - 0s - loss: 2.4021 - accuracy: 0.3391 - 385ms/epoch - 1000us/step\n",
      "Epoch 67/100\n",
      "385/385 - 0s - loss: 2.3961 - accuracy: 0.3386 - 400ms/epoch - 1ms/step\n",
      "Epoch 68/100\n",
      "385/385 - 0s - loss: 2.3890 - accuracy: 0.3415 - 408ms/epoch - 1ms/step\n",
      "Epoch 69/100\n",
      "385/385 - 0s - loss: 2.3818 - accuracy: 0.3428 - 400ms/epoch - 1ms/step\n",
      "Epoch 70/100\n",
      "385/385 - 0s - loss: 2.3691 - accuracy: 0.3411 - 392ms/epoch - 1ms/step\n",
      "Epoch 71/100\n",
      "385/385 - 0s - loss: 2.3661 - accuracy: 0.3473 - 402ms/epoch - 1ms/step\n",
      "Epoch 72/100\n",
      "385/385 - 0s - loss: 2.3667 - accuracy: 0.3467 - 400ms/epoch - 1ms/step\n",
      "Epoch 73/100\n",
      "385/385 - 0s - loss: 2.3615 - accuracy: 0.3456 - 401ms/epoch - 1ms/step\n",
      "Epoch 74/100\n",
      "385/385 - 0s - loss: 2.3460 - accuracy: 0.3493 - 400ms/epoch - 1ms/step\n",
      "Epoch 75/100\n",
      "385/385 - 0s - loss: 2.3420 - accuracy: 0.3493 - 401ms/epoch - 1ms/step\n",
      "Epoch 76/100\n",
      "385/385 - 0s - loss: 2.3452 - accuracy: 0.3454 - 400ms/epoch - 1ms/step\n",
      "Epoch 77/100\n",
      "385/385 - 0s - loss: 2.3312 - accuracy: 0.3520 - 392ms/epoch - 1ms/step\n",
      "Epoch 78/100\n",
      "385/385 - 0s - loss: 2.3242 - accuracy: 0.3528 - 392ms/epoch - 1ms/step\n",
      "Epoch 79/100\n",
      "385/385 - 0s - loss: 2.3257 - accuracy: 0.3550 - 400ms/epoch - 1ms/step\n",
      "Epoch 80/100\n",
      "385/385 - 0s - loss: 2.3138 - accuracy: 0.3570 - 392ms/epoch - 1ms/step\n",
      "Epoch 81/100\n",
      "385/385 - 0s - loss: 2.3082 - accuracy: 0.3540 - 416ms/epoch - 1ms/step\n",
      "Epoch 82/100\n",
      "385/385 - 0s - loss: 2.3038 - accuracy: 0.3606 - 400ms/epoch - 1ms/step\n",
      "Epoch 83/100\n",
      "385/385 - 0s - loss: 2.3026 - accuracy: 0.3629 - 392ms/epoch - 1ms/step\n",
      "Epoch 84/100\n",
      "385/385 - 0s - loss: 2.2966 - accuracy: 0.3600 - 400ms/epoch - 1ms/step\n",
      "Epoch 85/100\n",
      "385/385 - 0s - loss: 2.2914 - accuracy: 0.3618 - 399ms/epoch - 1ms/step\n",
      "Epoch 86/100\n",
      "385/385 - 0s - loss: 2.2819 - accuracy: 0.3639 - 400ms/epoch - 1ms/step\n",
      "Epoch 87/100\n",
      "385/385 - 0s - loss: 2.2738 - accuracy: 0.3678 - 416ms/epoch - 1ms/step\n",
      "Epoch 88/100\n",
      "385/385 - 0s - loss: 2.2705 - accuracy: 0.3689 - 480ms/epoch - 1ms/step\n",
      "Epoch 89/100\n",
      "385/385 - 0s - loss: 2.2730 - accuracy: 0.3645 - 451ms/epoch - 1ms/step\n",
      "Epoch 90/100\n",
      "385/385 - 0s - loss: 2.2652 - accuracy: 0.3723 - 466ms/epoch - 1ms/step\n",
      "Epoch 91/100\n",
      "385/385 - 0s - loss: 2.2597 - accuracy: 0.3633 - 467ms/epoch - 1ms/step\n",
      "Epoch 92/100\n",
      "385/385 - 0s - loss: 2.2484 - accuracy: 0.3735 - 474ms/epoch - 1ms/step\n",
      "Epoch 93/100\n",
      "385/385 - 0s - loss: 2.2413 - accuracy: 0.3716 - 459ms/epoch - 1ms/step\n",
      "Epoch 94/100\n",
      "385/385 - 0s - loss: 2.2420 - accuracy: 0.3756 - 435ms/epoch - 1ms/step\n",
      "Epoch 95/100\n",
      "385/385 - 0s - loss: 2.2456 - accuracy: 0.3739 - 418ms/epoch - 1ms/step\n",
      "Epoch 96/100\n",
      "385/385 - 0s - loss: 2.2273 - accuracy: 0.3743 - 385ms/epoch - 1ms/step\n",
      "Epoch 97/100\n",
      "385/385 - 0s - loss: 2.2335 - accuracy: 0.3765 - 399ms/epoch - 1ms/step\n",
      "Epoch 98/100\n",
      "385/385 - 0s - loss: 2.2210 - accuracy: 0.3785 - 401ms/epoch - 1ms/step\n",
      "Epoch 99/100\n",
      "385/385 - 0s - loss: 2.2211 - accuracy: 0.3745 - 400ms/epoch - 1ms/step\n",
      "Epoch 100/100\n",
      "385/385 - 0s - loss: 2.2113 - accuracy: 0.3793 - 391ms/epoch - 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x279abf712d0>"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    X_train_scaled,\n",
    "    y_train_categorical,\n",
    "    epochs=100,\n",
    "    shuffle=True,\n",
    "    verbose=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "tNwCatGkwvZ6",
    "outputId": "ad7635cd-d377-41df-e9b3-76c364b6e560"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "129/129 - 0s - loss: 2.9835 - accuracy: 0.2594 - 197ms/epoch - 2ms/step\n",
      "Normal Neural Network - Loss: 2.983454942703247, Accuracy: 0.25937652587890625\n"
     ]
    }
   ],
   "source": [
    "model_loss, model_accuracy = model.evaluate(\n",
    "    X_test_scaled, y_test_categorical, verbose=2)\n",
    "print(\n",
    "    f\"Normal Neural Network - Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MpwBPknpbcHO"
   },
   "source": [
    " Changing the Genre Dataframe - Considering the Top 50 Genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 445
    },
    "colab_type": "code",
    "id": "QHNgZ73bbihU",
    "outputId": "a17bf9bb-8cae-41c0-d19b-0217704bd0c7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>artist_name</th>\n",
       "      <th>track_name</th>\n",
       "      <th>track_id</th>\n",
       "      <th>artist_href</th>\n",
       "      <th>track_href</th>\n",
       "      <th>genre_name</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>drake</td>\n",
       "      <td>search &amp; rescue</td>\n",
       "      <td>7aRCf5cLOFN1U7kvtChY1G</td>\n",
       "      <td>https://open.spotify.com/artist/3TVXtAsR1Inumw...</td>\n",
       "      <td>https://open.spotify.com/track/7aRCf5cLOFN1U7k...</td>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.482</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tyler, the creator</td>\n",
       "      <td>see you again (feat. kali uchis)</td>\n",
       "      <td>7KA4W4McWYRpgf0fWsJZWB</td>\n",
       "      <td>https://open.spotify.com/artist/4V8LLVI7PbaPR0...</td>\n",
       "      <td>https://open.spotify.com/track/7KA4W4McWYRpgf0...</td>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.559</td>\n",
       "      <td>6</td>\n",
       "      <td>-9.222</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0959</td>\n",
       "      <td>0.3710</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>0.620</td>\n",
       "      <td>78.558</td>\n",
       "      <td>180387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>metro boomin</td>\n",
       "      <td>creepin' (with the weeknd &amp; 21 savage)</td>\n",
       "      <td>2dHHgzDwk4BJdRwy9uXhTO</td>\n",
       "      <td>https://open.spotify.com/artist/0iEtIxbK0KxaSl...</td>\n",
       "      <td>https://open.spotify.com/track/2dHHgzDwk4BJdRw...</td>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.620</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.005</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0484</td>\n",
       "      <td>0.4170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0822</td>\n",
       "      <td>0.172</td>\n",
       "      <td>97.950</td>\n",
       "      <td>221520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>j. cole</td>\n",
       "      <td>no role modelz</td>\n",
       "      <td>68Dni7IE4VyPkTOH9mRWHr</td>\n",
       "      <td>https://open.spotify.com/artist/6l3HvQ5sa6mXTs...</td>\n",
       "      <td>https://open.spotify.com/track/68Dni7IE4VyPkTO...</td>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.521</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.492</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3390</td>\n",
       "      <td>0.3240</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0534</td>\n",
       "      <td>0.494</td>\n",
       "      <td>100.028</td>\n",
       "      <td>292799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>drake</td>\n",
       "      <td>rich flex</td>\n",
       "      <td>1bDbXMyjaUIooNwFE9wn0N</td>\n",
       "      <td>https://open.spotify.com/artist/3TVXtAsR1Inumw...</td>\n",
       "      <td>https://open.spotify.com/track/1bDbXMyjaUIooNw...</td>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.520</td>\n",
       "      <td>11</td>\n",
       "      <td>-9.342</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2440</td>\n",
       "      <td>0.0503</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.3550</td>\n",
       "      <td>0.424</td>\n",
       "      <td>153.150</td>\n",
       "      <td>239360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          artist_name                              track_name   \n",
       "0               drake                         search & rescue  \\\n",
       "1  tyler, the creator        see you again (feat. kali uchis)   \n",
       "2        metro boomin  creepin' (with the weeknd & 21 savage)   \n",
       "3             j. cole                          no role modelz   \n",
       "4               drake                               rich flex   \n",
       "\n",
       "                 track_id                                        artist_href   \n",
       "0  7aRCf5cLOFN1U7kvtChY1G  https://open.spotify.com/artist/3TVXtAsR1Inumw...  \\\n",
       "1  7KA4W4McWYRpgf0fWsJZWB  https://open.spotify.com/artist/4V8LLVI7PbaPR0...   \n",
       "2  2dHHgzDwk4BJdRwy9uXhTO  https://open.spotify.com/artist/0iEtIxbK0KxaSl...   \n",
       "3  68Dni7IE4VyPkTOH9mRWHr  https://open.spotify.com/artist/6l3HvQ5sa6mXTs...   \n",
       "4  1bDbXMyjaUIooNwFE9wn0N  https://open.spotify.com/artist/3TVXtAsR1Inumw...   \n",
       "\n",
       "                                          track_href genre_name  danceability   \n",
       "0  https://open.spotify.com/track/7aRCf5cLOFN1U7k...    Hip-Hop         0.817  \\\n",
       "1  https://open.spotify.com/track/7KA4W4McWYRpgf0...    Hip-Hop         0.558   \n",
       "2  https://open.spotify.com/track/2dHHgzDwk4BJdRw...    Hip-Hop         0.715   \n",
       "3  https://open.spotify.com/track/68Dni7IE4VyPkTO...    Hip-Hop         0.690   \n",
       "4  https://open.spotify.com/track/1bDbXMyjaUIooNw...    Hip-Hop         0.561   \n",
       "\n",
       "   energy  key  loudness  mode  speechiness  acousticness  instrumentalness   \n",
       "0   0.440   10    -8.482     0       0.0734        0.0603          0.000001  \\\n",
       "1   0.559    6    -9.222     1       0.0959        0.3710          0.000007   \n",
       "2   0.620    1    -6.005     0       0.0484        0.4170          0.000000   \n",
       "3   0.521   10    -8.492     0       0.3390        0.3240          0.000000   \n",
       "4   0.520   11    -9.342     0       0.2440        0.0503          0.000002   \n",
       "\n",
       "   liveness  valence    tempo  duration_ms  \n",
       "0    0.3300    0.544  142.024       272113  \n",
       "1    0.1090    0.620   78.558       180387  \n",
       "2    0.0822    0.172   97.950       221520  \n",
       "3    0.0534    0.494  100.028       292799  \n",
       "4    0.3550    0.424  153.150       239360  "
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join(\"Downloads/spotify_df_Top50Genre.csv\"))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "colab_type": "code",
    "id": "O8MIAlAcb8xy",
    "outputId": "929788aa-a1a1-4d88-cdbf-c32694b17465"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2434 entries, 0 to 2433\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   genre_name        2434 non-null   object \n",
      " 1   danceability      2434 non-null   float64\n",
      " 2   energy            2434 non-null   float64\n",
      " 3   key               2434 non-null   int64  \n",
      " 4   loudness          2434 non-null   float64\n",
      " 5   mode              2434 non-null   int64  \n",
      " 6   speechiness       2434 non-null   float64\n",
      " 7   acousticness      2434 non-null   float64\n",
      " 8   instrumentalness  2434 non-null   float64\n",
      " 9   liveness          2434 non-null   float64\n",
      " 10  valence           2434 non-null   float64\n",
      " 11  tempo             2434 non-null   float64\n",
      " 12  duration_ms       2434 non-null   int64  \n",
      "dtypes: float64(9), int64(3), object(1)\n",
      "memory usage: 247.3+ KB\n"
     ]
    }
   ],
   "source": [
    "ML_df = df.drop(['artist_name', 'track_name', 'track_id', 'artist_href', 'track_href'], axis=1)\n",
    "ML_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "cwAcnxJicFSM",
    "outputId": "c4399b7e-5629-4519-e013-ac5e9a8e6da7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2434, 12) (2434,)\n"
     ]
    }
   ],
   "source": [
    "X = ML_df.drop(\"genre_name\", axis=1)\n",
    "y = ML_df[\"genre_name\"]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ou7U1j2RcLTK"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6UCbFEWHcLcO"
   },
   "outputs": [],
   "source": [
    "X_scaler = MinMaxScaler().fit(X_train)\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XakC0tFVcLh2"
   },
   "outputs": [],
   "source": [
    "# Step 1: Label-encode data set\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoder.fit(y_train)\n",
    "encoded_y_train = label_encoder.transform(y_train)\n",
    "encoded_y_test = label_encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kS6xEXL4cLpj"
   },
   "outputs": [],
   "source": [
    "# Step 2: Convert encoded labels to one-hot-encoding\n",
    "y_train_categorical = to_categorical(encoded_y_train)\n",
    "y_test_categorical = to_categorical(encoded_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XgiFB6gvcLsi"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RP2dxBhXcLwm"
   },
   "outputs": [],
   "source": [
    "# Create model and add layers\n",
    "model = Sequential()\n",
    "model.add(Dense(units=10, activation='relu', input_dim=12))\n",
    "# model.add(Dense(units=10, activation='sigmoid'))\n",
    "# model.add(Dense(units=10, activation='sigmoid'))\n",
    "# model.add(Dense(units=10, activation='sigmoid'))\n",
    "model.add(Dense(units=10, activation='relu'))\n",
    "model.add(Dense(units=10, activation='relu'))\n",
    "model.add(Dense(units=10, activation='relu'))\n",
    "model.add(Dense(units=10, activation='relu'))\n",
    "# model.add(Dense(units=10, activation='relu'))\n",
    "model.add(Dense(units=10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "K94hVcQqcL19"
   },
   "outputs": [],
   "source": [
    "# Compile and fit the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "colab_type": "code",
    "id": "iUUJUoKUcL7h",
    "outputId": "fd32c6f4-ea61-42fd-8c27-dde5fc99583b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_29\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_122 (Dense)           (None, 10)                130       \n",
      "                                                                 \n",
      " dense_123 (Dense)           (None, 10)                110       \n",
      "                                                                 \n",
      " dense_124 (Dense)           (None, 10)                110       \n",
      "                                                                 \n",
      " dense_125 (Dense)           (None, 10)                110       \n",
      "                                                                 \n",
      " dense_126 (Dense)           (None, 10)                110       \n",
      "                                                                 \n",
      " dense_127 (Dense)           (None, 10)                110       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 680\n",
      "Trainable params: 680\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "hUYd8K0McL5s",
    "outputId": "1ceef148-7a43-491f-985d-945e01bde6db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "58/58 - 1s - loss: 2.3007 - accuracy: 0.1058 - 775ms/epoch - 13ms/step\n",
      "Epoch 2/200\n",
      "58/58 - 0s - loss: 2.2907 - accuracy: 0.1068 - 66ms/epoch - 1ms/step\n",
      "Epoch 3/200\n",
      "58/58 - 0s - loss: 2.2809 - accuracy: 0.1326 - 83ms/epoch - 1ms/step\n",
      "Epoch 4/200\n",
      "58/58 - 0s - loss: 2.2640 - accuracy: 0.1359 - 73ms/epoch - 1ms/step\n",
      "Epoch 5/200\n",
      "58/58 - 0s - loss: 2.2368 - accuracy: 0.1595 - 74ms/epoch - 1ms/step\n",
      "Epoch 6/200\n",
      "58/58 - 0s - loss: 2.1985 - accuracy: 0.1726 - 71ms/epoch - 1ms/step\n",
      "Epoch 7/200\n",
      "58/58 - 0s - loss: 2.1553 - accuracy: 0.1847 - 71ms/epoch - 1ms/step\n",
      "Epoch 8/200\n",
      "58/58 - 0s - loss: 2.1058 - accuracy: 0.2252 - 68ms/epoch - 1ms/step\n",
      "Epoch 9/200\n",
      "58/58 - 0s - loss: 2.0630 - accuracy: 0.2438 - 67ms/epoch - 1ms/step\n",
      "Epoch 10/200\n",
      "58/58 - 0s - loss: 2.0292 - accuracy: 0.2400 - 69ms/epoch - 1ms/step\n",
      "Epoch 11/200\n",
      "58/58 - 0s - loss: 1.9890 - accuracy: 0.2630 - 63ms/epoch - 1ms/step\n",
      "Epoch 12/200\n",
      "58/58 - 0s - loss: 1.9621 - accuracy: 0.2685 - 67ms/epoch - 1ms/step\n",
      "Epoch 13/200\n",
      "58/58 - 0s - loss: 1.9432 - accuracy: 0.2773 - 67ms/epoch - 1ms/step\n",
      "Epoch 14/200\n",
      "58/58 - 0s - loss: 1.9175 - accuracy: 0.2915 - 68ms/epoch - 1ms/step\n",
      "Epoch 15/200\n",
      "58/58 - 0s - loss: 1.9022 - accuracy: 0.3058 - 64ms/epoch - 1ms/step\n",
      "Epoch 16/200\n",
      "58/58 - 0s - loss: 1.8826 - accuracy: 0.3282 - 64ms/epoch - 1ms/step\n",
      "Epoch 17/200\n",
      "58/58 - 0s - loss: 1.8654 - accuracy: 0.3266 - 56ms/epoch - 972us/step\n",
      "Epoch 18/200\n",
      "58/58 - 0s - loss: 1.8517 - accuracy: 0.3490 - 58ms/epoch - 992us/step\n",
      "Epoch 19/200\n",
      "58/58 - 0s - loss: 1.8354 - accuracy: 0.3485 - 65ms/epoch - 1ms/step\n",
      "Epoch 20/200\n",
      "58/58 - 0s - loss: 1.8472 - accuracy: 0.3299 - 68ms/epoch - 1ms/step\n",
      "Epoch 21/200\n",
      "58/58 - 0s - loss: 1.8247 - accuracy: 0.3458 - 60ms/epoch - 1ms/step\n",
      "Epoch 22/200\n",
      "58/58 - 0s - loss: 1.8161 - accuracy: 0.3529 - 67ms/epoch - 1ms/step\n",
      "Epoch 23/200\n",
      "58/58 - 0s - loss: 1.8012 - accuracy: 0.3534 - 59ms/epoch - 1ms/step\n",
      "Epoch 24/200\n",
      "58/58 - 0s - loss: 1.7887 - accuracy: 0.3638 - 58ms/epoch - 1ms/step\n",
      "Epoch 25/200\n",
      "58/58 - 0s - loss: 1.7882 - accuracy: 0.3699 - 75ms/epoch - 1ms/step\n",
      "Epoch 26/200\n",
      "58/58 - 0s - loss: 1.7786 - accuracy: 0.3666 - 66ms/epoch - 1ms/step\n",
      "Epoch 27/200\n",
      "58/58 - 0s - loss: 1.7641 - accuracy: 0.3655 - 67ms/epoch - 1ms/step\n",
      "Epoch 28/200\n",
      "58/58 - 0s - loss: 1.7653 - accuracy: 0.3660 - 67ms/epoch - 1ms/step\n",
      "Epoch 29/200\n",
      "58/58 - 0s - loss: 1.7601 - accuracy: 0.3710 - 66ms/epoch - 1ms/step\n",
      "Epoch 30/200\n",
      "58/58 - 0s - loss: 1.7566 - accuracy: 0.3732 - 56ms/epoch - 967us/step\n",
      "Epoch 31/200\n",
      "58/58 - 0s - loss: 1.7511 - accuracy: 0.3616 - 61ms/epoch - 1ms/step\n",
      "Epoch 32/200\n",
      "58/58 - 0s - loss: 1.7424 - accuracy: 0.3737 - 54ms/epoch - 931us/step\n",
      "Epoch 33/200\n",
      "58/58 - 0s - loss: 1.7412 - accuracy: 0.3759 - 63ms/epoch - 1ms/step\n",
      "Epoch 34/200\n",
      "58/58 - 0s - loss: 1.7355 - accuracy: 0.3682 - 76ms/epoch - 1ms/step\n",
      "Epoch 35/200\n",
      "58/58 - 0s - loss: 1.7292 - accuracy: 0.3699 - 60ms/epoch - 1ms/step\n",
      "Epoch 36/200\n",
      "58/58 - 0s - loss: 1.7331 - accuracy: 0.3638 - 69ms/epoch - 1ms/step\n",
      "Epoch 37/200\n",
      "58/58 - 0s - loss: 1.7564 - accuracy: 0.3655 - 67ms/epoch - 1ms/step\n",
      "Epoch 38/200\n",
      "58/58 - 0s - loss: 1.7186 - accuracy: 0.3808 - 83ms/epoch - 1ms/step\n",
      "Epoch 39/200\n",
      "58/58 - 0s - loss: 1.7279 - accuracy: 0.3753 - 65ms/epoch - 1ms/step\n",
      "Epoch 40/200\n",
      "58/58 - 0s - loss: 1.7133 - accuracy: 0.3786 - 65ms/epoch - 1ms/step\n",
      "Epoch 41/200\n",
      "58/58 - 0s - loss: 1.7025 - accuracy: 0.3808 - 67ms/epoch - 1ms/step\n",
      "Epoch 42/200\n",
      "58/58 - 0s - loss: 1.6992 - accuracy: 0.3781 - 67ms/epoch - 1ms/step\n",
      "Epoch 43/200\n",
      "58/58 - 0s - loss: 1.6992 - accuracy: 0.3737 - 60ms/epoch - 1ms/step\n",
      "Epoch 44/200\n",
      "58/58 - 0s - loss: 1.6948 - accuracy: 0.3858 - 73ms/epoch - 1ms/step\n",
      "Epoch 45/200\n",
      "58/58 - 0s - loss: 1.6874 - accuracy: 0.3956 - 68ms/epoch - 1ms/step\n",
      "Epoch 46/200\n",
      "58/58 - 0s - loss: 1.6885 - accuracy: 0.3940 - 58ms/epoch - 1ms/step\n",
      "Epoch 47/200\n",
      "58/58 - 0s - loss: 1.6828 - accuracy: 0.3885 - 50ms/epoch - 861us/step\n",
      "Epoch 48/200\n",
      "58/58 - 0s - loss: 1.6916 - accuracy: 0.3825 - 65ms/epoch - 1ms/step\n",
      "Epoch 49/200\n",
      "58/58 - 0s - loss: 1.6966 - accuracy: 0.3814 - 68ms/epoch - 1ms/step\n",
      "Epoch 50/200\n",
      "58/58 - 0s - loss: 1.6758 - accuracy: 0.3956 - 59ms/epoch - 1ms/step\n",
      "Epoch 51/200\n",
      "58/58 - 0s - loss: 1.6715 - accuracy: 0.3901 - 67ms/epoch - 1ms/step\n",
      "Epoch 52/200\n",
      "58/58 - 0s - loss: 1.6737 - accuracy: 0.3918 - 59ms/epoch - 1ms/step\n",
      "Epoch 53/200\n",
      "58/58 - 0s - loss: 1.6761 - accuracy: 0.3923 - 60ms/epoch - 1ms/step\n",
      "Epoch 54/200\n",
      "58/58 - 0s - loss: 1.6741 - accuracy: 0.4000 - 65ms/epoch - 1ms/step\n",
      "Epoch 55/200\n",
      "58/58 - 0s - loss: 1.6625 - accuracy: 0.3901 - 50ms/epoch - 857us/step\n",
      "Epoch 56/200\n",
      "58/58 - 0s - loss: 1.6584 - accuracy: 0.4071 - 59ms/epoch - 1ms/step\n",
      "Epoch 57/200\n",
      "58/58 - 0s - loss: 1.6746 - accuracy: 0.3912 - 69ms/epoch - 1ms/step\n",
      "Epoch 58/200\n",
      "58/58 - 0s - loss: 1.6696 - accuracy: 0.3912 - 83ms/epoch - 1ms/step\n",
      "Epoch 59/200\n",
      "58/58 - 0s - loss: 1.6677 - accuracy: 0.3989 - 55ms/epoch - 946us/step\n",
      "Epoch 60/200\n",
      "58/58 - 0s - loss: 1.6681 - accuracy: 0.3940 - 63ms/epoch - 1ms/step\n",
      "Epoch 61/200\n",
      "58/58 - 0s - loss: 1.6494 - accuracy: 0.4066 - 67ms/epoch - 1ms/step\n",
      "Epoch 62/200\n",
      "58/58 - 0s - loss: 1.6469 - accuracy: 0.3956 - 83ms/epoch - 1ms/step\n",
      "Epoch 63/200\n",
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      "Epoch 64/200\n",
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      "Epoch 65/200\n",
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      "Epoch 66/200\n",
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      "Epoch 67/200\n",
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      "Epoch 68/200\n",
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      "Epoch 69/200\n",
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      "Epoch 70/200\n",
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      "Epoch 71/200\n",
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      "Epoch 72/200\n",
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      "Epoch 73/200\n",
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      "Epoch 74/200\n",
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      "Epoch 75/200\n",
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      "Epoch 76/200\n",
      "58/58 - 0s - loss: 1.6252 - accuracy: 0.4170 - 64ms/epoch - 1ms/step\n",
      "Epoch 77/200\n",
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      "Epoch 78/200\n",
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      "Epoch 79/200\n",
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      "Epoch 80/200\n",
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      "Epoch 81/200\n",
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      "Epoch 82/200\n",
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      "Epoch 83/200\n",
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      "Epoch 84/200\n",
      "58/58 - 0s - loss: 1.6180 - accuracy: 0.4170 - 66ms/epoch - 1ms/step\n",
      "Epoch 85/200\n",
      "58/58 - 0s - loss: 1.6191 - accuracy: 0.4219 - 66ms/epoch - 1ms/step\n",
      "Epoch 86/200\n",
      "58/58 - 0s - loss: 1.6221 - accuracy: 0.4170 - 66ms/epoch - 1ms/step\n",
      "Epoch 87/200\n",
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      "Epoch 88/200\n",
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      "Epoch 89/200\n",
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      "Epoch 90/200\n",
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      "Epoch 91/200\n",
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      "Epoch 92/200\n",
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      "Epoch 93/200\n",
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      "Epoch 94/200\n",
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      "Epoch 95/200\n",
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      "Epoch 96/200\n",
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      "Epoch 97/200\n",
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      "Epoch 98/200\n",
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      "Epoch 99/200\n",
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      "Epoch 100/200\n",
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      "Epoch 101/200\n",
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      "Epoch 102/200\n",
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      "Epoch 103/200\n",
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      "Epoch 104/200\n",
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      "Epoch 105/200\n",
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      "Epoch 106/200\n",
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      "Epoch 107/200\n",
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      "Epoch 108/200\n",
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      "Epoch 109/200\n",
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      "Epoch 110/200\n",
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      "Epoch 111/200\n",
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      "Epoch 112/200\n",
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      "Epoch 113/200\n",
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      "Epoch 114/200\n",
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      "Epoch 115/200\n",
      "58/58 - 0s - loss: 1.5834 - accuracy: 0.4252 - 59ms/epoch - 1ms/step\n",
      "Epoch 116/200\n",
      "58/58 - 0s - loss: 1.5896 - accuracy: 0.4258 - 52ms/epoch - 902us/step\n",
      "Epoch 117/200\n",
      "58/58 - 0s - loss: 1.5805 - accuracy: 0.4274 - 56ms/epoch - 960us/step\n",
      "Epoch 118/200\n",
      "58/58 - 0s - loss: 1.6318 - accuracy: 0.4142 - 59ms/epoch - 1ms/step\n",
      "Epoch 119/200\n",
      "58/58 - 0s - loss: 1.5856 - accuracy: 0.4290 - 66ms/epoch - 1ms/step\n",
      "Epoch 120/200\n",
      "58/58 - 0s - loss: 1.5898 - accuracy: 0.4290 - 66ms/epoch - 1ms/step\n",
      "Epoch 121/200\n",
      "58/58 - 0s - loss: 1.5800 - accuracy: 0.4312 - 50ms/epoch - 854us/step\n",
      "Epoch 122/200\n",
      "58/58 - 0s - loss: 1.5745 - accuracy: 0.4334 - 60ms/epoch - 1ms/step\n",
      "Epoch 123/200\n",
      "58/58 - 0s - loss: 1.6175 - accuracy: 0.4181 - 59ms/epoch - 1ms/step\n",
      "Epoch 124/200\n",
      "58/58 - 0s - loss: 1.5743 - accuracy: 0.4263 - 58ms/epoch - 1ms/step\n",
      "Epoch 125/200\n",
      "58/58 - 0s - loss: 1.5869 - accuracy: 0.4329 - 58ms/epoch - 993us/step\n",
      "Epoch 126/200\n",
      "58/58 - 0s - loss: 1.5781 - accuracy: 0.4219 - 58ms/epoch - 999us/step\n",
      "Epoch 127/200\n",
      "58/58 - 0s - loss: 1.5787 - accuracy: 0.4279 - 50ms/epoch - 862us/step\n",
      "Epoch 128/200\n",
      "58/58 - 0s - loss: 1.5782 - accuracy: 0.4318 - 66ms/epoch - 1ms/step\n",
      "Epoch 129/200\n",
      "58/58 - 0s - loss: 1.5962 - accuracy: 0.4258 - 59ms/epoch - 1ms/step\n",
      "Epoch 130/200\n",
      "58/58 - 0s - loss: 1.5803 - accuracy: 0.4274 - 62ms/epoch - 1ms/step\n",
      "Epoch 131/200\n",
      "58/58 - 0s - loss: 1.5709 - accuracy: 0.4301 - 67ms/epoch - 1ms/step\n",
      "Epoch 132/200\n",
      "58/58 - 0s - loss: 1.5846 - accuracy: 0.4279 - 67ms/epoch - 1ms/step\n",
      "Epoch 133/200\n",
      "58/58 - 0s - loss: 1.5800 - accuracy: 0.4329 - 67ms/epoch - 1ms/step\n",
      "Epoch 134/200\n",
      "58/58 - 0s - loss: 1.5727 - accuracy: 0.4296 - 57ms/epoch - 989us/step\n",
      "Epoch 135/200\n",
      "58/58 - 0s - loss: 1.5723 - accuracy: 0.4285 - 47ms/epoch - 808us/step\n",
      "Epoch 136/200\n",
      "58/58 - 0s - loss: 1.5759 - accuracy: 0.4351 - 74ms/epoch - 1ms/step\n",
      "Epoch 137/200\n",
      "58/58 - 0s - loss: 1.5678 - accuracy: 0.4345 - 57ms/epoch - 976us/step\n",
      "Epoch 138/200\n",
      "58/58 - 0s - loss: 1.5731 - accuracy: 0.4241 - 83ms/epoch - 1ms/step\n",
      "Epoch 139/200\n",
      "58/58 - 0s - loss: 1.5799 - accuracy: 0.4285 - 66ms/epoch - 1ms/step\n",
      "Epoch 140/200\n",
      "58/58 - 0s - loss: 1.5640 - accuracy: 0.4384 - 66ms/epoch - 1ms/step\n",
      "Epoch 141/200\n",
      "58/58 - 0s - loss: 1.5696 - accuracy: 0.4307 - 67ms/epoch - 1ms/step\n",
      "Epoch 142/200\n",
      "58/58 - 0s - loss: 1.5741 - accuracy: 0.4334 - 83ms/epoch - 1ms/step\n",
      "Epoch 143/200\n",
      "58/58 - 0s - loss: 1.5950 - accuracy: 0.4230 - 67ms/epoch - 1ms/step\n",
      "Epoch 144/200\n",
      "58/58 - 0s - loss: 1.5712 - accuracy: 0.4323 - 83ms/epoch - 1ms/step\n",
      "Epoch 145/200\n",
      "58/58 - 0s - loss: 1.5663 - accuracy: 0.4329 - 67ms/epoch - 1ms/step\n",
      "Epoch 146/200\n",
      "58/58 - 0s - loss: 1.5757 - accuracy: 0.4274 - 67ms/epoch - 1ms/step\n",
      "Epoch 147/200\n",
      "58/58 - 0s - loss: 1.5733 - accuracy: 0.4362 - 84ms/epoch - 1ms/step\n",
      "Epoch 148/200\n",
      "58/58 - 0s - loss: 1.5589 - accuracy: 0.4345 - 66ms/epoch - 1ms/step\n",
      "Epoch 149/200\n",
      "58/58 - 0s - loss: 1.5574 - accuracy: 0.4367 - 67ms/epoch - 1ms/step\n",
      "Epoch 150/200\n",
      "58/58 - 0s - loss: 1.5679 - accuracy: 0.4356 - 83ms/epoch - 1ms/step\n",
      "Epoch 151/200\n",
      "58/58 - 0s - loss: 1.5646 - accuracy: 0.4422 - 68ms/epoch - 1ms/step\n",
      "Epoch 152/200\n",
      "58/58 - 0s - loss: 1.5605 - accuracy: 0.4285 - 67ms/epoch - 1ms/step\n",
      "Epoch 153/200\n",
      "58/58 - 0s - loss: 1.5614 - accuracy: 0.4405 - 66ms/epoch - 1ms/step\n",
      "Epoch 154/200\n",
      "58/58 - 0s - loss: 1.5872 - accuracy: 0.4285 - 66ms/epoch - 1ms/step\n",
      "Epoch 155/200\n",
      "58/58 - 0s - loss: 1.5752 - accuracy: 0.4362 - 67ms/epoch - 1ms/step\n",
      "Epoch 156/200\n",
      "58/58 - 0s - loss: 1.5747 - accuracy: 0.4274 - 68ms/epoch - 1ms/step\n",
      "Epoch 157/200\n",
      "58/58 - 0s - loss: 1.5564 - accuracy: 0.4433 - 67ms/epoch - 1ms/step\n",
      "Epoch 158/200\n",
      "58/58 - 0s - loss: 1.5588 - accuracy: 0.4427 - 83ms/epoch - 1ms/step\n",
      "Epoch 159/200\n",
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      "Epoch 160/200\n",
      "58/58 - 0s - loss: 1.5608 - accuracy: 0.4356 - 67ms/epoch - 1ms/step\n",
      "Epoch 161/200\n",
      "58/58 - 0s - loss: 1.5587 - accuracy: 0.4318 - 82ms/epoch - 1ms/step\n",
      "Epoch 162/200\n",
      "58/58 - 0s - loss: 1.5760 - accuracy: 0.4345 - 68ms/epoch - 1ms/step\n",
      "Epoch 163/200\n",
      "58/58 - 0s - loss: 1.5549 - accuracy: 0.4460 - 82ms/epoch - 1ms/step\n",
      "Epoch 164/200\n",
      "58/58 - 0s - loss: 1.5557 - accuracy: 0.4405 - 83ms/epoch - 1ms/step\n",
      "Epoch 165/200\n",
      "58/58 - 0s - loss: 1.5501 - accuracy: 0.4395 - 67ms/epoch - 1ms/step\n",
      "Epoch 166/200\n",
      "58/58 - 0s - loss: 1.5532 - accuracy: 0.4427 - 73ms/epoch - 1ms/step\n",
      "Epoch 167/200\n",
      "58/58 - 0s - loss: 1.5594 - accuracy: 0.4351 - 99ms/epoch - 2ms/step\n",
      "Epoch 168/200\n",
      "58/58 - 0s - loss: 1.5578 - accuracy: 0.4356 - 62ms/epoch - 1ms/step\n",
      "Epoch 169/200\n",
      "58/58 - 0s - loss: 1.5563 - accuracy: 0.4367 - 68ms/epoch - 1ms/step\n",
      "Epoch 170/200\n",
      "58/58 - 0s - loss: 1.5661 - accuracy: 0.4356 - 64ms/epoch - 1ms/step\n",
      "Epoch 171/200\n",
      "58/58 - 0s - loss: 1.5640 - accuracy: 0.4378 - 67ms/epoch - 1ms/step\n",
      "Epoch 172/200\n",
      "58/58 - 0s - loss: 1.5461 - accuracy: 0.4400 - 64ms/epoch - 1ms/step\n",
      "Epoch 173/200\n",
      "58/58 - 0s - loss: 1.5505 - accuracy: 0.4416 - 60ms/epoch - 1ms/step\n",
      "Epoch 174/200\n",
      "58/58 - 0s - loss: 1.5490 - accuracy: 0.4444 - 59ms/epoch - 1ms/step\n",
      "Epoch 175/200\n",
      "58/58 - 0s - loss: 1.5482 - accuracy: 0.4444 - 66ms/epoch - 1ms/step\n",
      "Epoch 176/200\n",
      "58/58 - 0s - loss: 1.5500 - accuracy: 0.4384 - 67ms/epoch - 1ms/step\n",
      "Epoch 177/200\n",
      "58/58 - 0s - loss: 1.5510 - accuracy: 0.4449 - 67ms/epoch - 1ms/step\n",
      "Epoch 178/200\n",
      "58/58 - 0s - loss: 1.5552 - accuracy: 0.4395 - 67ms/epoch - 1ms/step\n",
      "Epoch 179/200\n",
      "58/58 - 0s - loss: 1.5494 - accuracy: 0.4427 - 66ms/epoch - 1ms/step\n",
      "Epoch 180/200\n",
      "58/58 - 0s - loss: 1.5535 - accuracy: 0.4438 - 70ms/epoch - 1ms/step\n",
      "Epoch 181/200\n",
      "58/58 - 0s - loss: 1.5460 - accuracy: 0.4471 - 64ms/epoch - 1ms/step\n",
      "Epoch 182/200\n",
      "58/58 - 0s - loss: 1.5570 - accuracy: 0.4296 - 73ms/epoch - 1ms/step\n",
      "Epoch 183/200\n",
      "58/58 - 0s - loss: 1.5462 - accuracy: 0.4438 - 68ms/epoch - 1ms/step\n",
      "Epoch 184/200\n",
      "58/58 - 0s - loss: 1.5425 - accuracy: 0.4384 - 61ms/epoch - 1ms/step\n",
      "Epoch 185/200\n",
      "58/58 - 0s - loss: 1.5454 - accuracy: 0.4526 - 68ms/epoch - 1ms/step\n",
      "Epoch 186/200\n",
      "58/58 - 0s - loss: 1.5448 - accuracy: 0.4460 - 67ms/epoch - 1ms/step\n",
      "Epoch 187/200\n",
      "58/58 - 0s - loss: 1.5430 - accuracy: 0.4422 - 67ms/epoch - 1ms/step\n",
      "Epoch 188/200\n",
      "58/58 - 0s - loss: 1.5472 - accuracy: 0.4411 - 73ms/epoch - 1ms/step\n",
      "Epoch 189/200\n",
      "58/58 - 0s - loss: 1.5432 - accuracy: 0.4433 - 67ms/epoch - 1ms/step\n",
      "Epoch 190/200\n",
      "58/58 - 0s - loss: 1.5409 - accuracy: 0.4438 - 72ms/epoch - 1ms/step\n",
      "Epoch 191/200\n",
      "58/58 - 0s - loss: 1.5520 - accuracy: 0.4411 - 78ms/epoch - 1ms/step\n",
      "Epoch 192/200\n",
      "58/58 - 0s - loss: 1.5400 - accuracy: 0.4466 - 74ms/epoch - 1ms/step\n",
      "Epoch 193/200\n",
      "58/58 - 0s - loss: 1.5418 - accuracy: 0.4422 - 59ms/epoch - 1ms/step\n",
      "Epoch 194/200\n",
      "58/58 - 0s - loss: 1.5405 - accuracy: 0.4482 - 60ms/epoch - 1ms/step\n",
      "Epoch 195/200\n",
      "58/58 - 0s - loss: 1.5345 - accuracy: 0.4449 - 59ms/epoch - 1ms/step\n",
      "Epoch 196/200\n",
      "58/58 - 0s - loss: 1.5551 - accuracy: 0.4444 - 67ms/epoch - 1ms/step\n",
      "Epoch 197/200\n",
      "58/58 - 0s - loss: 1.5412 - accuracy: 0.4460 - 66ms/epoch - 1ms/step\n",
      "Epoch 198/200\n",
      "58/58 - 0s - loss: 1.5551 - accuracy: 0.4378 - 52ms/epoch - 891us/step\n",
      "Epoch 199/200\n",
      "58/58 - 0s - loss: 1.5568 - accuracy: 0.4526 - 66ms/epoch - 1ms/step\n",
      "Epoch 200/200\n",
      "58/58 - 0s - loss: 1.5449 - accuracy: 0.4477 - 52ms/epoch - 904us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x279abf4aed0>"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    X_train_scaled,\n",
    "    y_train_categorical,\n",
    "    epochs=200,\n",
    "    shuffle=True,\n",
    "    verbose=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "8JsLb7hYcL0P",
    "outputId": "d7c5c2a3-d350-4981-afdc-8b22c85668dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 - 0s - loss: 1.6301 - accuracy: 0.4319 - 124ms/epoch - 6ms/step\n",
      "Normal Neural Network - Loss: 1.630128026008606, Accuracy: 0.4318554997444153\n"
     ]
    }
   ],
   "source": [
    "model_loss, model_accuracy = model.evaluate(\n",
    "    X_test_scaled, y_test_categorical, verbose=2)\n",
    "print(\n",
    "    f\"Normal Neural Network - Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J0ueoe05xo-P"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ytJOBkBwGDZk"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-VeJP3qGGDlc"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FzyPOm9wGDvT"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aYV24RCyGEH2"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sm7zH1ZvGERk"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qFRIPdr1GEZC"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EdBy-ImYGJHu"
   },
   "source": [
    " trying this with less features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "colab_type": "code",
    "id": "E7IU8-23GLhO",
    "outputId": "d2a45f2a-c7f3-4a2a-9c00-09a40de4d379"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_name</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.482</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.559</td>\n",
       "      <td>6</td>\n",
       "      <td>-9.222</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0959</td>\n",
       "      <td>0.3710</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>0.620</td>\n",
       "      <td>78.558</td>\n",
       "      <td>180387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.620</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.005</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0484</td>\n",
       "      <td>0.4170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0822</td>\n",
       "      <td>0.172</td>\n",
       "      <td>97.950</td>\n",
       "      <td>221520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.521</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.492</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3390</td>\n",
       "      <td>0.3240</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0534</td>\n",
       "      <td>0.494</td>\n",
       "      <td>100.028</td>\n",
       "      <td>292799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.520</td>\n",
       "      <td>11</td>\n",
       "      <td>-9.342</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2440</td>\n",
       "      <td>0.0503</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.3550</td>\n",
       "      <td>0.424</td>\n",
       "      <td>153.150</td>\n",
       "      <td>239360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  genre_name  danceability  energy  key  loudness  mode  speechiness   \n",
       "0    Hip-Hop         0.817   0.440   10    -8.482     0       0.0734  \\\n",
       "1    Hip-Hop         0.558   0.559    6    -9.222     1       0.0959   \n",
       "2    Hip-Hop         0.715   0.620    1    -6.005     0       0.0484   \n",
       "3    Hip-Hop         0.690   0.521   10    -8.492     0       0.3390   \n",
       "4    Hip-Hop         0.561   0.520   11    -9.342     0       0.2440   \n",
       "\n",
       "   acousticness  instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "0        0.0603          0.000001    0.3300    0.544  142.024       272113  \n",
       "1        0.3710          0.000007    0.1090    0.620   78.558       180387  \n",
       "2        0.4170          0.000000    0.0822    0.172   97.950       221520  \n",
       "3        0.3240          0.000000    0.0534    0.494  100.028       292799  \n",
       "4        0.0503          0.000002    0.3550    0.424  153.150       239360  "
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ML_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "wCM36LlpGL-u",
    "outputId": "63142c3a-cc97-42e1-823b-50cdf88d60e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     genre_name  duration_ms\n",
      "0       Hip-Hop       272113\n",
      "1       Hip-Hop       180387\n",
      "2       Hip-Hop       221520\n",
      "3       Hip-Hop       292799\n",
      "4       Hip-Hop       239360\n",
      "...         ...          ...\n",
      "2429       Mood       141000\n",
      "2430       Mood       140197\n",
      "2431       Mood       146625\n",
      "2432       Mood       189000\n",
      "2433       Mood       130625\n",
      "\n",
      "[2434 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_name</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>Dance/Electronic</td>\n",
       "      <td>79173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>Dance/Electronic</td>\n",
       "      <td>161972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>Dance/Electronic</td>\n",
       "      <td>342000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>Dance/Electronic</td>\n",
       "      <td>184000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>Dance/Electronic</td>\n",
       "      <td>245133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            genre_name  duration_ms\n",
       "2000  Dance/Electronic        79173\n",
       "2001  Dance/Electronic       161972\n",
       "2002  Dance/Electronic       342000\n",
       "2003  Dance/Electronic       184000\n",
       "2004  Dance/Electronic       245133"
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "droppedML_df = ML_df.drop(['danceability',\n",
    "                            'energy',\n",
    "                            'key',\n",
    "                            'loudness',\n",
    "                            'mode', \n",
    "                            'speechiness', \n",
    "                            'acousticness', \n",
    "                            'instrumentalness', \n",
    "                            'liveness', \n",
    "                            'valence', \n",
    "                            'tempo'],\n",
    "                           axis=1)\n",
    "\n",
    "# dropped_ML_df()\n",
    "print(droppedML_df)\n",
    "droppedML_df.iloc[2000:2005]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "rrF0GEXnGMTF",
    "outputId": "54bc9d31-a6b2-45f7-eca2-16e9774a6a56"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2434, 1) (2434,)\n"
     ]
    }
   ],
   "source": [
    "X = droppedML_df.drop(\"genre_name\", axis=1)\n",
    "y = droppedML_df[\"genre_name\"]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FgkggRa0GL0C"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ea1j7i2sGLYy"
   },
   "outputs": [],
   "source": [
    "X_scaler = MinMaxScaler().fit(X_train)\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xLj1GR3PHUYD"
   },
   "outputs": [],
   "source": [
    "# Step 1: Label-encode data set\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoder.fit(y_train)\n",
    "encoded_y_train = label_encoder.transform(y_train)\n",
    "encoded_y_test = label_encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6ggQIqyoHUzx"
   },
   "outputs": [],
   "source": [
    "# Step 2: Convert encoded labels to one-hot-encoding\n",
    "y_train_categorical = to_categorical(encoded_y_train)\n",
    "y_test_categorical = to_categorical(encoded_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0d8KgukbHUqt"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ujTXwaI2HUKa"
   },
   "outputs": [],
   "source": [
    "# Create model and add layers\n",
    "model = Sequential()\n",
    "model.add(Dense(units=10, activation='relu', input_dim =1))\n",
    "# model.add(Dense(units=10, activation='sigmoid'))\n",
    "# model.add(Dense(units=10, activation='sigmoid'))\n",
    "# model.add(Dense(units=10, activation='sigmoid'))\n",
    "# model.add(Dense(units=10, activation='relu'))\n",
    "# model.add(Dense(units=10, activation='relu'))\n",
    "# model.add(Dense(units=10, activation='relu'))\n",
    "# model.add(Dense(units=10, activation='relu'))\n",
    "# model.add(Dense(units=10, activation='relu'))\n",
    "model.add(Dense(units=10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ddZWc7YaHewO"
   },
   "outputs": [],
   "source": [
    "# Compile and fit the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "4Q9n56xRHeoL",
    "outputId": "a40a13ab-2d35-41f9-ecf5-52757af6abaa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "58/58 - 0s - loss: 2.3066 - accuracy: 0.0953 - 392ms/epoch - 7ms/step\n",
      "Epoch 2/10\n",
      "58/58 - 0s - loss: 2.2928 - accuracy: 0.1205 - 52ms/epoch - 888us/step\n",
      "Epoch 3/10\n",
      "58/58 - 0s - loss: 2.2844 - accuracy: 0.1211 - 59ms/epoch - 1ms/step\n",
      "Epoch 4/10\n",
      "58/58 - 0s - loss: 2.2792 - accuracy: 0.1288 - 51ms/epoch - 873us/step\n",
      "Epoch 5/10\n",
      "58/58 - 0s - loss: 2.2755 - accuracy: 0.1238 - 67ms/epoch - 1ms/step\n",
      "Epoch 6/10\n",
      "58/58 - 0s - loss: 2.2721 - accuracy: 0.1304 - 52ms/epoch - 888us/step\n",
      "Epoch 7/10\n",
      "58/58 - 0s - loss: 2.2697 - accuracy: 0.1321 - 56ms/epoch - 973us/step\n",
      "Epoch 8/10\n",
      "58/58 - 0s - loss: 2.2677 - accuracy: 0.1403 - 59ms/epoch - 1ms/step\n",
      "Epoch 9/10\n",
      "58/58 - 0s - loss: 2.2661 - accuracy: 0.1304 - 59ms/epoch - 1ms/step\n",
      "Epoch 10/10\n",
      "58/58 - 0s - loss: 2.2647 - accuracy: 0.1386 - 59ms/epoch - 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x279ac168ad0>"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    X_train_scaled,\n",
    "    y_train_categorical,\n",
    "    epochs=10,\n",
    "    shuffle=True,\n",
    "    verbose=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KKre1xqKHrZD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 - 0s - loss: 2.2635 - accuracy: 0.1363 - 114ms/epoch - 6ms/step\n",
      "Normal Neural Network - Loss: 2.2635278701782227, Accuracy: 0.13628900051116943\n"
     ]
    }
   ],
   "source": [
    "model_loss, model_accuracy = model.evaluate(\n",
    "    X_test_scaled, y_test_categorical, verbose=2)\n",
    "print(\n",
    "    f\"Normal Neural Network - Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IbKnl1RgwvMm"
   },
   "source": [
    "Changing the Genre Dataframe - considering Top 20 Genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 445
    },
    "colab_type": "code",
    "id": "4cA9IMIJcLlq",
    "outputId": "1fc765e6-ddcc-4469-bdaa-dd767ff1e013"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>artist_name</th>\n",
       "      <th>track_name</th>\n",
       "      <th>track_id</th>\n",
       "      <th>artist_href</th>\n",
       "      <th>track_href</th>\n",
       "      <th>genre_name</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>drake</td>\n",
       "      <td>search &amp; rescue</td>\n",
       "      <td>7aRCf5cLOFN1U7kvtChY1G</td>\n",
       "      <td>https://open.spotify.com/artist/3TVXtAsR1Inumw...</td>\n",
       "      <td>https://open.spotify.com/track/7aRCf5cLOFN1U7k...</td>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.482</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tyler, the creator</td>\n",
       "      <td>see you again (feat. kali uchis)</td>\n",
       "      <td>7KA4W4McWYRpgf0fWsJZWB</td>\n",
       "      <td>https://open.spotify.com/artist/4V8LLVI7PbaPR0...</td>\n",
       "      <td>https://open.spotify.com/track/7KA4W4McWYRpgf0...</td>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.559</td>\n",
       "      <td>6</td>\n",
       "      <td>-9.222</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0959</td>\n",
       "      <td>0.3710</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>0.620</td>\n",
       "      <td>78.558</td>\n",
       "      <td>180387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>metro boomin</td>\n",
       "      <td>creepin' (with the weeknd &amp; 21 savage)</td>\n",
       "      <td>2dHHgzDwk4BJdRwy9uXhTO</td>\n",
       "      <td>https://open.spotify.com/artist/0iEtIxbK0KxaSl...</td>\n",
       "      <td>https://open.spotify.com/track/2dHHgzDwk4BJdRw...</td>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.620</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.005</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0484</td>\n",
       "      <td>0.4170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0822</td>\n",
       "      <td>0.172</td>\n",
       "      <td>97.950</td>\n",
       "      <td>221520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>j. cole</td>\n",
       "      <td>no role modelz</td>\n",
       "      <td>68Dni7IE4VyPkTOH9mRWHr</td>\n",
       "      <td>https://open.spotify.com/artist/6l3HvQ5sa6mXTs...</td>\n",
       "      <td>https://open.spotify.com/track/68Dni7IE4VyPkTO...</td>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.521</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.492</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3390</td>\n",
       "      <td>0.3240</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0534</td>\n",
       "      <td>0.494</td>\n",
       "      <td>100.028</td>\n",
       "      <td>292799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>drake</td>\n",
       "      <td>rich flex</td>\n",
       "      <td>1bDbXMyjaUIooNwFE9wn0N</td>\n",
       "      <td>https://open.spotify.com/artist/3TVXtAsR1Inumw...</td>\n",
       "      <td>https://open.spotify.com/track/1bDbXMyjaUIooNw...</td>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.520</td>\n",
       "      <td>11</td>\n",
       "      <td>-9.342</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2440</td>\n",
       "      <td>0.0503</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.3550</td>\n",
       "      <td>0.424</td>\n",
       "      <td>153.150</td>\n",
       "      <td>239360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          artist_name                              track_name   \n",
       "0               drake                         search & rescue  \\\n",
       "1  tyler, the creator        see you again (feat. kali uchis)   \n",
       "2        metro boomin  creepin' (with the weeknd & 21 savage)   \n",
       "3             j. cole                          no role modelz   \n",
       "4               drake                               rich flex   \n",
       "\n",
       "                 track_id                                        artist_href   \n",
       "0  7aRCf5cLOFN1U7kvtChY1G  https://open.spotify.com/artist/3TVXtAsR1Inumw...  \\\n",
       "1  7KA4W4McWYRpgf0fWsJZWB  https://open.spotify.com/artist/4V8LLVI7PbaPR0...   \n",
       "2  2dHHgzDwk4BJdRwy9uXhTO  https://open.spotify.com/artist/0iEtIxbK0KxaSl...   \n",
       "3  68Dni7IE4VyPkTOH9mRWHr  https://open.spotify.com/artist/6l3HvQ5sa6mXTs...   \n",
       "4  1bDbXMyjaUIooNwFE9wn0N  https://open.spotify.com/artist/3TVXtAsR1Inumw...   \n",
       "\n",
       "                                          track_href genre_name  danceability   \n",
       "0  https://open.spotify.com/track/7aRCf5cLOFN1U7k...    Hip-Hop         0.817  \\\n",
       "1  https://open.spotify.com/track/7KA4W4McWYRpgf0...    Hip-Hop         0.558   \n",
       "2  https://open.spotify.com/track/2dHHgzDwk4BJdRw...    Hip-Hop         0.715   \n",
       "3  https://open.spotify.com/track/68Dni7IE4VyPkTO...    Hip-Hop         0.690   \n",
       "4  https://open.spotify.com/track/1bDbXMyjaUIooNw...    Hip-Hop         0.561   \n",
       "\n",
       "   energy  key  loudness  mode  speechiness  acousticness  instrumentalness   \n",
       "0   0.440   10    -8.482     0       0.0734        0.0603          0.000001  \\\n",
       "1   0.559    6    -9.222     1       0.0959        0.3710          0.000007   \n",
       "2   0.620    1    -6.005     0       0.0484        0.4170          0.000000   \n",
       "3   0.521   10    -8.492     0       0.3390        0.3240          0.000000   \n",
       "4   0.520   11    -9.342     0       0.2440        0.0503          0.000002   \n",
       "\n",
       "   liveness  valence    tempo  duration_ms  \n",
       "0    0.3300    0.544  142.024       272113  \n",
       "1    0.1090    0.620   78.558       180387  \n",
       "2    0.0822    0.172   97.950       221520  \n",
       "3    0.0534    0.494  100.028       292799  \n",
       "4    0.3550    0.424  153.150       239360  "
      ]
     },
     "execution_count": 435,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join(\"Downloads/spotify_df_Top20Genre.csv\"))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "colab_type": "code",
    "id": "vW5yNpQpcLf_",
    "outputId": "bf73d5d9-a068-4c67-e9ee-19393fbbf3b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3773 entries, 0 to 3772\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   genre_name        3773 non-null   object \n",
      " 1   danceability      3773 non-null   float64\n",
      " 2   energy            3773 non-null   float64\n",
      " 3   key               3773 non-null   int64  \n",
      " 4   loudness          3773 non-null   float64\n",
      " 5   mode              3773 non-null   int64  \n",
      " 6   speechiness       3773 non-null   float64\n",
      " 7   acousticness      3773 non-null   float64\n",
      " 8   instrumentalness  3773 non-null   float64\n",
      " 9   liveness          3773 non-null   float64\n",
      " 10  valence           3773 non-null   float64\n",
      " 11  tempo             3773 non-null   float64\n",
      " 12  duration_ms       3773 non-null   int64  \n",
      "dtypes: float64(9), int64(3), object(1)\n",
      "memory usage: 383.3+ KB\n"
     ]
    }
   ],
   "source": [
    "ML_df = df.drop(['artist_name', 'track_name', 'track_id', 'artist_href', 'track_href'], axis=1)\n",
    "ML_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "YAmohxhKw7B8",
    "outputId": "55835a77-89c6-4e21-ede7-88a508461839"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3773, 12) (3773,)\n"
     ]
    }
   ],
   "source": [
    "X = ML_df.drop(\"genre_name\", axis=1)\n",
    "y = ML_df[\"genre_name\"]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5cE2sUj4w9zB"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ya22OeKExAmA"
   },
   "outputs": [],
   "source": [
    "X_scaler = MinMaxScaler().fit(X_train)\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EOO2oVAbxC2T"
   },
   "outputs": [],
   "source": [
    "# Step 1: Label-encode data set\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoder.fit(y_train)\n",
    "encoded_y_train = label_encoder.transform(y_train)\n",
    "encoded_y_test = label_encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "klo9XChvxFIr"
   },
   "outputs": [],
   "source": [
    "# Step 2: Convert encoded labels to one-hot-encoding\n",
    "y_train_categorical = to_categorical(encoded_y_train)\n",
    "y_test_categorical = to_categorical(encoded_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3N2g0Y8LxHWh"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GB3hSdT8xJ2l"
   },
   "outputs": [],
   "source": [
    "# Create model and add layers\n",
    "model = Sequential()\n",
    "model.add(Dense(units=15, activation='relu', input_dim=12))\n",
    "# model.add(Dense(units=20, activation='sigmoid'))\n",
    "# model.add(Dense(units=20, activation='sigmoid'))\n",
    "# model.add(Dense(units=20, activation='sigmoid'))\n",
    "model.add(Dense(units=15, activation='relu'))\n",
    "model.add(Dense(units=15, activation='relu'))\n",
    "model.add(Dense(units=15, activation='relu'))\n",
    "model.add(Dense(units=15, activation='relu'))\n",
    "# model.add(Dense(units=20, activation='relu'))\n",
    "model.add(Dense(units=15, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rLcd9rP6xMRf"
   },
   "outputs": [],
   "source": [
    "# Compile and fit the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "colab_type": "code",
    "id": "Um5JNc_ZxOna",
    "outputId": "6b19c727-e15f-4784-c239-492b0f34fb2e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_31\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_130 (Dense)           (None, 15)                195       \n",
      "                                                                 \n",
      " dense_131 (Dense)           (None, 15)                240       \n",
      "                                                                 \n",
      " dense_132 (Dense)           (None, 15)                240       \n",
      "                                                                 \n",
      " dense_133 (Dense)           (None, 15)                240       \n",
      "                                                                 \n",
      " dense_134 (Dense)           (None, 15)                240       \n",
      "                                                                 \n",
      " dense_135 (Dense)           (None, 15)                240       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,395\n",
      "Trainable params: 1,395\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "2zxXQSXkxQ2z",
    "outputId": "689538a6-c6b1-4e3d-d6d0-9c2b9b9e48ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "89/89 - 1s - loss: 2.6553 - accuracy: 0.1075 - 794ms/epoch - 9ms/step\n",
      "Epoch 2/10\n",
      "89/89 - 0s - loss: 2.5012 - accuracy: 0.1573 - 100ms/epoch - 1ms/step\n",
      "Epoch 3/10\n",
      "89/89 - 0s - loss: 2.4021 - accuracy: 0.1820 - 101ms/epoch - 1ms/step\n",
      "Epoch 4/10\n",
      "89/89 - 0s - loss: 2.2866 - accuracy: 0.2213 - 115ms/epoch - 1ms/step\n",
      "Epoch 5/10\n",
      "89/89 - 0s - loss: 2.2094 - accuracy: 0.2517 - 108ms/epoch - 1ms/step\n",
      "Epoch 6/10\n",
      "89/89 - 0s - loss: 2.1654 - accuracy: 0.2641 - 92ms/epoch - 1ms/step\n",
      "Epoch 7/10\n",
      "89/89 - 0s - loss: 2.1331 - accuracy: 0.2771 - 92ms/epoch - 1ms/step\n",
      "Epoch 8/10\n",
      "89/89 - 0s - loss: 2.1146 - accuracy: 0.2849 - 92ms/epoch - 1ms/step\n",
      "Epoch 9/10\n",
      "89/89 - 0s - loss: 2.0924 - accuracy: 0.2927 - 99ms/epoch - 1ms/step\n",
      "Epoch 10/10\n",
      "89/89 - 0s - loss: 2.0695 - accuracy: 0.3026 - 105ms/epoch - 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x279b11a6b90>"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    X_train_scaled,\n",
    "    y_train_categorical,\n",
    "    epochs=10,\n",
    "    shuffle=True,\n",
    "    verbose=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "INbEZF3zxTy8"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CQ0jcpYIynJ9"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mzmcXfxs-4Ah"
   },
   "source": [
    " trying this with KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 275
    },
    "colab_type": "code",
    "id": "4Z8685-VyHPc",
    "outputId": "d5085586-3a68-4bea-84b2-cab71cbffd67"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>artist_name</th>\n",
       "      <th>track_name</th>\n",
       "      <th>track_id</th>\n",
       "      <th>artist_href</th>\n",
       "      <th>track_href</th>\n",
       "      <th>genre_name</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>drake</td>\n",
       "      <td>search &amp; rescue</td>\n",
       "      <td>7aRCf5cLOFN1U7kvtChY1G</td>\n",
       "      <td>https://open.spotify.com/artist/3TVXtAsR1Inumw...</td>\n",
       "      <td>https://open.spotify.com/track/7aRCf5cLOFN1U7k...</td>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.482</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tyler, the creator</td>\n",
       "      <td>see you again (feat. kali uchis)</td>\n",
       "      <td>7KA4W4McWYRpgf0fWsJZWB</td>\n",
       "      <td>https://open.spotify.com/artist/4V8LLVI7PbaPR0...</td>\n",
       "      <td>https://open.spotify.com/track/7KA4W4McWYRpgf0...</td>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.559</td>\n",
       "      <td>6</td>\n",
       "      <td>-9.222</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0959</td>\n",
       "      <td>0.3710</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>0.620</td>\n",
       "      <td>78.558</td>\n",
       "      <td>180387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>metro boomin</td>\n",
       "      <td>creepin' (with the weeknd &amp; 21 savage)</td>\n",
       "      <td>2dHHgzDwk4BJdRwy9uXhTO</td>\n",
       "      <td>https://open.spotify.com/artist/0iEtIxbK0KxaSl...</td>\n",
       "      <td>https://open.spotify.com/track/2dHHgzDwk4BJdRw...</td>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.620</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.005</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0484</td>\n",
       "      <td>0.4170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0822</td>\n",
       "      <td>0.172</td>\n",
       "      <td>97.950</td>\n",
       "      <td>221520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>j. cole</td>\n",
       "      <td>no role modelz</td>\n",
       "      <td>68Dni7IE4VyPkTOH9mRWHr</td>\n",
       "      <td>https://open.spotify.com/artist/6l3HvQ5sa6mXTs...</td>\n",
       "      <td>https://open.spotify.com/track/68Dni7IE4VyPkTO...</td>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.521</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.492</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3390</td>\n",
       "      <td>0.3240</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0534</td>\n",
       "      <td>0.494</td>\n",
       "      <td>100.028</td>\n",
       "      <td>292799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>drake</td>\n",
       "      <td>rich flex</td>\n",
       "      <td>1bDbXMyjaUIooNwFE9wn0N</td>\n",
       "      <td>https://open.spotify.com/artist/3TVXtAsR1Inumw...</td>\n",
       "      <td>https://open.spotify.com/track/1bDbXMyjaUIooNw...</td>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.520</td>\n",
       "      <td>11</td>\n",
       "      <td>-9.342</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2440</td>\n",
       "      <td>0.0503</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.3550</td>\n",
       "      <td>0.424</td>\n",
       "      <td>153.150</td>\n",
       "      <td>239360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          artist_name                              track_name   \n",
       "0               drake                         search & rescue  \\\n",
       "1  tyler, the creator        see you again (feat. kali uchis)   \n",
       "2        metro boomin  creepin' (with the weeknd & 21 savage)   \n",
       "3             j. cole                          no role modelz   \n",
       "4               drake                               rich flex   \n",
       "\n",
       "                 track_id                                        artist_href   \n",
       "0  7aRCf5cLOFN1U7kvtChY1G  https://open.spotify.com/artist/3TVXtAsR1Inumw...  \\\n",
       "1  7KA4W4McWYRpgf0fWsJZWB  https://open.spotify.com/artist/4V8LLVI7PbaPR0...   \n",
       "2  2dHHgzDwk4BJdRwy9uXhTO  https://open.spotify.com/artist/0iEtIxbK0KxaSl...   \n",
       "3  68Dni7IE4VyPkTOH9mRWHr  https://open.spotify.com/artist/6l3HvQ5sa6mXTs...   \n",
       "4  1bDbXMyjaUIooNwFE9wn0N  https://open.spotify.com/artist/3TVXtAsR1Inumw...   \n",
       "\n",
       "                                          track_href genre_name  danceability   \n",
       "0  https://open.spotify.com/track/7aRCf5cLOFN1U7k...    Hip-Hop         0.817  \\\n",
       "1  https://open.spotify.com/track/7KA4W4McWYRpgf0...    Hip-Hop         0.558   \n",
       "2  https://open.spotify.com/track/2dHHgzDwk4BJdRw...    Hip-Hop         0.715   \n",
       "3  https://open.spotify.com/track/68Dni7IE4VyPkTO...    Hip-Hop         0.690   \n",
       "4  https://open.spotify.com/track/1bDbXMyjaUIooNw...    Hip-Hop         0.561   \n",
       "\n",
       "   energy  key  loudness  mode  speechiness  acousticness  instrumentalness   \n",
       "0   0.440   10    -8.482     0       0.0734        0.0603          0.000001  \\\n",
       "1   0.559    6    -9.222     1       0.0959        0.3710          0.000007   \n",
       "2   0.620    1    -6.005     0       0.0484        0.4170          0.000000   \n",
       "3   0.521   10    -8.492     0       0.3390        0.3240          0.000000   \n",
       "4   0.520   11    -9.342     0       0.2440        0.0503          0.000002   \n",
       "\n",
       "   liveness  valence    tempo  duration_ms  \n",
       "0    0.3300    0.544  142.024       272113  \n",
       "1    0.1090    0.620   78.558       180387  \n",
       "2    0.0822    0.172   97.950       221520  \n",
       "3    0.0534    0.494  100.028       292799  \n",
       "4    0.3550    0.424  153.150       239360  "
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join(\"Downloads/spotify_df_Top20genre.csv\"))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "bWLBYAqlyt0Q",
    "outputId": "107a2c07-2ec3-4cd3-91c2-0acee0eec9ad"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_name</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
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       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.482</td>\n",
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       "      <td>0.0603</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.558</td>\n",
       "      <td>0.559</td>\n",
       "      <td>6</td>\n",
       "      <td>-9.222</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0959</td>\n",
       "      <td>0.3710</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>0.620</td>\n",
       "      <td>78.558</td>\n",
       "      <td>180387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.620</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.005</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0484</td>\n",
       "      <td>0.4170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0822</td>\n",
       "      <td>0.172</td>\n",
       "      <td>97.950</td>\n",
       "      <td>221520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.521</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.492</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3390</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.494</td>\n",
       "      <td>100.028</td>\n",
       "      <td>292799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hip-Hop</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.520</td>\n",
       "      <td>11</td>\n",
       "      <td>-9.342</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2440</td>\n",
       "      <td>0.0503</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.3550</td>\n",
       "      <td>0.424</td>\n",
       "      <td>153.150</td>\n",
       "      <td>239360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  genre_name  danceability  energy  key  loudness  mode  speechiness   \n",
       "0    Hip-Hop         0.817   0.440   10    -8.482     0       0.0734  \\\n",
       "1    Hip-Hop         0.558   0.559    6    -9.222     1       0.0959   \n",
       "2    Hip-Hop         0.715   0.620    1    -6.005     0       0.0484   \n",
       "3    Hip-Hop         0.690   0.521   10    -8.492     0       0.3390   \n",
       "4    Hip-Hop         0.561   0.520   11    -9.342     0       0.2440   \n",
       "\n",
       "   acousticness  instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "0        0.0603          0.000001    0.3300    0.544  142.024       272113  \n",
       "1        0.3710          0.000007    0.1090    0.620   78.558       180387  \n",
       "2        0.4170          0.000000    0.0822    0.172   97.950       221520  \n",
       "3        0.3240          0.000000    0.0534    0.494  100.028       292799  \n",
       "4        0.0503          0.000002    0.3550    0.424  153.150       239360  "
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ML_df = df.drop(['artist_name', 'track_name', 'track_id', 'artist_href', 'track_href'], axis=1)\n",
    "ML_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "zfrGYG8TytnE",
    "outputId": "a59bc635-c715-4689-9c63-3d8d0e03754d"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.817</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0.000007</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>0.620</td>\n",
       "      <td>78.558</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.715</td>\n",
       "      <td>0.620</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.005</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.172</td>\n",
       "      <td>97.950</td>\n",
       "      <td>221520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.690</td>\n",
       "      <td>0.521</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.492</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3390</td>\n",
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       "      <td>0.494</td>\n",
       "      <td>100.028</td>\n",
       "      <td>292799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.561</td>\n",
       "      <td>0.520</td>\n",
       "      <td>11</td>\n",
       "      <td>-9.342</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2440</td>\n",
       "      <td>0.0503</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.3550</td>\n",
       "      <td>0.424</td>\n",
       "      <td>153.150</td>\n",
       "      <td>239360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   danceability  energy  key  loudness  mode  speechiness  acousticness   \n",
       "0         0.817   0.440   10    -8.482     0       0.0734        0.0603  \\\n",
       "1         0.558   0.559    6    -9.222     1       0.0959        0.3710   \n",
       "2         0.715   0.620    1    -6.005     0       0.0484        0.4170   \n",
       "3         0.690   0.521   10    -8.492     0       0.3390        0.3240   \n",
       "4         0.561   0.520   11    -9.342     0       0.2440        0.0503   \n",
       "\n",
       "   instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "0          0.000001    0.3300    0.544  142.024       272113  \n",
       "1          0.000007    0.1090    0.620   78.558       180387  \n",
       "2          0.000000    0.0822    0.172   97.950       221520  \n",
       "3          0.000000    0.0534    0.494  100.028       292799  \n",
       "4          0.000002    0.3550    0.424  153.150       239360  "
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = ML_df[\"genre_name\"]\n",
    "X = ML_df.drop(\"genre_name\", axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xUbWi7N2yyAK"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "colab_type": "code",
    "id": "I-1f2r7tyzfU",
    "outputId": "79e63f19-78da-4611-f94b-64817e78ca3d"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k: 1, Train/Test Accuracy: 1.000/0.126, 1.126\n",
      "k: 3, Train/Test Accuracy: 0.450/0.109, 0.559\n",
      "k: 5, Train/Test Accuracy: 0.360/0.112, 0.472\n",
      "k: 7, Train/Test Accuracy: 0.317/0.120, 0.437\n",
      "k: 9, Train/Test Accuracy: 0.290/0.118, 0.408\n",
      "k: 11, Train/Test Accuracy: 0.276/0.123, 0.399\n",
      "k: 13, Train/Test Accuracy: 0.263/0.115, 0.378\n",
      "k: 15, Train/Test Accuracy: 0.245/0.124, 0.369\n",
      "k: 17, Train/Test Accuracy: 0.239/0.122, 0.361\n",
      "k: 19, Train/Test Accuracy: 0.235/0.128, 0.363\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_acc = []\n",
    "test_acc = []\n",
    "for k in range(1, 20, 2):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    train_accuracy = knn.score(X_train, y_train)\n",
    "    test_accuracy = knn.score(X_test, y_test)\n",
    "    all_accuracy = train_accuracy + test_accuracy\n",
    "    train_acc.append(train_accuracy)\n",
    "    test_acc.append(test_accuracy)\n",
    "    print(f\"k: {k}, Train/Test Accuracy: {train_accuracy:.3f}/{test_accuracy:.3f}, {all_accuracy:.3f}\")\n",
    "    \n",
    "plt.plot(range(1, 20, 2), train_acc, marker='o')\n",
    "plt.plot(range(1, 20, 2), test_acc, marker=\"x\")\n",
    "plt.xlabel(\"k neighbors\")\n",
    "plt.ylabel(\"Accuracy Score\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OHRl7wsFX8YS"
   },
   "source": [
    "# Feature Selection Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "UVFjuaiXZkEh",
    "outputId": "992ed7d4-5c95-4040-c282-7e82299737c6"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.stats as st\n",
    "import seaborn as sn\n",
    "\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 585
    },
    "colab_type": "code",
    "id": "bb5QK96wYAUL",
    "outputId": "85e20376-11df-4a08-872b-61c535458101"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_features = pd.DataFrame(ML_df, columns=['danceability',\n",
    "                            'energy',\n",
    "                            'key',\n",
    "                            'loudness',\n",
    "                            'mode', \n",
    "                            'speechiness', \n",
    "                            'acousticness', \n",
    "                            'instrumentalness', \n",
    "                            'liveness', \n",
    "                            'valence', \n",
    "                            'tempo'],)\n",
    "corrMatrix = all_features.corr()\n",
    "plt.figure(figsize=(10,8))\n",
    "sn.heatmap(corrMatrix, cmap='seismic', annot=True)\n",
    "fig_name = \"all_features\"\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 585
    },
    "colab_type": "code",
    "id": "g4w67X2kZV2f",
    "outputId": "5f04e1f2-c943-4ff7-88e3-5a9db4b12561"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_set_1 = pd.DataFrame(ML_df, columns=['danceability',\n",
    "                            'energy',\n",
    "                            'loudness', \n",
    "                            'valence', \n",
    "                            ],)\n",
    "corrMatrix = features_set_1.corr()\n",
    "plt.figure(figsize=(10,8))\n",
    "sn.heatmap(corrMatrix, cmap='seismic', annot=True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 585
    },
    "colab_type": "code",
    "id": "LsQfuCAgaSdl",
    "outputId": "a80f0cac-5b3f-4d77-ae00-c539cc8cf603"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_set_2 = pd.DataFrame(ML_df, columns=['danceability',\n",
    "                            'energy'\n",
    "                            ],)\n",
    "corrMatrix = features_set_2.corr()\n",
    "plt.figure(figsize=(10,8))\n",
    "sn.heatmap(corrMatrix, cmap='seismic', annot=True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oFHJF8BFbENy"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "F6vhryxTekFZ"
   },
   "source": [
    "Changing Genre Dataframe - Considering top 10 Grammy Genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 445
    },
    "colab_type": "code",
    "id": "OMclXYQ5nBAG",
    "outputId": "52c7f83b-873f-4445-913a-06172e999e98"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>artist_name</th>\n",
       "      <th>track_name</th>\n",
       "      <th>track_id</th>\n",
       "      <th>artist_href</th>\n",
       "      <th>track_href</th>\n",
       "      <th>genre_name</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>miley cyrus</td>\n",
       "      <td>flowers</td>\n",
       "      <td>0yLdNVWF3Srea0uzk55zFn</td>\n",
       "      <td>https://open.spotify.com/artist/5YGY8feqx7naU7...</td>\n",
       "      <td>https://open.spotify.com/track/0yLdNVWF3Srea0u...</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.325</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0668</td>\n",
       "      <td>0.0632</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>0.646</td>\n",
       "      <td>117.999</td>\n",
       "      <td>200455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sza</td>\n",
       "      <td>kill bill</td>\n",
       "      <td>1Qrg8KqiBpW07V7PNxwwwL</td>\n",
       "      <td>https://open.spotify.com/artist/7tYKF4w9nC0nq9...</td>\n",
       "      <td>https://open.spotify.com/track/1Qrg8KqiBpW07V7...</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.644</td>\n",
       "      <td>0.735</td>\n",
       "      <td>8</td>\n",
       "      <td>-5.747</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>0.0521</td>\n",
       "      <td>0.144000</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.418</td>\n",
       "      <td>88.980</td>\n",
       "      <td>153947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>taylor swift</td>\n",
       "      <td>anti-hero</td>\n",
       "      <td>0V3wPSX9ygBnCm8psDIegu</td>\n",
       "      <td>https://open.spotify.com/artist/06HL4z0CvFAxyc...</td>\n",
       "      <td>https://open.spotify.com/track/0V3wPSX9ygBnCm8...</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.643</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.571</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.1420</td>\n",
       "      <td>0.533</td>\n",
       "      <td>97.008</td>\n",
       "      <td>200690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>drake</td>\n",
       "      <td>search &amp; rescue</td>\n",
       "      <td>7aRCf5cLOFN1U7kvtChY1G</td>\n",
       "      <td>https://open.spotify.com/artist/3TVXtAsR1Inumw...</td>\n",
       "      <td>https://open.spotify.com/track/7aRCf5cLOFN1U7k...</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.482</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fifty fifty</td>\n",
       "      <td>cupid - twin ver.</td>\n",
       "      <td>7FbrGaHYVDmfr7KoLIZnQ7</td>\n",
       "      <td>https://open.spotify.com/artist/4GJ6xDCF5jaUqD...</td>\n",
       "      <td>https://open.spotify.com/track/7FbrGaHYVDmfr7K...</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.592</td>\n",
       "      <td>11</td>\n",
       "      <td>-8.332</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>0.4350</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.3470</td>\n",
       "      <td>0.726</td>\n",
       "      <td>120.018</td>\n",
       "      <td>174253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    artist_name         track_name                track_id   \n",
       "0   miley cyrus            flowers  0yLdNVWF3Srea0uzk55zFn  \\\n",
       "1           sza          kill bill  1Qrg8KqiBpW07V7PNxwwwL   \n",
       "2  taylor swift          anti-hero  0V3wPSX9ygBnCm8psDIegu   \n",
       "3         drake    search & rescue  7aRCf5cLOFN1U7kvtChY1G   \n",
       "4   fifty fifty  cupid - twin ver.  7FbrGaHYVDmfr7KoLIZnQ7   \n",
       "\n",
       "                                         artist_href   \n",
       "0  https://open.spotify.com/artist/5YGY8feqx7naU7...  \\\n",
       "1  https://open.spotify.com/artist/7tYKF4w9nC0nq9...   \n",
       "2  https://open.spotify.com/artist/06HL4z0CvFAxyc...   \n",
       "3  https://open.spotify.com/artist/3TVXtAsR1Inumw...   \n",
       "4  https://open.spotify.com/artist/4GJ6xDCF5jaUqD...   \n",
       "\n",
       "                                          track_href genre_name  danceability   \n",
       "0  https://open.spotify.com/track/0yLdNVWF3Srea0u...        Pop         0.707  \\\n",
       "1  https://open.spotify.com/track/1Qrg8KqiBpW07V7...        Pop         0.644   \n",
       "2  https://open.spotify.com/track/0V3wPSX9ygBnCm8...        Pop         0.637   \n",
       "3  https://open.spotify.com/track/7aRCf5cLOFN1U7k...        Pop         0.817   \n",
       "4  https://open.spotify.com/track/7FbrGaHYVDmfr7K...        Pop         0.783   \n",
       "\n",
       "   energy  key  loudness  mode  speechiness  acousticness  instrumentalness   \n",
       "0   0.681    0    -4.325     1       0.0668        0.0632          0.000005  \\\n",
       "1   0.735    8    -5.747     1       0.0391        0.0521          0.144000   \n",
       "2   0.643    4    -6.571     1       0.0519        0.1300          0.000002   \n",
       "3   0.440   10    -8.482     0       0.0734        0.0603          0.000001   \n",
       "4   0.592   11    -8.332     0       0.0331        0.4350          0.000004   \n",
       "\n",
       "   liveness  valence    tempo  duration_ms  \n",
       "0    0.0322    0.646  117.999       200455  \n",
       "1    0.1610    0.418   88.980       153947  \n",
       "2    0.1420    0.533   97.008       200690  \n",
       "3    0.3300    0.544  142.024       272113  \n",
       "4    0.3470    0.726  120.018       174253  "
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join(\"Downloads/spotify_df_Top10GrammyGenre.csv\"))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "07mxPjI_nF0A",
    "outputId": "2e5890aa-2be7-460d-a2af-15afdf26c7ca"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_name</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.325</td>\n",
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       "      <td>0.0632</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>0.646</td>\n",
       "      <td>117.999</td>\n",
       "      <td>200455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.644</td>\n",
       "      <td>0.735</td>\n",
       "      <td>8</td>\n",
       "      <td>-5.747</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>0.0521</td>\n",
       "      <td>0.144000</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.418</td>\n",
       "      <td>88.980</td>\n",
       "      <td>153947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.643</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.571</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.1420</td>\n",
       "      <td>0.533</td>\n",
       "      <td>97.008</td>\n",
       "      <td>200690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.482</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.592</td>\n",
       "      <td>11</td>\n",
       "      <td>-8.332</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>0.4350</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.3470</td>\n",
       "      <td>0.726</td>\n",
       "      <td>120.018</td>\n",
       "      <td>174253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  genre_name  danceability  energy  key  loudness  mode  speechiness   \n",
       "0        Pop         0.707   0.681    0    -4.325     1       0.0668  \\\n",
       "1        Pop         0.644   0.735    8    -5.747     1       0.0391   \n",
       "2        Pop         0.637   0.643    4    -6.571     1       0.0519   \n",
       "3        Pop         0.817   0.440   10    -8.482     0       0.0734   \n",
       "4        Pop         0.783   0.592   11    -8.332     0       0.0331   \n",
       "\n",
       "   acousticness  instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "0        0.0632          0.000005    0.0322    0.646  117.999       200455  \n",
       "1        0.0521          0.144000    0.1610    0.418   88.980       153947  \n",
       "2        0.1300          0.000002    0.1420    0.533   97.008       200690  \n",
       "3        0.0603          0.000001    0.3300    0.544  142.024       272113  \n",
       "4        0.4350          0.000004    0.3470    0.726  120.018       174253  "
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ML_df = df.drop(['artist_name', 'track_name', 'track_id', 'artist_href', 'track_href'], axis=1)\n",
    "ML_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GHE4s-oTn0Nt"
   },
   "source": [
    "trying with KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "yWGy5YSLnKMY",
    "outputId": "bdd70c27-888c-43dc-f7ae-91c68a928ec9"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.707</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.000005</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>0.646</td>\n",
       "      <td>117.999</td>\n",
       "      <td>200455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.644</td>\n",
       "      <td>0.735</td>\n",
       "      <td>8</td>\n",
       "      <td>-5.747</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>0.0521</td>\n",
       "      <td>0.144000</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.418</td>\n",
       "      <td>88.980</td>\n",
       "      <td>153947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.637</td>\n",
       "      <td>0.643</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.571</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.1420</td>\n",
       "      <td>0.533</td>\n",
       "      <td>97.008</td>\n",
       "      <td>200690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.817</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.482</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.783</td>\n",
       "      <td>0.592</td>\n",
       "      <td>11</td>\n",
       "      <td>-8.332</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>0.4350</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.3470</td>\n",
       "      <td>0.726</td>\n",
       "      <td>120.018</td>\n",
       "      <td>174253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   danceability  energy  key  loudness  mode  speechiness  acousticness   \n",
       "0         0.707   0.681    0    -4.325     1       0.0668        0.0632  \\\n",
       "1         0.644   0.735    8    -5.747     1       0.0391        0.0521   \n",
       "2         0.637   0.643    4    -6.571     1       0.0519        0.1300   \n",
       "3         0.817   0.440   10    -8.482     0       0.0734        0.0603   \n",
       "4         0.783   0.592   11    -8.332     0       0.0331        0.4350   \n",
       "\n",
       "   instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "0          0.000005    0.0322    0.646  117.999       200455  \n",
       "1          0.144000    0.1610    0.418   88.980       153947  \n",
       "2          0.000002    0.1420    0.533   97.008       200690  \n",
       "3          0.000001    0.3300    0.544  142.024       272113  \n",
       "4          0.000004    0.3470    0.726  120.018       174253  "
      ]
     },
     "execution_count": 458,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = ML_df[\"genre_name\"]\n",
    "X = ML_df.drop(\"genre_name\", axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vm403IBhnKTB"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 449
    },
    "colab_type": "code",
    "id": "DVwljjKBnKCi",
    "outputId": "6b95d72d-04f9-47fb-e992-f1e682f3faca"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k: 1, Train/Test Accuracy Score: 1.000/0.140, 1.140\n",
      "k: 3, Train/Test Accuracy Score: 0.487/0.148, 0.635\n",
      "k: 5, Train/Test Accuracy Score: 0.422/0.142, 0.563\n",
      "k: 7, Train/Test Accuracy Score: 0.374/0.164, 0.538\n",
      "k: 9, Train/Test Accuracy Score: 0.347/0.170, 0.517\n",
      "k: 11, Train/Test Accuracy Score: 0.325/0.175, 0.499\n",
      "k: 13, Train/Test Accuracy Score: 0.320/0.181, 0.501\n",
      "k: 15, Train/Test Accuracy Score: 0.308/0.190, 0.497\n",
      "k: 17, Train/Test Accuracy Score: 0.294/0.184, 0.478\n",
      "k: 19, Train/Test Accuracy Score: 0.284/0.187, 0.471\n"
     ]
    },
    {
     "data": {
      "image/png": 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7LF555RWd+cuWLcN//vMfJCcnm7WB5lZYWAiFQoGCggL4+vraujlm99T6k9ifcgtv/zUCT/NWDERE5CRM+f02uecmLS0NcXFxdeaPGTMG6enppm6OzIx1N0RE5OpMDjehoaH4448/6sz/448/EBoaapZGUeOx7oaIiFydyePczJkzBzNnzkRiYiIGDhwIkUiEw4cPY8OGDVixYoUl2kgm4Hg3RETk6ho1iF9gYCCWLl2K//73vwDUdTibN2/G2LFjzd5AMo1mvJv9KbdwPO0Oww0REbkck8MNADz66KN49NFHzd0WMpP+7VpWhZs8FhUTEZHLMbnm5tSpUzhx4kSd+SdOnMDp06fN0ihqGtbdEBGRKzM53MyYMQOZmZl15t+4cQMzZswwS6OoaTjeDRERuTKTw01SUhL69OlTZ37v3r2RlJRklkZR0/A+U0RE5MpMDjcymQw5OTl15mdlZUEqbVQJD1kAx7shIiJXZXK4iYmJwbx581BQUKCdl5+fjzfffBMxMTFmbRw1HutuiIjIVZnc1bJ06VIMHToUYWFh6N27NwAgMTERAQEB+Oabb8zeQGqcyGBfeMukHO+GiIhcjsk9N61bt8a5c+ewZMkSREREICoqCitWrMD58+c5QrEdkUrEeKBtcwCsuyEiItfSqCIZLy8vPPvss+ZuC5lZ/3YtsS/lFo5d5Xg3RETkOozuubly5Qri4+N15v3xxx8YPnw4+vbtiw8//NDsjaOmqa67yYOSdTdEROQijA43c+fOxY4dO7Sv09PTERcXB3d3dwwYMAALFy7E8uXLLdBEaqxuVXU3haWVSM7ieDdEROQajA43p0+fxujRo7Wvv/vuO3Tq1Am//vorVqxYgeXLl2PDhg2WaCM1km7dDS8JJyIi12B0uLl9+zZCQkK0r/ft24e4uDjt6wcffBB//vmnWRtHTVc93g2LiomIyDUYHW5atGiBrKwsAIBKpcLp06fRr18/7fvl5eUQBNZ12BvW3RARkasxOtwMGzYM//73v5GZmYnly5dDpVJh+PDh2veTkpLQtm1bS7SRmoB1N0RE5GqMDjcffPABkpOT0bZtW7z++utYsmQJvLy8tO9/8803GDFihMkNWLVqFcLDwyGXyxEVFYVDhw4ZXL6srAxvvfUWwsLCIJPJ0L59e6xbt87kz3UVrLshIiJXY/Q4N+Hh4UhOTkZSUhL8/PwQHBys8/57772nU5NjjM2bN+Pll1/GqlWrMGjQIHzxxRcYNWoUkpKS0KZNG73rjB8/Hjk5Ofjqq6/QoUMH5ObmorKy0qTPdTWa8W6Op93BM0Pa2bo5REREFiUSbFgo069fP/Tp0werV6/WzuvatSvGjRuHhQsX1ll+9+7dmDhxItLS0tCiRQujPqOsrAxlZWXa14WFhQgNDUVBQQF8fX2bvhMO4GxmPsZ+fgS+cikS3omFRCyydZOIiIhMUlhYCIVCYdTvt8m3XzCX8vJyxMfHIzY2Vmd+bGwsjh49qnednTt3Ijo6GkuWLEHr1q3RqVMnvPrqq7h//369n7Nw4UIoFArt5Iq3iGDdDRERuRKbhZvbt29DqVQiICBAZ35AQACys7P1rpOWlobDhw/jwoUL2L59O5YvX44ffvgBM2bMqPdzNHcw10yZmZlm3Q9HwLobIiJyJTYLNxoike4pEkEQ6szTUKlUEIlE+O6779C3b1+MHj0ay5Ytw4YNG+rtvZHJZPD19dWZXBHHuyEiIldhs3DTqlUrSCSSOr00ubm5dXpzNIKCgtC6dWsoFArtvK5du0IQBFy/ft2i7XV0HO+GiIhchcnhpm3btliwYAEyMjKa9MHu7u6IiorCnj17dObv2bMHAwcO1LvOoEGDcPPmTRQXF2vnXb58GWKx2OQrtVwN626IiMhVmBxu5syZgx9//BHt2rVDTEwMvv/+e52rkUwxe/ZsrF27FuvWrUNycjJeeeUVZGRk4PnnnwegrpeZMmWKdvknn3wSLVu2xLRp05CUlISDBw9i7ty5mD59Ojw8PBrVBlfBuhsiInIVJoebl156CfHx8YiPj0dERARmzpyJoKAgvPjiizhz5oxJ25owYQKWL1+OBQsWoFevXjh48CB27dqFsLAwAEBWVpZOD5G3tzf27NmD/Px8REdHY9KkSYiLi8Onn35q6m64JNbdEBGRK2jyODcVFRVYtWoVXn/9dVRUVCAyMhKzZs3CtGnT6i0MtiVTrpN3NhzvhoiIHJVVxrmpqKjAf//7X4wZMwZz5sxBdHQ01q5di/Hjx+Ott97CpEmTGrtpshDW3RARkSsw+vYLGmfOnMH69euxadMmSCQSTJ48GZ988gm6dOmiXSY2NhZDhw41a0Op6TR1N+pbMeQhsrWi4ZWIiIgcjMk9Nw888ABSU1OxevVqXL9+HR9//LFOsAGAiIgITJw40WyNJPNh3Q0RETk7k3tu0tLStAW/9fHy8sL69esb3SiynAHtdce7Yd0NERE5G5N7bnJzc3HixIk680+cOIHTp0+bpVFkORFBvvBh3Q0RETkxk8PNjBkz9N6f6caNGwbv8UT2QSoR44Fw9R3VOd4NERE5I5PDTVJSEvr06VNnfu/evZGUlGSWRpFl9W/HcENERM7L5HAjk8mQk5NTZ35WVhakUpNLeMgGNEXFJ9Lv8D5TRETkdEwONzExMZg3bx4KCgq08/Lz8/Hmm28iJibGrI0jy9DU3RSx7oaIiJyQyeFm6dKlyMzMRFhYGIYPH47hw4cjPDwc2dnZWLp0qSXaSGbGuhsiInJmJoeb1q1b49y5c1iyZAkiIiIQFRWFFStW4Pz58wgNDbVEG8kCWHdDRETOqlFFMl5eXnj22WfN3Rayotp1NxzvhoiInEWjK4CTkpKQkZGB8vJynfljxoxpcqPI8mrX3fBWDERE5CwaNULxo48+ivPnz0MkEkFzU3HNHcCVSqV5W0gWoam72Xspl/eZIiIip2Jyzc2sWbMQHh6OnJwceHp64uLFizh48CCio6Oxf/9+CzSRLIV1N0RE5IxM7rk5duwY9u7dCz8/P4jFYojFYgwePBgLFy7EzJkzkZCQYIl2kgWw7oaIiJyRyT03SqUS3t7eAIBWrVrh5s2bAICwsDCkpKSYt3VkURzvhoiInJHJ4SYyMhLnzp0DAPTr1w9LlizBkSNHsGDBArRr187sDSTL4Xg3RETkjEwON//3f/8HlUoFAHj//fdx7do1DBkyBLt27cKnn35q9gaSZbHuhoiInI3JNTcjR47UPm/Xrh2SkpJw584dNG/eXHvFFDkO1t0QEZGzMannprKyElKpFBcuXNCZ36JFCwYbB8W6GyIicjYmhRupVIqwsDCOZeNEWHdDRETOplE1N/PmzcOdO3cs0R6yAdbdEBGRMzG55ubTTz/FlStXEBwcjLCwMHh5eem8f+bMGbM1jqyDdTdERORMTA4348aNs0AzyJZ4nykiInImJoeb+fPnW6IdZEO8zxQRETkTk2tuyDkNqDo1xbobIiJydCb33IjFYoOXffNKKsfEuhsiInIWJoeb7du367yuqKhAQkICNm7ciPfee89sDSPrighm3Q0RETkHk8PN2LFj68z729/+hm7dumHz5s14+umnzdIwsi6JWIS+4S3wx6VcHLvKuhsiInJcZqu56devH37//XdzbY5soD/rboiIyAmYJdzcv38fK1euREhIiDk2RzaiCTdHr97G9oQbOHY1D0qVYONWERERmcbk01K1b5ApCAKKiorg6emJb7/91qyNI+vKvHMPIgD3K1R4ZXMiACBIIcf8uAg8Ehlk07YREREZy+Rw88knn+iEG7FYDD8/P/Tr1w/Nmzc3a+PIenZfyMKM/3cGtftpsgtK8cK3Z7D6H30YcIiIyCGYHG6eeuopCzSDbEmpEvDeT0l1gg0ACABEAN77KQkxEYG8RJyIiOyeyTU369evx5YtW+rM37JlCzZu3GiWRpF1nUy/g6yC0nrfFwBkFZTiZDpvlkpERPbP5HCzaNEitGrVqs58f39/fPjhh2ZpFFlXblH9waYxyxEREdmSyeHm2rVrCA8PrzM/LCwMGRkZZmkUWZe/j9yo5e6VV1q4JURERE1ncrjx9/fHuXPn6sw/e/YsWrZsaZZGkXX1DW+BIIUcDVXTzNt2Af/6Lh6pOUVWaRcREVFjmBxuJk6ciJkzZ2Lfvn1QKpVQKpXYu3cvZs2ahYkTJ1qijWRhErEI8+MiAKBOwNG8jg5rDpEI2HU+G7HLD+Ll7xOQfrvEqu0kIiIyhkgQBJNGaSsvL8fkyZOxZcsWSKXqi61UKhWmTJmCNWvWwN3d3SINNZfCwkIoFAoUFBTA19fX1s2xK7svZOG9n5J0iotrjnOTkl2ET/Zcxu6L2QDUoejxPq3x0oiOCG3haatmExGRCzDl99vkcKORmpqKxMREeHh4oHv37ggLC2tUY62N4cYwpUrAyfQ7yC0qhb+PHH3DW9S5/PvCjQIs23MZey/lAgDcJCKMjw7FiyM6IEjhYYtmExGRk7NKuHFUDDfmcybjLpb9dhmHr9wGALhLxZjUrw1eeLC90UXKRERExjDl99vkmpu//e1vWLRoUZ35H330Ef7+97+bujlyYH3aNMe3z/TD98/2R9+2LVBeqcL6I39i6JJ9WPhLMu6WlNu6iURE5IJM7rnx8/PD3r170b17d53558+fx8MPP4ycnByzNtDc2HNjGYIg4PCV21j622UkZuYDALxlUkwf1BZPD2kHhYebbRtIREQOzaI9N8XFxXqLht3c3FBYWGjq5shJiEQiDOnoh+3/GoivpkYjIsgXxWWV+HTvFQxZvBef7U1FcRnHySEiIsszOdxERkZi8+bNdeZ///33iIiIMEujyHGJRCI81DUA/3tpMFZP6oNOAd4oLK3Ex79dxtAl+/Dlwau4X660dTOJiMiJmXxaaufOnXj88cfx5JNPYsSIEQCAP/74A5s2bcKWLVswbtw4S7TTbHhayrqUKgH/O3cTy39P1Y6L4+cjw4wH2+OJfm0gk0ps3EIiInIEFr9a6ueff8aHH36ovRS8R48emD9/PoYNG9boRlsLw41tVCpV2J5wAyv+SMX1u/cBqMfQeWlER/w9OgRuEpM7EYmIyIXY7FLwxMRE9OrVy1ybswiGG9sqr1RhS3wmVv5xBdmF6sECQ1t4YOaIjni0d2tIGXKIiEgPq4abgoICfPfdd1i7di3Onj0LpdK+6ykYbuxDaYUSm05m4PN9V3G7uAwA0K6VF2Y93BFxPYIhFjd0pysiInIlFr1aSmPv3r2YNGkSgoKCsHLlSowePRqnT59u7ObIxcjdJJg2KByHXhuOeaO6oLmnG9Jul2DW94kYteIQdl/IgouNL0lERGZiUs/N9evXsWHDBqxbtw4lJSUYP3481qxZg7NnzzrMlVLsubFPxWWVWH84HV8eSkNRqfqS8cjWvpgd0wnDO/tDJGJPDhGRK7NIz83o0aMRERGBpKQkrFy5Ejdv3sTKlSub3FgiQD3g30sPdcTh10bgpREd4OUuwYUbhZi+4TQeW30Uh1NvsyeHiIiMYnTPjVQqxcyZM/HCCy+gY8eO2vlubm7suSGzu1NSji8OXsXGo3+itEIFAOgb3gJzYjqhX7uWNm4dERFZm0V6bg4dOoSioiJER0ejX79++Oyzz3Dr1q0mN5ZInxZe7pg3qisOvjYc0wa1hbtEjJPpdzDhy+OY/NUJJGTctXUTiYjITpl8tdS9e/fw/fffY926dTh58iSUSiWWLVuG6dOnw8fHx1LtNBv23DimrIL7+GzvFWw+lYlKlfqP7ENd/PFKTCdEtlbYuHVERGRpVrsUPCUlBV999RW++eYb5OfnIyYmBjt37mzs5qyC4caxZd65h0//SMW2hBtQVoWcUZGBeCWmEzoFVIdrpUrAyfQ7yC0qhb+PHH3DW0DCy8uJiByW1QfxUyqV+Omnn7Bu3TqGG7KKtFvFWPFHKnaevQlBAEQiYEzPYMx6qCMu5xThvZ+SkFVQql0+SCHH/LgIPBIZZMNWExFRY9lshGJHwHDjXC7nFGH575ex63w2AEAsAlR6/kRr+mxW/6MPAw4RkQOyyiB+RPagU4APVk2Kwv9eGoyHuvjpDTYAoJn93k9J2tNZRETknBhuyClEtlbgmSHtDS4jAMgqKMXJ9DvWaRQREdkEww05jdyi0oYXAnD6zzscEJCIyIkx3JDT8PeRG7Xc0j2XEfPJQaw5cBU5hcYFIiIichwsKCanoVQJGLx4L7ILSlHfH2oPNzGUKgHlSvUSYhEwuKMf/hYVgtiIAMjdJNZrMBERGY1XSxnAcOPcdl/IwgvfngEAnYBT82qpgR1aYde5LGw9cx2n/qwe6dhHLsVfewTh8T4hiAprzpt1EhHZEYYbAxhunN/uC1lGj3Pz5+0SbDtzHVvP3MCN/Pva+eGtvPBY79Z4tE9rhDT3tFrbiYhIP4cKN6tWrcJHH32ErKwsdOvWDcuXL8eQIUMaXO/IkSMYNmwYIiMjkZiYaPTnMdy4BlNHKFapBBxPz8PW+Bv45UIW7pUrte8NaNcSf4sKwSORgfCSSa3RfCIiqsVhws3mzZsxefJkrFq1CoMGDcIXX3yBtWvXIikpCW3atKl3vYKCAvTp0wcdOnRATk4Oww2ZVUlZJXZfyMYP8ddxLC1PO9/TXYJRkUH4W1QI+oW3gJi3cyAishqHCTf9+vVDnz59sHr1au28rl27Yty4cVi4cGG9602cOBEdO3aERCLBjh07DIabsrIylJWVaV8XFhYiNDSU4YaMcv3uPWw/cwM/nLmOa3n3tPNDmnvgsd6t8XhUCMJaetmwhURErsEhRiguLy9HfHw8YmNjdebHxsbi6NGj9a63fv16XL16FfPnzzfqcxYuXAiFQqGdQkNDm9Ruci0hzT3x0kMdsf/VB/HD8wPwRN9Q+MikuH73Pj7dewXDPtqPv685iu9PZqCwtMLWzSUiIgA2KyC4ffs2lEolAgICdOYHBAQgOztb7zqpqal44403cOjQIUilxjV93rx5mD17tva1pueGyBQikQjRbVsgum0LzI/rhl8vZmPrmRs4nHoLp/68i1N/3sX8nRfxSGQgHu8TgkEdWvEu5ERENmLz6sjal9sKgqD3ElylUoknn3wS7733Hjp16mT09mUyGWQyWZPbSaQhd5NgbK/WGNurNbILSrE94Qa2nrmOK7nF+DHxJn5MvIlAXzke7dMaj/cJQQd/b1s3mYjIpdis5qa8vByenp7YsmULHn30Ue38WbNmITExEQcOHNBZPj8/H82bN4dEUj3ImkqlgiAIkEgk+O233zBixIgGP5cFxWQJgiDg3PUC/BB/HTvP3kTB/epTVD1Dm+FvUSGI6xGEZp7uNmwlEZHjcqiC4qioKKxatUo7LyIiAmPHjq1TUKxSqZCUlKQzb9WqVdi7dy9++OEHhIeHw8ur4cJOhhuytLJKJfYm5+KH+OvYf/mW9i7k7hIxHo7wx+N9QjCskx+kEt79hIjIWKb8ftv0tNTs2bMxefJkREdHY8CAAfjyyy+RkZGB559/HoC6XubGjRv4+uuvIRaLERkZqbO+v78/5HJ5nflEtiSTSjCqexBGdQ/CraIy/Jh4Az/EX8el7CLsOp+NXeez0cpbhnG9gvF4VAi6BjFkExGZk03DzYQJE5CXl4cFCxYgKysLkZGR2LVrF8LCwgAAWVlZyMjIsGUTiZrEz0eGZ4a0wzND2uHizQJsjb+BHxNv4HZxGdYeTsfaw+noFuyLx/uEYGyvYLT01q0PM3UwQiIisoMRiq2Np6XI1iqUKuxPuYWt8dfxx6UcVFTdxFMqFmF4F/VpqxFd/LH3Uo7Rt5EgInJ2DlNzYwsMN2RP7paUY+fZm9h65jrOXS/Qzvdyl6Ckxi0gNGreAJQBh4hcCcONAQw3ZK8u5xRha/x1bDtzHbeKy+tdTgQgUCHH4ddH8BQVEbkMhxihmIh0dQrwwbzRXfHJhF4GlxMAZBWUYtEvyUjOKtRejUVERGo2H8SPiHTlldTfa1PTfw6l4z+H0uHpLkFkawV6hTZDz5Bm6BmqQOtmHnoHwyQicgUMN0R2xt9HbtRyEUG+uJZXgpJyJU6m38HJ9Dva91p5y9ArVFEVdtShR+HpZqkmExHZFYYbIjvTN7wFghRyZBeUQt8JJ03NzU8vDQYApN0qRmJmPs5ez0diZj4uZRXhdnEZfk/Oxe/Judr12rXyqgo6CvQMbYauQb6Qu0n0fAIRkWNjQTGRHdp9IQsvfHsGAHQCjjFXS5VWKHHxZiHOVgWes5n5+DPvXp3l3CQidA3yrXE6qxnatfKCmEXKRGSHeLWUAQw35Ch2X8gy2zg3d0vKq4JOgbaH546e2h4fmRQ9Qqvrd3qFNoO/r3GnyYiILInhxgCGG3IklhqhWBAEXL97X306q6qH5/yNApRWqOosG6SQq4NOG3Xg6R6igLfMtDPaHGmZiJqK4cYAhhsi/SqVKlzOKVb37GSoA8/lnCLUvtJcJAI6+nvrBJ7OgT5wq+dGoObsgSIi18VwYwDDDZHxSsoqceFGgfaUVmJmPm7k36+znEwqRmRrhTbw9ApphtAWHvj1YjZe+PZMncJojrRMRKZiuDGA4YaoaXKLSnGuRu3O2cx8FJZW1lmumYcU9ytUKKuse6oL4EjLRGQaU36/eSk4EZnE30eOhyPkeDgiAACgUgn4M69Ep3cn6WYh8u/XDTw1aUZaPnrlNoZ08rNCy4nIVbDnhojMrrxShS8OXsXS3y43uKxYBHTw90anAB90CfRBpwAfdA70QWhzT16WTkRa7LkhIptyl4oRHdbCqGVVAnA5pxiXc4rxv3NZ2vkebhJ0DFCHns4BPugUqH4M8JXx1hJEZBDDDRFZhLEjLf/3uQG4cqsYl7OLkJJThMs5RUjNKcb9CiXOXS/AuesFOuspPNyqwo63+rGqp6eZp7tV9ouI7B9PSxGRxTR2pGWlSsC1vBJczilCSnax+jGnCOm3S+q9C7q/jwydA3V7eToGeMPTnf+HI3IGvFrKAIYbIusy5zg3ZZVKpN0qQYqml6fq8frdupenA+oxeUKbe1b17nijc6AvOgf4ILyVF9yl+sflMYSDERLZDsONAQw3RNZn6VBQXFaJ1KpTWpeyi7Q9PreLy/QuLxWL0M7Pq049T5sW9RcxczBCIttiuDGA4YbIdeQVl1UVK1eHnsvZRSgq03+ZutxNjE6aOp4aoSch4y7+9Z1zDUbIXihyNAw3BjDcELk2QRCQVVBafVqr6tRWam4xyg0MOGjoH8oAXxl2vzwUXu5SuElEdn81F3uhyBEx3BjAcENE+ugrYr6UXYj02yV17q9liFgEyN0k6kkqhtxNApmbBHI3MeTSqkfN+25iyKTVz2uuo33fTaJ3PXnVejKp2KTxgDRF3s7UC0WugeHGAIYbIjLFD/GZeHXLOVs3wyB3qRgeOgGpRjCqEZjcpSLsOp+Ne+XKercVxFtikJ3iIH5ERGbSupmnUcttnP4AeoU0R2mlEqUVSpRWqKoelSitrH5eVqHSs0z1vDLNvEr1/Pvl6udltbZX85L48koVyitVKNB/0ZhJsgpKEf3+HoS28IS/jwx+PnL4+8jg7yuDf43nrbxl9d4J3ppYO0T6MNwQERlg7GCEgzv4QSIWQQE3q7SrQqnSCUhllbXCUo2ApAlFiZn5OqNA1+fuvQrcvVdgcBmRCGjh6Q5/36rAoycA+fvI4ecjg9xNYq7d1sHaIaoPww0RkQESsQjz4yLwwrdn6hQWa/oH5sdFWL23wE0ihptEDB+58escu5pnVLh5f1wkAnzlyC0qRW5hGXKLynCrqBS5RWXILSzDreIyKFUC8krKkVdSjuQGNukrl8LfV46AGuHHz0dWKxjJ4S0z/iepvtqh7IJSvPDtGdYOuTiGGyKiBjwSGYTV/+hTp5cg0MF6CYzthXqibxuDYU2lEnDnXjlyC8uQU1SKW4Vl6iBUFX60z4vKUF6pQmFpJQpLi3Elt9hg+zzdJVVhR17dC+QrqzVPBm+ZFO/9lKR3H4Sq/XjvpyTERATyFJWLYkExEZGRnKG+o7G3xGgMQRBQeL+yRtip7gnKLSpDTmEpbhWVIbewFCUGipxrk4pFqDTiErZN/+yHAe1bNWUXyI7waikDGG6IyNXZY61KSVllVc9Pda9PrrZXqLo3KP9ehdHblIhFCG4mR4CPHAG+6p6fQN/q5wFVz005HUa2w3BjAMMNEZHj9kKVVSrx68VszNyUaLZterlL6gQef5/q5wFV8y1VGA047vGwJoYbAxhuiIgcm1IlYPDivQZrhwIUcnz/z/7IKylDTqH6FFhOobpnKKeoFNkF6lNk9d2KQx+Fh5s26Pj7VIeeAF9ZVcG0HH7eMpNvymqPPWn2iOHGAIYbIiLHZ67aIc3pMHX4UQeenMJS5NSYl1NYitIK/bfm0Kell/oS+UBNENL0/vhU9wS19JZBIhZxxGgTMNwYwHBDROQcrNXjIQgCCksr1b0+2vBTIwhpeoWKSlGhNO4nVSwCWnm74+69CoPrBPjKcHDucMgseErMUTDcGMBwQ0TkPOypVkWlEnD3Xrk6ABWV6oahGkHodnGZSfcrA9SnxFp4uaO5p+bRXf3o5Y4WnlWPmsnTHT5yqUn3HDMXSx4P3n6BiIhcgkQswoD2LW3dDACAWCxCS2/1KacI1P/jW6lUIa+kHP89nYmlv102atsF9ytQcL8C6Ua2RSIWobmnG5p71g5AbnWCkSYUebpLmnRHe3uqHWK4ISIisiKpRIwAXzmiw1oYtfyaf/RBez9v3Ckpx9175bhTUlH1WI67JeW4c6/mYwWKyyqhVAm4XVyO28XlRrfLXSrWH4Lq9BJV9x5priCztxGjGW6IiIhswNgRo00dabmsUon8exW4U1KunXTDUIX6sWp+Xkm59uar2YWlyC4sbfhDqni5S9DM0w25RWV2NWI0ww0REZENWOq+ZTKpBAG+6rF7jCEIAu5XKKvCT0V1T1CN8HNXJySpe46UKgEl5coGR5cWoL7b/Mn0O1Y7hchwQ0REZCP2cN8ykUgET3cpPN2lCGlu3DqaK8julpTjx7M38Mme1AbXyS0yvkeoqRhuiIiIbOiRyCDERATazVVfxhCJRFB4uEHh4Ya+bVsCaDjc+JtyC/smYrghIiKyMXu66stUxtYO9Q03roDaHEwbI5qIiIioBk3tEFBdK6TRlNqhpmC4ISIioibR1A4FKnRPPQUq5Da5hQRPSxEREVGT2VPtEMMNERERmYW91A7xtBQRERE5FYYbIiIicioMN0RERORUGG6IiIjIqTDcEBERkVNhuCEiIiKnwnBDREREToXhhoiIiJwKww0RERE5FYYbIiIicioMN0RERORUGG6IiIjIqTDcEBERkVNhuCEiIiKnwnBDREREToXhhoiIiJwKww0RERE5FYYbIiIicioMN0RERORUGG6IiIjIqTDcEBERkVNhuCEiIiKnYvNws2rVKoSHh0MulyMqKgqHDh2qd9lt27YhJiYGfn5+8PX1xYABA/Drr79asbVERERk72wabjZv3oyXX34Zb731FhISEjBkyBCMGjUKGRkZepc/ePAgYmJisGvXLsTHx2P48OGIi4tDQkKClVtORERE9kokCIJgqw/v168f+vTpg9WrV2vnde3aFePGjcPChQuN2ka3bt0wYcIEvPPOO0YtX1hYCIVCgYKCAvj6+jaq3URERGRdpvx+26znpry8HPHx8YiNjdWZHxsbi6NHjxq1DZVKhaKiIrRo0aLeZcrKylBYWKgzERERkfOyWbi5ffs2lEolAgICdOYHBAQgOzvbqG0sXboUJSUlGD9+fL3LLFy4EAqFQjuFhoY2qd1ERGQH9i0EDizR/96BJer3yWXZvKBYJBLpvBYEoc48fTZt2oR3330Xmzdvhr+/f73LzZs3DwUFBdopMzOzyW0mIiIbE0uAfR/UDTgHlqjniyW2aZersrOwKbXqp9XQqlUrSCSSOr00ubm5dXpzatu8eTOefvppbNmyBQ8//LDBZWUyGWQyWZPbS0RENiIIQMV94P7d6smvC9B5tDrIXN0HhA8Frp8Eru4Fuo4B/CPU8929AXevqqnquVQGGPGfaKvYt1AdxIa9Vve9A0sAlRIYPs/67TKVJmwCuvuiCZvD37Jqc2wWbtzd3REVFYU9e/bg0Ucf1c7fs2cPxo4dW+96mzZtwvTp07Fp0yb85S9/sUZTiYichy1/TAUBKC/WDSn6pnt65inL6t9uxlH1pJG8Uz3VRySpFXqqgo9MTxCq87ye99w8GxeY7CwUaKlUgKoCUJYDSs2jgefBvYHIv6nbnH0BiHwUuJ1avQ/6/rxZkM3CDQDMnj0bkydPRnR0NAYMGIAvv/wSGRkZeP755wGoTynduHEDX3/9NQB1sJkyZQpWrFiB/v37a3t9PDw8oFAobLYfREQOwxw/pioVUFZYI3zcAe7nNxxa7t8FVJVNaLsU8Ghedzq3GRBUgEis7s0pL6kxFVc/r7yv3o6gBMoK1JPZiGoFICMDUsv2QPfx6u++8CbQZwoQvx448zXQYyIQ2ANI2mkgWBgRPJQV6nBo9LLlTTtOyT+qJ8AmwQawcbiZMGEC8vLysGDBAmRlZSEyMhK7du1CWFgYACArK0tnzJsvvvgClZWVmDFjBmbMmKGdP3XqVGzYsMHazSciV+Espw6A6n3Y94E6pPT9J3BwCXBiDdB9AtAsDDi+xnBAKc1Xh4nGksgAzxa1Qkoz/cGl5uTuXbd35MASdVsk7uof5aCe9f+YqpT1B596nxvxHgT1VF5c9bqR4terJ41z36snuyBSn86TuAMSt1qPtZ5fOwpAUD+3QbABbDzOjS1wnBsiMlnNXo36ejss/Y+4sqKRP8DFQJme90rzm/a/cwBw8zI+nNQMM24e5vhG6n7/1jweGpp6oEaFpVqPty9Xb9evS/3hocHnjV3PwHNjC7Q1x0ATNs14LEz5/bZpzw0ROTln6fGo2duheV3fD6kgAJWl5u0ZKC9R/1BYUrOwhntOave0SG14sYa+71/fcbI0kQhw91RP8Gv8dmqHgsjHbdbr0Wj1hU3AtWpuiMjJ2VOxpCCoez8q7wMVpVWPVVNlqYHH0up1gvuo271/ofpUiKINcOl/6pqPmmGkKadsGiKRGVHkaqjeo+p5wrfA8c+rf0x7/8OxfkxVSv29AprXKqX129RYdhQKGs1ewmYVhhsishxDPR4PvgkMeBG4d8eIgGHqY2mtEFP1aK7QodlOQYZ6qo+bZ+PDR33vSdya3v4DS9TBxpF/TA31+DnKPgB2Fwoazc7CJmtuiOyRPZ3OMUcR5p2rQHFO9TZFEvUVKzYjUtd9SOUGHuWA1EP38cYZ4M9D1e3v9ijQa5L+IOLmaZ8DydlD/RBVs6e/63aONTdEjq6xp3OaUnRaezlNEarm8llzqh1sxNK6QcJg8DDwaMwyEnfTxyQ5sEQdbGr3dvhHOFYYsLP/Ybs8Z+mBsjMMN0T2aNhrQMU99Y/n9VNAq07AtSPAzQSgZUf1j+zl3dYtOhWJAXefxtV5pPyivqRV7KYeGGzADGDQK9VhRmLn/xQ5y6kDgD+m5BLs/F8UIhdQUQrcTgFykoDci1WPSUBRlvr91N/Uk0ZeqnoyROJuYp1Hfe95N33I+gNL1MGmdo+HvJnj/Jiyt4PIoTDcEFmLSgXkX1MHl5pBJu9K/fUnzdoABZnqK31EEiD23w0HEzcvQOpu3X2rj7P0eLC3g8ihMNwQWcK9O0DOxaogc0EdYm5dqn/0UnkzIKCbun4joJt68uuiHjW25tgX5SXqUzqOgj0eRGQDDDekxor9xqkoVYeW3KQaYSYJKM7Wv7zEHfDrDPh3AwIiqh99gvQPK+/oY1+wx4OIbIDhhtTsabA1e6RSAfl/VtfDaIJM3lUDp5TCavTGVAWZlu2NG6fEWU7nEBHZAMMNqdX84VRWAINfBo5+Buz/0LHGvTBHD1RJXlU9zMXqEJN7Cago0b+8R3MgIFI3xPh3AWQ+jd8Pns4hImo0DuLXVPZ0OkdzT5vSQqCssOqxoNbrmo8F1Y+aeffv6o7iKnGvuhuvV92rZxo74qolBzYzZYCyivvqU0q1e2NqDjZXk0SmPqVUuzfGJ7BxVxEREZHROIifNZnrdE7NYFIzdOgNJbXDSY35qgrz7p+yvP4f+8aSejRxSHo962iuDtJ36mb/YnUPVOTj6u/5v1PUQeZOWv3D8TdvW10PE9BN/bxFO/sfj4WIiNhzYxaaIDNwJtB7svqeLfEb1EOztxtug2AiAmS+gNy34Ud5M915id8Bxz6rvjqn/wvq4eUbeyfjmqPdWnK4fbGbbvApK6waJ0YEwMAfcc+W1VcoaR79ugAyb8u1lYiITMaeG2sb9hpwPx84+ql60ri4XT2ZQiRW12rIFEaEE4X++e7egFhs+n4cWKIONrWvzvFo0fSaG0EAKstMuAWAobBUc0TeMvX2VRVAab560v1g9YNUXusqpQh1nYy3P08pERE5GYYbcxk2V91joxEQaWIwqQoz7t62+bG19NU5IpF6qH03OeDVsmltrUlZoT/0nPkaOP9f9T2LVJXA4FeAB98w3+cSEZHdYrgxlxNfqh81p3MixjrOFUaA416dI3EDPJqpJ40DS9TBpnYPlEjsWMeEiIgaheHGHA4s0b1kmoOt2Q7HhyEicnkMN03FH1P74qg9UEREZDYMN03FH1P74iw9UERE1Gi8FJyIiIjsnim/3424XpiIiIjIfjHcEBERkVNhuCEiIiKnwnBDREREToXhhoiIiJwKww0RERE5FYYbIiIicioMN0RERORUGG6IiIjIqTDcEBERkVNxuXtLae42UVhYaOOWEBERkbE0v9vG3DXK5cJNUVERACA0NNTGLSEiIiJTFRUVQaFQGFzG5W6cqVKpcPPmTfj4+EAkEtm6OVZRWFiI0NBQZGZmutTNQl11vwHX3XdX3W+A++6K++5q+y0IAoqKihAcHAyx2HBVjcv13IjFYoSEhNi6GTbh6+vrEn8BanPV/QZcd99ddb8B7rsr7rsr7XdDPTYaLCgmIiIip8JwQ0RERE6F4cYFyGQyzJ8/HzKZzNZNsSpX3W/AdffdVfcb4L674r676n4bw+UKiomIiMi5seeGiIiInArDDRERETkVhhsiIiJyKgw3RERE5FQYbhzYwoUL8cADD8DHxwf+/v4YN24cUlJSDK6zf/9+iESiOtOlS5es1GrzePfdd+vsQ2BgoMF1Dhw4gKioKMjlcrRr1w5r1qyxUmvNq23btnqP4YwZM/Qu76jH/ODBg4iLi0NwcDBEIhF27Nih874gCHj33XcRHBwMDw8PPPjgg7h48WKD2926dSsiIiIgk8kQERGB7du3W2gPGs/QvldUVOD1119H9+7d4eXlheDgYEyZMgU3b940uM0NGzbo/XNQWlpq4b0xTUPH/amnnqqzD/37929wu/Z+3Bvab33HTiQS4aOPPqp3m45yzC2B4caBHThwADNmzMDx48exZ88eVFZWIjY2FiUlJQ2um5KSgqysLO3UsWNHK7TYvLp166azD+fPn6932fT0dIwePRpDhgxBQkIC3nzzTcycORNbt261YovN49SpUzr7vWfPHgDA3//+d4PrOdoxLykpQc+ePfHZZ5/pfX/JkiVYtmwZPvvsM5w6dQqBgYGIiYnR3j9On2PHjmHChAmYPHkyzp49i8mTJ2P8+PE4ceKEpXajUQzt+71793DmzBm8/fbbOHPmDLZt24bLly9jzJgxDW7X19dX589AVlYW5HK5JXah0Ro67gDwyCOP6OzDrl27DG7TEY57Q/td+7itW7cOIpEIjz/+uMHtOsIxtwiBnEZubq4AQDhw4EC9y+zbt08AINy9e9d6DbOA+fPnCz179jR6+ddee03o0qWLzrznnntO6N+/v5lbZn2zZs0S2rdvL6hUKr3vO8MxByBs375d+1qlUgmBgYHCokWLtPNKS0sFhUIhrFmzpt7tjB8/XnjkkUd05o0cOVKYOHGi2dtsLrX3XZ+TJ08KAIRr167Vu8z69esFhUJh3sZZmL59nzp1qjB27FiTtuNox92YYz527FhhxIgRBpdxxGNuLuy5cSIFBQUAgBYtWjS4bO/evREUFISHHnoI+/bts3TTLCI1NRXBwcEIDw/HxIkTkZaWVu+yx44dQ2xsrM68kSNH4vTp06ioqLB0Uy2mvLwc3377LaZPn97gjWCd4ZhrpKenIzs7W+eYymQyDBs2DEePHq13vfr+HBhaxxEUFBRAJBKhWbNmBpcrLi5GWFgYQkJC8Ne//hUJCQnWaaCZ7d+/H/7+/ujUqRP++c9/Ijc31+Dyznbcc3Jy8PPPP+Ppp59ucFlnOeamYrhxEoIgYPbs2Rg8eDAiIyPrXS4oKAhffvkltm7dim3btqFz58546KGHcPDgQSu2tun69euHr7/+Gr/++iv+85//IDs7GwMHDkReXp7e5bOzsxEQEKAzLyAgAJWVlbh9+7Y1mmwRO3bsQH5+Pp566ql6l3GWY15TdnY2AOg9ppr36lvP1HXsXWlpKd544w08+eSTBm+e2KVLF2zYsAE7d+7Epk2bIJfLMWjQIKSmplqxtU03atQofPfdd9i7dy+WLl2KU6dOYcSIESgrK6t3HWc77hs3boSPjw8ee+wxg8s5yzFvDJe7K7izevHFF3Hu3DkcPnzY4HKdO3dG586dta8HDBiAzMxMfPzxxxg6dKilm2k2o0aN0j7v3r07BgwYgPbt22Pjxo2YPXu23nVq92wIVYNzN9TjYc+++uorjBo1CsHBwfUu4yzHXB99x7Sh49mYdexVRUUFJk6cCJVKhVWrVhlctn///jqFt4MGDUKfPn2wcuVKfPrpp5ZuqtlMmDBB+zwyMhLR0dEICwvDzz//bPDH3pmO+7p16zBp0qQGa2ec5Zg3BntunMBLL72EnTt3Yt++fQgJCTF5/f79+zt8kvfy8kL37t3r3Y/AwMA6/0vLzc2FVCpFy5YtrdFEs7t27Rp+//13PPPMMyav6+jHXHNlnL5jWvt/6LXXM3Ude1VRUYHx48cjPT0de/bsMdhro49YLMYDDzzg0H8OAHXPZFhYmMH9cKbjfujQIaSkpDTq772zHHNjMNw4MEEQ8OKLL2Lbtm3Yu3cvwsPDG7WdhIQEBAUFmbl11lVWVobk5OR692PAgAHaq4o0fvvtN0RHR8PNzc0aTTS79evXw9/fH3/5y19MXtfRj3l4eDgCAwN1jml5eTkOHDiAgQMH1rtefX8ODK1jjzTBJjU1Fb///nujArogCEhMTHToPwcAkJeXh8zMTIP74SzHHVD31kZFRaFnz54mr+ssx9wotqtlpqZ64YUXBIVCIezfv1/IysrSTvfu3dMu88YbbwiTJ0/Wvv7kk0+E7du3C5cvXxYuXLggvPHGGwIAYevWrbbYhUabM2eOsH//fiEtLU04fvy48Ne//lXw8fER/vzzT0EQ6u53Wlqa4OnpKbzyyitCUlKS8NVXXwlubm7CDz/8YKtdaBKlUim0adNGeP311+u85yzHvKioSEhISBASEhIEAMKyZcuEhIQE7RVBixYtEhQKhbBt2zbh/PnzwhNPPCEEBQUJhYWF2m1MnjxZeOONN7Svjxw5IkgkEmHRokVCcnKysGjRIkEqlQrHjx+3+v4ZYmjfKyoqhDFjxgghISFCYmKizt/9srIy7TZq7/u7774r7N69W7h69aqQkJAgTJs2TZBKpcKJEydssYv1MrTvRUVFwpw5c4SjR48K6enpwr59+4QBAwYIrVu3dvjj3tCfd0EQhIKCAsHT01NYvXq13m046jG3BIYbBwZA77R+/XrtMlOnThWGDRumfb148WKhffv2glwuF5o3by4MHjxY+Pnnn63f+CaaMGGCEBQUJLi5uQnBwcHCY489Jly8eFH7fu39FgRB2L9/v9C7d2/B3d1daNu2bb3/QDiCX3/9VQAgpKSk1HnPWY655hL22tPUqVMFQVBfDj5//nwhMDBQkMlkwtChQ4Xz58/rbGPYsGHa5TW2bNkidO7cWXBzcxO6dOlilyHP0L6np6fX+3d/37592m3U3veXX35ZaNOmjeDu7i74+fkJsbGxwtGjR62/cw0wtO/37t0TYmNjBT8/P8HNzU1o06aNMHXqVCEjI0NnG4543Bv68y4IgvDFF18IHh4eQn5+vt5tOOoxtwSRIFRVVRIRERE5AdbcEBERkVNhuCEiIiKnwnBDREREToXhhoiIiJwKww0RERE5FYYbIiIicioMN0RERORUGG6IiIjIqTDcEJHR/vzzT4hEIiQmJtq6KVqXLl1C//79IZfL0atXL4t/Xtu2bbF8+XKjlzfmO9uwYQOaNWvW5LYRkRrDDZEDeeqppyASibBo0SKd+Tt27IBIJLJRq2xr/vz58PLyQkpKCv744w+9y5jzezt16hSeffbZRreXiCyP4YbIwcjlcixevBh37961dVPMpry8vNHrXr16FYMHD0ZYWJjBu2Ob63vz8/ODp6dnk7ZhLRUVFbZuApFNMNwQOZiHH34YgYGBWLhwYb3LvPvuu3VO0Sxfvhxt27bVvn7qqacwbtw4fPjhhwgICECzZs3w3nvvobKyEnPnzkWLFi0QEhKCdevW1dn+pUuXMHDgQMjlcnTr1g379+/XeT8pKQmjR4+Gt7c3AgICMHnyZNy+fVv7/oMPPogXX3wRs2fPRqtWrRATE6N3P1QqFRYsWICQkBDIZDL06tULu3fv1r4vEokQHx+PBQsWQCQS4d13323S9wYAR48exdChQ+Hh4YHQ0FDMnDkTJSUl2vdrn5a6dOkSBg8eDLlcjoiICPz+++8QiUTYsWOHznbT0tIwfPhweHp6omfPnjh27Fidz96xYwc6deoEuVyOmJgYZGZm6ry/evVqtG/fHu7u7ujcuTO++eYbnfdFIhHWrFmDsWPHwsvLC++//z7u3r2LSZMmwc/PDx4eHujYsSPWr19v8DsgcnQMN0QORiKR4MMPP8TKlStx/fr1Jm1r7969uHnzJg4ePIhly5bh3XffxV//+lc0b94cJ06cwPPPP4/nn3++zo/s3LlzMWfOHCQkJGDgwIEYM2YM8vLyAABZWVkYNmwYevXqhdOnT2P37t3IycnB+PHjdbaxceNGSKVSHDlyBF988YXe9q1YsQJLly7Fxx9/jHPnzmHkyJEYM2YMUlNTtZ/VrVs3zJkzB1lZWXj11Vfr3Vdjvrfz589j5MiReOyxx3Du3Dls3rwZhw8fxosvvqh3eZVKhXHjxsHT0xMnTpzAl19+ibfeekvvsm+99RZeffVVJCYmolOnTnjiiSdQWVmpff/evXv44IMPsHHjRhw5cgSFhYWYOHGi9v3t27dj1qxZmDNnDi5cuIDnnnsO06ZNw759+3Q+Z/78+Rg7dizOnz+P6dOn4+2330ZSUhJ++eUXJCcnY/Xq1WjVqlW93xORU7D1bcmJyHhTp04Vxo4dKwiCIPTv31+YPn26IAiCsH37dqHmX+f58+cLPXv21Fn3k08+EcLCwnS2FRYWJiiVSu28zp07C0OGDNG+rqysFLy8vIRNmzYJgiAI6enpAgBh0aJF2mUqKiqEkJAQYfHixYIgCMLbb78txMbG6nx2ZmamAEBISUkRBEEQhg0bJvTq1avB/Q0ODhY++OADnXkPPPCA8K9//Uv7umfPnsL8+fMNbsfY723y5MnCs88+q7PuoUOHBLFYLNy/f18QBEEICwsTPvnkE0EQBOGXX34RpFKpkJWVpV1+z549AgBh+/btgiBUf2dr167VLnPx4kUBgJCcnCwIgiCsX79eACAcP35cu0xycrIAQDhx4oQgCIIwcOBA4Z///KdO2/7+978Lo0eP1r4GILz88ss6y8TFxQnTpk0z+P0QORv23BA5qMWLF2Pjxo1ISkpq9Da6desGsbj6n4GAgAB0795d+1oikaBly5bIzc3VWW/AgAHa51KpFNHR0UhOTgYAxMfHY9++ffD29tZOXbp0AaCuj9GIjo422LbCwkLcvHkTgwYN0pk/aNAg7Wc1hqHvLT4+Hhs2bNBp+8iRI6FSqZCenl5n+ZSUFISGhiIwMFA7r2/fvno/t0ePHtrnQUFBAKDzvWq+R40uXbqgWbNm2n1NTk426ruo/b2+8MIL+P7779GrVy+89tprOHr0qN72ETkThhsiBzV06FCMHDkSb775Zp33xGIxBEHQmaevuNTNzU3ntUgk0jtPpVI12B7NVUcqlQpxcXFITEzUmVJTUzF06FDt8l5eXg1us+Z2NQRBaNKVYYa+N5VKheeee06n3WfPnkVqairat29fZ3lT2lLze635XdWkb1s15xnzXdT+XkeNGoVr167h5Zdfxs2bN/HQQw8ZPH1H5AwYbogc2KJFi/DTTz/V+d+4n58fsrOzdQKOOcemOX78uPZ5ZWUl4uPjtb0zffr0wcWLF9G2bVt06NBBZzI20ACAr68vgoODcfjwYZ35R48eRdeuXZvU/vq+N03ba7e7Q4cOcHd3r7OdLl26ICMjAzk5Odp5p06dalSbKisrcfr0ae3rlJQU5Ofna7/Xrl27Nvq78PPzw1NPPYVvv/0Wy5cvx5dfftmoNhI5CoYbIgfWvXt3TJo0CStXrtSZ/+CDD+LWrVtYsmQJrl69is8//xy//PKL2T73888/x/bt23Hp0iXMmDEDd+/exfTp0wEAM2bMwJ07d/DEE0/g5MmTSEtLw2+//Ybp06dDqVSa9Dlz587F4sWLsXnzZqSkpOCNN95AYmIiZs2a1aT21/e9vf766zh27BhmzJih7W3auXMnXnrpJb3biYmJQfv27TF16lScO3cOR44c0RYUm9q75ObmhpdeegknTpzAmTNnMG3aNPTv3197mmvu3LnYsGED1qxZg9TUVCxbtgzbtm1rsBfmnXfewY8//ogrV67g4sWL+N///tfkcEhk7xhuiBzcv//97zqnoLp27YpVq1bh888/R8+ePXHy5EmznopYtGgRFi9ejJ49e+LQoUP48ccftVfgBAcH48iRI1AqlRg5ciQiIyMxa9YsKBQKnfoeY8ycORNz5szBnDlz0L17d+zevRs7d+5Ex44dm7wP+r63Hj164MCBA0hNTcWQIUPQu3dvvP3229oamdokEgl27NiB4uJiPPDAA3jmmWfwf//3fwDU4+qYwtPTE6+//jqefPJJDBgwAB4eHvj++++1748bNw4rVqzARx99hG7duuGLL77A+vXr8eCDDxrcrru7O+bNm4cePXpg6NChkEgkOtslckYiofbfbiIiarQjR45g8ODBuHLlit46HSKyPIYbIqIm2L59O7y9vdGxY0dcuXIFs2bNQvPmzevUxxCR9Uht3QAiIkdWVFSE1157DZmZmWjVqhUefvhhLF261NbNInJp7LkhIiIip8KCYiIiInIqDDdERETkVBhuiIiIyKkw3BAREZFTYbghIiIip8JwQ0RERE6F4YaIiIicCsMNEREROZX/D26b2S9XzxAUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_acc_scores = []\n",
    "test_acc_scores = []\n",
    "\n",
    "for n in range(1, 20, 2):\n",
    "    knn = KNeighborsClassifier(n_neighbors=n)\n",
    "    knn.fit(X_train, y_train)\n",
    "    train_acc = knn.score(X_train, y_train)\n",
    "    test_acc = knn.score(X_test, y_test)\n",
    "    total_acc = train_acc + test_acc\n",
    "    train_acc_scores.append(train_acc)\n",
    "    test_acc_scores.append(test_acc)\n",
    "    print(f\"k: {n}, Train/Test Accuracy Score: {train_acc:.3f}/{test_acc:.3f}, {total_acc:.3f}\")\n",
    "\n",
    "plt.plot(range(1, 20, 2), train_acc_scores, marker='o')\n",
    "plt.plot(range(1, 20, 2), test_acc_scores, marker=\"x\")\n",
    "plt.xlabel(\"Number of Neighbors\")\n",
    "plt.ylabel(\"Accuracy Score\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "thGPO-y8n5gK"
   },
   "source": [
    "## Now trying with neural networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pl4Kx59PpGHm"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "djZlmHnoorUK",
    "outputId": "cc5b6509-944d-42c4-c148-4c4c0d63ead8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2654, 12) (2654,)\n"
     ]
    }
   ],
   "source": [
    "X = ML_df.drop(\"genre_name\", axis=1)\n",
    "y = ML_df[\"genre_name\"]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Z3B1c53dn9B-"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1sImtFVBoFHC"
   },
   "outputs": [],
   "source": [
    "X_scaler = MinMaxScaler().fit(X_train)\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gO5HIXcroFNI"
   },
   "outputs": [],
   "source": [
    "# Step 1: Label-encode data set\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoder.fit(y_train)\n",
    "encoded_y_train = label_encoder.transform(y_train)\n",
    "encoded_y_test = label_encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HmuXi8nDoFrD"
   },
   "outputs": [],
   "source": [
    "# Step 2: Convert encoded labels to one-hot-encoding\n",
    "y_train_categorical = to_categorical(encoded_y_train)\n",
    "y_test_categorical = to_categorical(encoded_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iiq6zRygoFfG"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EfWnQgjeoFKE"
   },
   "outputs": [],
   "source": [
    "# Create model and add layers\n",
    "model = Sequential()\n",
    "model.add(Dense(units=10, activation='relu', input_dim=12))\n",
    "# model.add(Dense(units=9, activation='sigmoid'))\n",
    "# model.add(Dense(units=9, activation='sigmoid'))\n",
    "# model.add(Dense(units=9, activation='sigmoid'))\n",
    "model.add(Dense(units=10, activation='relu'))\n",
    "model.add(Dense(units=10, activation='relu'))\n",
    "model.add(Dense(units=10, activation='relu'))\n",
    "# model.add(Dense(units=9, activation='relu'))\n",
    "# model.add(Dense(units=9, activation='relu'))\n",
    "model.add(Dense(units=10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QtRppQARoFEP"
   },
   "outputs": [],
   "source": [
    "# Compile and fit the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "colab_type": "code",
    "id": "LEHYVTYeobvj",
    "outputId": "022260d1-9912-4238-a672-34fbf28c9d78"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_32\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_136 (Dense)           (None, 10)                130       \n",
      "                                                                 \n",
      " dense_137 (Dense)           (None, 10)                110       \n",
      "                                                                 \n",
      " dense_138 (Dense)           (None, 10)                110       \n",
      "                                                                 \n",
      " dense_139 (Dense)           (None, 10)                110       \n",
      "                                                                 \n",
      " dense_140 (Dense)           (None, 10)                110       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 570\n",
      "Trainable params: 570\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "nAU-l4BsoblM",
    "outputId": "cb082c72-4502-4729-912f-81b850bf6d8a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "63/63 - 1s - loss: 2.2902 - accuracy: 0.1588 - 775ms/epoch - 12ms/step\n",
      "Epoch 2/10\n",
      "63/63 - 0s - loss: 2.2522 - accuracy: 0.1819 - 75ms/epoch - 1ms/step\n",
      "Epoch 3/10\n",
      "63/63 - 0s - loss: 2.1757 - accuracy: 0.1839 - 74ms/epoch - 1ms/step\n",
      "Epoch 4/10\n",
      "63/63 - 0s - loss: 2.1069 - accuracy: 0.2126 - 67ms/epoch - 1ms/step\n",
      "Epoch 5/10\n",
      "63/63 - 0s - loss: 2.0621 - accuracy: 0.2266 - 67ms/epoch - 1ms/step\n",
      "Epoch 6/10\n",
      "63/63 - 0s - loss: 2.0200 - accuracy: 0.2528 - 75ms/epoch - 1ms/step\n",
      "Epoch 7/10\n",
      "63/63 - 0s - loss: 1.9802 - accuracy: 0.2588 - 75ms/epoch - 1ms/step\n",
      "Epoch 8/10\n",
      "63/63 - 0s - loss: 1.9386 - accuracy: 0.2744 - 82ms/epoch - 1ms/step\n",
      "Epoch 9/10\n",
      "63/63 - 0s - loss: 1.9038 - accuracy: 0.2854 - 67ms/epoch - 1ms/step\n",
      "Epoch 10/10\n",
      "63/63 - 0s - loss: 1.8708 - accuracy: 0.2975 - 58ms/epoch - 922us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x279b099a590>"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    X_train_scaled,\n",
    "    y_train_categorical,\n",
    "    epochs=10,\n",
    "    shuffle=True,\n",
    "    verbose=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "p-ueuOYOon3F",
    "outputId": "0f947f1b-c69b-4082-861f-d0456e1be1e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21/21 - 0s - loss: 1.9141 - accuracy: 0.2967 - 116ms/epoch - 6ms/step\n",
      "Normal Neural Network - Loss: 1.9141441583633423, Accuracy: 0.29668673872947693\n"
     ]
    }
   ],
   "source": [
    "model_loss, model_accuracy = model.evaluate(\n",
    "    X_test_scaled, y_test_categorical, verbose=2)\n",
    "print(\n",
    "    f\"Normal Neural Network - Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AW_xUTylsMgC"
   },
   "source": [
    "## Now trying it with less features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "colab_type": "code",
    "id": "5H9VCuUTqLd8",
    "outputId": "36dc394b-7353-4501-b219-480030e8fdfc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_name</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>loudness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.440</td>\n",
       "      <td>-8.482</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.592</td>\n",
       "      <td>-8.332</td>\n",
       "      <td>0.726</td>\n",
       "      <td>120.018</td>\n",
       "      <td>174253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.801</td>\n",
       "      <td>0.806</td>\n",
       "      <td>-5.206</td>\n",
       "      <td>0.802</td>\n",
       "      <td>106.999</td>\n",
       "      <td>239318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.571</td>\n",
       "      <td>0.544</td>\n",
       "      <td>-10.613</td>\n",
       "      <td>0.216</td>\n",
       "      <td>132.052</td>\n",
       "      <td>132631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.559</td>\n",
       "      <td>0.551</td>\n",
       "      <td>-7.231</td>\n",
       "      <td>0.392</td>\n",
       "      <td>143.008</td>\n",
       "      <td>201800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.369</td>\n",
       "      <td>0.192</td>\n",
       "      <td>-12.151</td>\n",
       "      <td>0.148</td>\n",
       "      <td>175.212</td>\n",
       "      <td>228013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.552</td>\n",
       "      <td>0.702</td>\n",
       "      <td>-5.707</td>\n",
       "      <td>0.564</td>\n",
       "      <td>169.994</td>\n",
       "      <td>178427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.520</td>\n",
       "      <td>-9.342</td>\n",
       "      <td>0.424</td>\n",
       "      <td>153.150</td>\n",
       "      <td>239360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.574</td>\n",
       "      <td>0.469</td>\n",
       "      <td>-8.209</td>\n",
       "      <td>0.288</td>\n",
       "      <td>132.023</td>\n",
       "      <td>242485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.582</td>\n",
       "      <td>0.568</td>\n",
       "      <td>-7.300</td>\n",
       "      <td>0.394</td>\n",
       "      <td>121.991</td>\n",
       "      <td>207541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   genre_name  danceability  energy  loudness  valence    tempo  duration_ms\n",
       "3         Pop         0.817   0.440    -8.482    0.544  142.024       272113\n",
       "4         Pop         0.783   0.592    -8.332    0.726  120.018       174253\n",
       "5         Pop         0.801   0.806    -5.206    0.802  106.999       239318\n",
       "6         Pop         0.571   0.544   -10.613    0.216  132.052       132631\n",
       "7         Pop         0.559   0.551    -7.231    0.392  143.008       201800\n",
       "8         Pop         0.369   0.192   -12.151    0.148  175.212       228013\n",
       "9         Pop         0.552   0.702    -5.707    0.564  169.994       178427\n",
       "10        Pop         0.561   0.520    -9.342    0.424  153.150       239360\n",
       "11        Pop         0.574   0.469    -8.209    0.288  132.023       242485\n",
       "12        Pop         0.582   0.568    -7.300    0.394  121.991       207541"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "droppedML_df = ML_df.drop([\n",
    "                            'key',\n",
    "                            \n",
    "                            'mode', \n",
    "                            'speechiness', \n",
    "                            'acousticness', \n",
    "                            'instrumentalness', \n",
    "                            'liveness', \n",
    "                            ],\n",
    "                           axis=1)\n",
    "\n",
    "# dropped_ML_df()\n",
    "# print(dropped_ML_df)\n",
    "droppedML_df.iloc[3:13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "A_U-erbmsnbh",
    "outputId": "0abbfe02-810e-480b-9d83-7e9a5cc0f75e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2654, 6) (2654,)\n"
     ]
    }
   ],
   "source": [
    "X = droppedML_df.drop(\"genre_name\", axis=1)\n",
    "y = droppedML_df[\"genre_name\"]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DQKgxiFZtHgc"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wq6w4fr3tK57"
   },
   "outputs": [],
   "source": [
    "X_scaler = MinMaxScaler().fit(X_train)\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_K88fvUctM_h"
   },
   "outputs": [],
   "source": [
    "# Step 1: Label-encode data set\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoder.fit(y_train)\n",
    "encoded_y_train = label_encoder.transform(y_train)\n",
    "encoded_y_test = label_encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CJsHPCJttP82"
   },
   "outputs": [],
   "source": [
    "# Step 2: Convert encoded labels to one-hot-encoding\n",
    "y_train_categorical = to_categorical(encoded_y_train)\n",
    "y_test_categorical = to_categorical(encoded_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kE3rx_2Rta-p"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6TXtyHSitbt1"
   },
   "outputs": [],
   "source": [
    "# Create model and add layers\n",
    "model = Sequential()\n",
    "model.add(Dense(units=10, activation='relu', input_dim=6))\n",
    "# model.add(Dense(units=10, activation='sigmoid'))\n",
    "# model.add(Dense(units=10, activation='sigmoid'))\n",
    "# model.add(Dense(units=10, activation='sigmoid'))\n",
    "model.add(Dense(units=10, activation='relu'))\n",
    "# model.add(Dense(units=10, activation='relu'))\n",
    "# model.add(Dense(units=10, activation='relu'))\n",
    "# model.add(Dense(units=10, activation='relu'))\n",
    "# model.add(Dense(units=10, activation='relu'))\n",
    "model.add(Dense(units=10, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1m0aP3wPtbmm"
   },
   "outputs": [],
   "source": [
    "# Compile and fit the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "colab_type": "code",
    "id": "RTDjDJ5puVLk",
    "outputId": "b5fbfeed-091b-4839-83ab-1785ae8789bb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_33\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_141 (Dense)           (None, 10)                70        \n",
      "                                                                 \n",
      " dense_142 (Dense)           (None, 10)                110       \n",
      "                                                                 \n",
      " dense_143 (Dense)           (None, 10)                110       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 290\n",
      "Trainable params: 290\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "cPSXJHltubXb",
    "outputId": "5bfbcbb1-44f2-4fb3-eefb-f9c3af323e4c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "63/63 - 0s - loss: 2.3010 - accuracy: 0.1010 - 463ms/epoch - 7ms/step\n",
      "Epoch 2/10\n",
      "63/63 - 0s - loss: 2.2860 - accuracy: 0.1417 - 80ms/epoch - 1ms/step\n",
      "Epoch 3/10\n",
      "63/63 - 0s - loss: 2.2723 - accuracy: 0.1492 - 58ms/epoch - 916us/step\n",
      "Epoch 4/10\n",
      "63/63 - 0s - loss: 2.2573 - accuracy: 0.1729 - 61ms/epoch - 976us/step\n",
      "Epoch 5/10\n",
      "63/63 - 0s - loss: 2.2439 - accuracy: 0.2060 - 83ms/epoch - 1ms/step\n",
      "Epoch 6/10\n",
      "63/63 - 0s - loss: 2.2289 - accuracy: 0.2176 - 68ms/epoch - 1ms/step\n",
      "Epoch 7/10\n",
      "63/63 - 0s - loss: 2.2099 - accuracy: 0.2286 - 67ms/epoch - 1ms/step\n",
      "Epoch 8/10\n",
      "63/63 - 0s - loss: 2.1880 - accuracy: 0.2447 - 66ms/epoch - 1ms/step\n",
      "Epoch 9/10\n",
      "63/63 - 0s - loss: 2.1673 - accuracy: 0.2482 - 73ms/epoch - 1ms/step\n",
      "Epoch 10/10\n",
      "63/63 - 0s - loss: 2.1451 - accuracy: 0.2472 - 77ms/epoch - 1ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x279b0e69850>"
      ]
     },
     "execution_count": 483,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    X_train_scaled,\n",
    "    y_train_categorical,\n",
    "    epochs=10,\n",
    "    shuffle=True,\n",
    "    verbose=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "ZtxSc_jIuXWY",
    "outputId": "1ca7d664-8744-430d-b402-8492f53e8fc2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21/21 - 0s - loss: 2.1814 - accuracy: 0.2395 - 115ms/epoch - 5ms/step\n",
      "Normal Neural Network - Loss: 2.1813647747039795, Accuracy: 0.23945783078670502\n"
     ]
    }
   ],
   "source": [
    "model_loss, model_accuracy = model.evaluate(\n",
    "    X_test_scaled, y_test_categorical, verbose=2)\n",
    "print(\n",
    "    f\"Normal Neural Network - Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G8mRbgI7xFoI"
   },
   "source": [
    "Changing Genre Dataframe Changed - Considering Top 7Grammy Genres "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 434
    },
    "colab_type": "code",
    "id": "VO1b3B8VRBXI",
    "outputId": "a3a7e43e-9051-411d-ef9e-182c68e40a25"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>artist_name</th>\n",
       "      <th>track_name</th>\n",
       "      <th>track_id</th>\n",
       "      <th>artist_href</th>\n",
       "      <th>track_href</th>\n",
       "      <th>genre_name</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>miley cyrus</td>\n",
       "      <td>flowers</td>\n",
       "      <td>0yLdNVWF3Srea0uzk55zFn</td>\n",
       "      <td>https://open.spotify.com/artist/5YGY8feqx7naU7...</td>\n",
       "      <td>https://open.spotify.com/track/0yLdNVWF3Srea0u...</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.325</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0668</td>\n",
       "      <td>0.0632</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>0.646</td>\n",
       "      <td>117.999</td>\n",
       "      <td>200455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sza</td>\n",
       "      <td>kill bill</td>\n",
       "      <td>1Qrg8KqiBpW07V7PNxwwwL</td>\n",
       "      <td>https://open.spotify.com/artist/7tYKF4w9nC0nq9...</td>\n",
       "      <td>https://open.spotify.com/track/1Qrg8KqiBpW07V7...</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.644</td>\n",
       "      <td>0.735</td>\n",
       "      <td>8</td>\n",
       "      <td>-5.747</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>0.0521</td>\n",
       "      <td>0.144000</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.418</td>\n",
       "      <td>88.980</td>\n",
       "      <td>153947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>taylor swift</td>\n",
       "      <td>anti-hero</td>\n",
       "      <td>0V3wPSX9ygBnCm8psDIegu</td>\n",
       "      <td>https://open.spotify.com/artist/06HL4z0CvFAxyc...</td>\n",
       "      <td>https://open.spotify.com/track/0V3wPSX9ygBnCm8...</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.643</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.571</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.1420</td>\n",
       "      <td>0.533</td>\n",
       "      <td>97.008</td>\n",
       "      <td>200690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>drake</td>\n",
       "      <td>search &amp; rescue</td>\n",
       "      <td>7aRCf5cLOFN1U7kvtChY1G</td>\n",
       "      <td>https://open.spotify.com/artist/3TVXtAsR1Inumw...</td>\n",
       "      <td>https://open.spotify.com/track/7aRCf5cLOFN1U7k...</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.482</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fifty fifty</td>\n",
       "      <td>cupid - twin ver.</td>\n",
       "      <td>7FbrGaHYVDmfr7KoLIZnQ7</td>\n",
       "      <td>https://open.spotify.com/artist/4GJ6xDCF5jaUqD...</td>\n",
       "      <td>https://open.spotify.com/track/7FbrGaHYVDmfr7K...</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.592</td>\n",
       "      <td>11</td>\n",
       "      <td>-8.332</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>0.4350</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.3470</td>\n",
       "      <td>0.726</td>\n",
       "      <td>120.018</td>\n",
       "      <td>174253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    artist_name         track_name                track_id   \n",
       "0   miley cyrus            flowers  0yLdNVWF3Srea0uzk55zFn  \\\n",
       "1           sza          kill bill  1Qrg8KqiBpW07V7PNxwwwL   \n",
       "2  taylor swift          anti-hero  0V3wPSX9ygBnCm8psDIegu   \n",
       "3         drake    search & rescue  7aRCf5cLOFN1U7kvtChY1G   \n",
       "4   fifty fifty  cupid - twin ver.  7FbrGaHYVDmfr7KoLIZnQ7   \n",
       "\n",
       "                                         artist_href   \n",
       "0  https://open.spotify.com/artist/5YGY8feqx7naU7...  \\\n",
       "1  https://open.spotify.com/artist/7tYKF4w9nC0nq9...   \n",
       "2  https://open.spotify.com/artist/06HL4z0CvFAxyc...   \n",
       "3  https://open.spotify.com/artist/3TVXtAsR1Inumw...   \n",
       "4  https://open.spotify.com/artist/4GJ6xDCF5jaUqD...   \n",
       "\n",
       "                                          track_href genre_name  danceability   \n",
       "0  https://open.spotify.com/track/0yLdNVWF3Srea0u...        Pop         0.707  \\\n",
       "1  https://open.spotify.com/track/1Qrg8KqiBpW07V7...        Pop         0.644   \n",
       "2  https://open.spotify.com/track/0V3wPSX9ygBnCm8...        Pop         0.637   \n",
       "3  https://open.spotify.com/track/7aRCf5cLOFN1U7k...        Pop         0.817   \n",
       "4  https://open.spotify.com/track/7FbrGaHYVDmfr7K...        Pop         0.783   \n",
       "\n",
       "   energy  key  loudness  mode  speechiness  acousticness  instrumentalness   \n",
       "0   0.681    0    -4.325     1       0.0668        0.0632          0.000005  \\\n",
       "1   0.735    8    -5.747     1       0.0391        0.0521          0.144000   \n",
       "2   0.643    4    -6.571     1       0.0519        0.1300          0.000002   \n",
       "3   0.440   10    -8.482     0       0.0734        0.0603          0.000001   \n",
       "4   0.592   11    -8.332     0       0.0331        0.4350          0.000004   \n",
       "\n",
       "   liveness  valence    tempo  duration_ms  \n",
       "0    0.0322    0.646  117.999       200455  \n",
       "1    0.1610    0.418   88.980       153947  \n",
       "2    0.1420    0.533   97.008       200690  \n",
       "3    0.3300    0.544  142.024       272113  \n",
       "4    0.3470    0.726  120.018       174253  "
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join(\"Downloads/spotify_df_Top7GrammyGenre.csv\"))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "A2Zv2DETxOfa",
    "outputId": "f0c580a7-e86e-4c4c-a851-b2e792487178"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_name</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.325</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0668</td>\n",
       "      <td>0.063200</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.0322</td>\n",
       "      <td>0.646</td>\n",
       "      <td>117.999</td>\n",
       "      <td>200455</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.644</td>\n",
       "      <td>0.735</td>\n",
       "      <td>8</td>\n",
       "      <td>-5.747</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>0.052100</td>\n",
       "      <td>0.144000</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.418</td>\n",
       "      <td>88.980</td>\n",
       "      <td>153947</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.643</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.571</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.1420</td>\n",
       "      <td>0.533</td>\n",
       "      <td>97.008</td>\n",
       "      <td>200690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.817</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.482</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.060300</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pop</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.592</td>\n",
       "      <td>11</td>\n",
       "      <td>-8.332</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>0.435000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.3470</td>\n",
       "      <td>0.726</td>\n",
       "      <td>120.018</td>\n",
       "      <td>174253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1832</th>\n",
       "      <td>New Age</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.796</td>\n",
       "      <td>7</td>\n",
       "      <td>-6.879</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0352</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.3050</td>\n",
       "      <td>0.426</td>\n",
       "      <td>179.379</td>\n",
       "      <td>179240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1833</th>\n",
       "      <td>New Age</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.740</td>\n",
       "      <td>5</td>\n",
       "      <td>-12.128</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.191000</td>\n",
       "      <td>0.969000</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.102</td>\n",
       "      <td>123.997</td>\n",
       "      <td>379083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1834</th>\n",
       "      <td>New Age</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.501</td>\n",
       "      <td>5</td>\n",
       "      <td>-14.892</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9330</td>\n",
       "      <td>0.793000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.322</td>\n",
       "      <td>64.438</td>\n",
       "      <td>152125</td>\n",
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       "    <tr>\n",
       "      <th>1835</th>\n",
       "      <td>New Age</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.409</td>\n",
       "      <td>5</td>\n",
       "      <td>-14.871</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0310</td>\n",
       "      <td>0.394000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.0633</td>\n",
       "      <td>0.141</td>\n",
       "      <td>97.188</td>\n",
       "      <td>354667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1836</th>\n",
       "      <td>New Age</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.926</td>\n",
       "      <td>10</td>\n",
       "      <td>-13.634</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0441</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.879000</td>\n",
       "      <td>0.0471</td>\n",
       "      <td>0.942</td>\n",
       "      <td>131.230</td>\n",
       "      <td>305333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1837 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     genre_name  danceability  energy  key  loudness  mode  speechiness   \n",
       "0           Pop         0.707   0.681    0    -4.325     1       0.0668  \\\n",
       "1           Pop         0.644   0.735    8    -5.747     1       0.0391   \n",
       "2           Pop         0.637   0.643    4    -6.571     1       0.0519   \n",
       "3           Pop         0.817   0.440   10    -8.482     0       0.0734   \n",
       "4           Pop         0.783   0.592   11    -8.332     0       0.0331   \n",
       "...         ...           ...     ...  ...       ...   ...          ...   \n",
       "1832    New Age         0.352   0.796    7    -6.879     1       0.0352   \n",
       "1833    New Age         0.603   0.740    5   -12.128     0       0.0358   \n",
       "1834    New Age         0.555   0.501    5   -14.892     1       0.9330   \n",
       "1835    New Age         0.603   0.409    5   -14.871     1       0.0310   \n",
       "1836    New Age         0.631   0.926   10   -13.634     0       0.0441   \n",
       "\n",
       "      acousticness  instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "0         0.063200          0.000005    0.0322    0.646  117.999       200455  \n",
       "1         0.052100          0.144000    0.1610    0.418   88.980       153947  \n",
       "2         0.130000          0.000002    0.1420    0.533   97.008       200690  \n",
       "3         0.060300          0.000001    0.3300    0.544  142.024       272113  \n",
       "4         0.435000          0.000004    0.3470    0.726  120.018       174253  \n",
       "...            ...               ...       ...      ...      ...          ...  \n",
       "1832      0.000255          0.000058    0.3050    0.426  179.379       179240  \n",
       "1833      0.191000          0.969000    0.1100    0.102  123.997       379083  \n",
       "1834      0.793000          0.000000    0.9100    0.322   64.438       152125  \n",
       "1835      0.394000          0.490000    0.0633    0.141   97.188       354667  \n",
       "1836      0.000021          0.879000    0.0471    0.942  131.230       305333  \n",
       "\n",
       "[1837 rows x 13 columns]"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ML_df = df.drop(['artist_name', 'track_name', 'track_id', 'artist_href', 'track_href'], axis=1)\n",
    "ML_df = ML_df.reset_index(drop=True)\n",
    "\n",
    "ML_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 372
    },
    "colab_type": "code",
    "id": "Rsw2oCcJ1sdl",
    "outputId": "7a7699e8-ffce-40fc-d8cb-f1e1b1e63cf2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1837 entries, 0 to 1836\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   genre_name        1837 non-null   object \n",
      " 1   danceability      1837 non-null   float64\n",
      " 2   energy            1837 non-null   float64\n",
      " 3   key               1837 non-null   int64  \n",
      " 4   loudness          1837 non-null   float64\n",
      " 5   mode              1837 non-null   int64  \n",
      " 6   speechiness       1837 non-null   float64\n",
      " 7   acousticness      1837 non-null   float64\n",
      " 8   instrumentalness  1837 non-null   float64\n",
      " 9   liveness          1837 non-null   float64\n",
      " 10  valence           1837 non-null   float64\n",
      " 11  tempo             1837 non-null   float64\n",
      " 12  duration_ms       1837 non-null   int64  \n",
      "dtypes: float64(9), int64(3), object(1)\n",
      "memory usage: 186.7+ KB\n"
     ]
    }
   ],
   "source": [
    "ML_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Db7nt_xpxfwQ"
   },
   "source": [
    "## Now trying with KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "colab_type": "code",
    "id": "Q_PDTooPxhWf",
    "outputId": "ca01437c-d851-4379-cde7-a4947a98e41e"
   },
   "outputs": [
    {
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       "      <td>-8.482</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.06030</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.783</td>\n",
       "      <td>0.592</td>\n",
       "      <td>11</td>\n",
       "      <td>-8.332</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>0.43500</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.3470</td>\n",
       "      <td>0.726</td>\n",
       "      <td>120.018</td>\n",
       "      <td>174253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.726</td>\n",
       "      <td>0.698</td>\n",
       "      <td>5</td>\n",
       "      <td>-4.165</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0431</td>\n",
       "      <td>0.04900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.3090</td>\n",
       "      <td>0.867</td>\n",
       "      <td>143.994</td>\n",
       "      <td>233478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.509</td>\n",
       "      <td>0.637</td>\n",
       "      <td>4</td>\n",
       "      <td>-4.830</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0471</td>\n",
       "      <td>0.07170</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.0789</td>\n",
       "      <td>0.378</td>\n",
       "      <td>172.006</td>\n",
       "      <td>224000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.696</td>\n",
       "      <td>0.559</td>\n",
       "      <td>7</td>\n",
       "      <td>-9.190</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0693</td>\n",
       "      <td>0.06180</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.0887</td>\n",
       "      <td>0.433</td>\n",
       "      <td>163.999</td>\n",
       "      <td>194166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.609</td>\n",
       "      <td>0.692</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.958</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0259</td>\n",
       "      <td>0.01570</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0864</td>\n",
       "      <td>0.454</td>\n",
       "      <td>116.959</td>\n",
       "      <td>233720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.474</td>\n",
       "      <td>0.921</td>\n",
       "      <td>9</td>\n",
       "      <td>-10.431</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0457</td>\n",
       "      <td>0.00491</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.4760</td>\n",
       "      <td>0.696</td>\n",
       "      <td>93.010</td>\n",
       "      <td>122339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    danceability  energy  key  loudness  mode  speechiness  acousticness   \n",
       "0          0.707   0.681    0    -4.325     1       0.0668       0.06320  \\\n",
       "1          0.644   0.735    8    -5.747     1       0.0391       0.05210   \n",
       "2          0.637   0.643    4    -6.571     1       0.0519       0.13000   \n",
       "3          0.817   0.440   10    -8.482     0       0.0734       0.06030   \n",
       "4          0.783   0.592   11    -8.332     0       0.0331       0.43500   \n",
       "..           ...     ...  ...       ...   ...          ...           ...   \n",
       "95         0.726   0.698    5    -4.165     1       0.0431       0.04900   \n",
       "96         0.509   0.637    4    -4.830     0       0.0471       0.07170   \n",
       "97         0.696   0.559    7    -9.190     1       0.0693       0.06180   \n",
       "98         0.609   0.692    1    -2.958     1       0.0259       0.01570   \n",
       "99         0.474   0.921    9   -10.431     0       0.0457       0.00491   \n",
       "\n",
       "    instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "0           0.000005    0.0322    0.646  117.999       200455  \n",
       "1           0.144000    0.1610    0.418   88.980       153947  \n",
       "2           0.000002    0.1420    0.533   97.008       200690  \n",
       "3           0.000001    0.3300    0.544  142.024       272113  \n",
       "4           0.000004    0.3470    0.726  120.018       174253  \n",
       "..               ...       ...      ...      ...          ...  \n",
       "95          0.000000    0.3090    0.867  143.994       233478  \n",
       "96          0.000011    0.0789    0.378  172.006       224000  \n",
       "97          0.000056    0.0887    0.433  163.999       194166  \n",
       "98          0.000000    0.0864    0.454  116.959       233720  \n",
       "99          0.850000    0.4760    0.696   93.010       122339  \n",
       "\n",
       "[100 rows x 12 columns]"
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = ML_df[\"genre_name\"]\n",
    "X = ML_df.drop(\"genre_name\", axis=1)\n",
    "X.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "colab_type": "code",
    "id": "hs54HyZuxjMG",
    "outputId": "df9d0a3b-5881-4f71-a814-d040f14de21b"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>0.760</td>\n",
       "      <td>0.479</td>\n",
       "      <td>2</td>\n",
       "      <td>-5.574</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0466</td>\n",
       "      <td>0.55600</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0703</td>\n",
       "      <td>0.9130</td>\n",
       "      <td>89.911</td>\n",
       "      <td>158040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1060</th>\n",
       "      <td>0.633</td>\n",
       "      <td>0.548</td>\n",
       "      <td>5</td>\n",
       "      <td>-5.634</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0467</td>\n",
       "      <td>0.26900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>0.3500</td>\n",
       "      <td>160.011</td>\n",
       "      <td>192000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>0.553</td>\n",
       "      <td>0.587</td>\n",
       "      <td>5</td>\n",
       "      <td>-6.829</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0277</td>\n",
       "      <td>0.34500</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.2560</td>\n",
       "      <td>0.3460</td>\n",
       "      <td>90.018</td>\n",
       "      <td>242293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>0.901</td>\n",
       "      <td>0.464</td>\n",
       "      <td>5</td>\n",
       "      <td>-9.789</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0645</td>\n",
       "      <td>0.36800</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.2380</td>\n",
       "      <td>0.6380</td>\n",
       "      <td>109.004</td>\n",
       "      <td>203267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>0.470</td>\n",
       "      <td>0.809</td>\n",
       "      <td>8</td>\n",
       "      <td>-6.740</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0495</td>\n",
       "      <td>0.27200</td>\n",
       "      <td>0.006710</td>\n",
       "      <td>0.0491</td>\n",
       "      <td>0.4900</td>\n",
       "      <td>151.836</td>\n",
       "      <td>363240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>0.720</td>\n",
       "      <td>0.978</td>\n",
       "      <td>9</td>\n",
       "      <td>-4.495</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0595</td>\n",
       "      <td>0.00147</td>\n",
       "      <td>0.819000</td>\n",
       "      <td>0.0283</td>\n",
       "      <td>0.1960</td>\n",
       "      <td>127.984</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>0.451</td>\n",
       "      <td>0.962</td>\n",
       "      <td>0</td>\n",
       "      <td>-3.947</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.01880</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.2770</td>\n",
       "      <td>0.3770</td>\n",
       "      <td>131.435</td>\n",
       "      <td>323707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1610</th>\n",
       "      <td>0.139</td>\n",
       "      <td>0.331</td>\n",
       "      <td>4</td>\n",
       "      <td>-13.226</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0372</td>\n",
       "      <td>0.56900</td>\n",
       "      <td>0.876000</td>\n",
       "      <td>0.0959</td>\n",
       "      <td>0.0357</td>\n",
       "      <td>67.393</td>\n",
       "      <td>170333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1581</th>\n",
       "      <td>0.380</td>\n",
       "      <td>0.835</td>\n",
       "      <td>11</td>\n",
       "      <td>-9.627</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0296</td>\n",
       "      <td>0.19200</td>\n",
       "      <td>0.837000</td>\n",
       "      <td>0.0965</td>\n",
       "      <td>0.7430</td>\n",
       "      <td>133.849</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>0.528</td>\n",
       "      <td>0.782</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.170</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0314</td>\n",
       "      <td>0.00928</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.1790</td>\n",
       "      <td>0.7190</td>\n",
       "      <td>120.311</td>\n",
       "      <td>276508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1377 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      danceability  energy  key  loudness  mode  speechiness  acousticness   \n",
       "936          0.760   0.479    2    -5.574     1       0.0466       0.55600  \\\n",
       "1060         0.633   0.548    5    -5.634     0       0.0467       0.26900   \n",
       "111          0.553   0.587    5    -6.829     1       0.0277       0.34500   \n",
       "939          0.901   0.464    5    -9.789     0       0.0645       0.36800   \n",
       "613          0.470   0.809    8    -6.740     1       0.0495       0.27200   \n",
       "...            ...     ...  ...       ...   ...          ...           ...   \n",
       "342          0.720   0.978    9    -4.495     0       0.0595       0.00147   \n",
       "716          0.451   0.962    0    -3.947     1       0.0734       0.01880   \n",
       "1610         0.139   0.331    4   -13.226     0       0.0372       0.56900   \n",
       "1581         0.380   0.835   11    -9.627     0       0.0296       0.19200   \n",
       "464          0.528   0.782    4    -6.170     0       0.0314       0.00928   \n",
       "\n",
       "      instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "936           0.000000    0.0703   0.9130   89.911       158040  \n",
       "1060          0.000000    0.1600   0.3500  160.011       192000  \n",
       "111           0.100000    0.2560   0.3460   90.018       242293  \n",
       "939           0.000017    0.2380   0.6380  109.004       203267  \n",
       "613           0.006710    0.0491   0.4900  151.836       363240  \n",
       "...                ...       ...      ...      ...          ...  \n",
       "342           0.819000    0.0283   0.1960  127.984       210000  \n",
       "716           0.000003    0.2770   0.3770  131.435       323707  \n",
       "1610          0.876000    0.0959   0.0357   67.393       170333  \n",
       "1581          0.837000    0.0965   0.7430  133.849       300000  \n",
       "464           0.000022    0.1790   0.7190  120.311       276508  \n",
       "\n",
       "[1377 rows x 12 columns]"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ELupJQM4d0DU"
   },
   "source": [
    "### Baseline Accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PtdXpQfycXgK"
   },
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "dummy_clf = DummyClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 90
    },
    "colab_type": "code",
    "id": "DHG-P_4ycXpK",
    "outputId": "e5124270-9a98-412e-9aa5-824c331cb0e9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-17 {color: black;background-color: white;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-17 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-17 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DummyClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DummyClassifier</label><div class=\"sk-toggleable__content\"><pre>DummyClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DummyClassifier()"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "aSSVyvG4cXW5",
    "outputId": "46dc6942-1be8-4d72-f44d-5ffd248a7c75"
   },
   "outputs": [],
   "source": [
    "y_pred = dummy_clf.predict(X_test)\n",
    "# print(y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "14zFL4tXciJ2",
    "outputId": "61a3a31c-bc01-48ca-e55d-d3d9663f394a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13695652173913042\n"
     ]
    }
   ],
   "source": [
    "dummy_score = dummy_clf.score(X_test, y_test)\n",
    "print(dummy_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "SZ82ExY5wTn1",
    "outputId": "67f0d562-8fad-439b-e62f-8d5c8b4454c1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-18 {color: black;background-color: white;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-18 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-18 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Creating a StandardScater model and fit it to the training data\n",
    "X_scaler = StandardScaler().fit(X_train)\n",
    "X_scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 426
    },
    "colab_type": "code",
    "id": "ajL-L9qKXooa",
    "outputId": "bf27a209-68c1-48f1-c1c7-008ac2a87e28"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.698</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0</td>\n",
       "      <td>-7.234</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0559</td>\n",
       "      <td>0.42500</td>\n",
       "      <td>0.003140</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.335</td>\n",
       "      <td>145.044</td>\n",
       "      <td>181080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>0.575</td>\n",
       "      <td>0.568</td>\n",
       "      <td>9</td>\n",
       "      <td>-5.509</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>0.04840</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.370</td>\n",
       "      <td>93.986</td>\n",
       "      <td>206280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>0.825</td>\n",
       "      <td>0.572</td>\n",
       "      <td>6</td>\n",
       "      <td>-6.067</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0864</td>\n",
       "      <td>0.00790</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.568</td>\n",
       "      <td>141.991</td>\n",
       "      <td>175954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>0.774</td>\n",
       "      <td>0.794</td>\n",
       "      <td>5</td>\n",
       "      <td>-4.446</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.00824</td>\n",
       "      <td>0.003170</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.724</td>\n",
       "      <td>120.002</td>\n",
       "      <td>347880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>0.684</td>\n",
       "      <td>0.671</td>\n",
       "      <td>7</td>\n",
       "      <td>-4.392</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0464</td>\n",
       "      <td>0.18900</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.156</td>\n",
       "      <td>88.048</td>\n",
       "      <td>199920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.559</td>\n",
       "      <td>0.551</td>\n",
       "      <td>5</td>\n",
       "      <td>-7.231</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1320</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.392</td>\n",
       "      <td>143.008</td>\n",
       "      <td>201800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>0.558</td>\n",
       "      <td>0.559</td>\n",
       "      <td>6</td>\n",
       "      <td>-9.222</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0959</td>\n",
       "      <td>0.37100</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.620</td>\n",
       "      <td>78.558</td>\n",
       "      <td>180387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1652</th>\n",
       "      <td>0.702</td>\n",
       "      <td>0.915</td>\n",
       "      <td>3</td>\n",
       "      <td>-6.187</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2010</td>\n",
       "      <td>0.03750</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.722</td>\n",
       "      <td>90.022</td>\n",
       "      <td>176592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537</th>\n",
       "      <td>0.661</td>\n",
       "      <td>0.633</td>\n",
       "      <td>7</td>\n",
       "      <td>-5.986</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0348</td>\n",
       "      <td>0.03800</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.613</td>\n",
       "      <td>81.984</td>\n",
       "      <td>209760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>0.625</td>\n",
       "      <td>0.576</td>\n",
       "      <td>11</td>\n",
       "      <td>-5.693</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1750</td>\n",
       "      <td>0.05430</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.235</td>\n",
       "      <td>179.063</td>\n",
       "      <td>193147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>460 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      danceability  energy  key  loudness  mode  speechiness  acousticness   \n",
       "20           0.698   0.546    0    -7.234     0       0.0559       0.42500  \\\n",
       "229          0.575   0.568    9    -5.509     0       0.0300       0.04840   \n",
       "1003         0.825   0.572    6    -6.067     0       0.0864       0.00790   \n",
       "436          0.774   0.794    5    -4.446     0       0.0358       0.00824   \n",
       "865          0.684   0.671    7    -4.392     0       0.0464       0.18900   \n",
       "...            ...     ...  ...       ...   ...          ...           ...   \n",
       "7            0.559   0.551    5    -7.231     1       0.1320       0.14100   \n",
       "838          0.558   0.559    6    -9.222     1       0.0959       0.37100   \n",
       "1652         0.702   0.915    3    -6.187     0       0.2010       0.03750   \n",
       "1537         0.661   0.633    7    -5.986     1       0.0348       0.03800   \n",
       "1136         0.625   0.576   11    -5.693     0       0.1750       0.05430   \n",
       "\n",
       "      instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "20            0.003140     0.160    0.335  145.044       181080  \n",
       "229           0.000417     0.286    0.370   93.986       206280  \n",
       "1003          0.000000     0.113    0.568  141.991       175954  \n",
       "436           0.003170     0.080    0.724  120.002       347880  \n",
       "865           0.000024     0.122    0.156   88.048       199920  \n",
       "...                ...       ...      ...      ...          ...  \n",
       "7             0.000000     0.110    0.392  143.008       201800  \n",
       "838           0.000007     0.109    0.620   78.558       180387  \n",
       "1652          0.000002     0.059    0.722   90.022       176592  \n",
       "1537          0.000003     0.124    0.613   81.984       209760  \n",
       "1136          0.000041     0.187    0.235  179.063       193147  \n",
       "\n",
       "[460 rows x 12 columns]"
      ]
     },
     "execution_count": 498,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "p9D36CfmwSRf",
    "outputId": "16f6c012-5905-49ea-e337-a83660db86d8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.86270755,  1.07231145,  1.33160906,  0.81931649, -1.38692714,\n",
       "       -0.55912542, -0.68932919, -0.07061185, -0.38835896, -0.09233465,\n",
       "       -1.25278035,  0.13690747])"
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)\n",
    "X_test_scaled[11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 545
    },
    "colab_type": "code",
    "id": "lLaaHxlgxnZ6",
    "outputId": "56280f7c-ef38-4d01-dc1c-4fd199c42662"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k: 1, Train/Test Accuracy: 1.000/0.183, 1.183\n",
      "k: 3, Train/Test Accuracy: 0.500/0.159, 0.658\n",
      "k: 5, Train/Test Accuracy: 0.426/0.200, 0.626\n",
      "k: 7, Train/Test Accuracy: 0.386/0.222, 0.607\n",
      "k: 9, Train/Test Accuracy: 0.363/0.222, 0.585\n",
      "k: 11, Train/Test Accuracy: 0.351/0.222, 0.573\n",
      "k: 13, Train/Test Accuracy: 0.344/0.217, 0.562\n",
      "k: 15, Train/Test Accuracy: 0.335/0.237, 0.572\n",
      "k: 17, Train/Test Accuracy: 0.330/0.228, 0.558\n",
      "k: 19, Train/Test Accuracy: 0.325/0.222, 0.546\n",
      "k: 21, Train/Test Accuracy: 0.314/0.200, 0.514\n",
      "k: 23, Train/Test Accuracy: 0.300/0.226, 0.526\n",
      "k: 25, Train/Test Accuracy: 0.303/0.226, 0.529\n",
      "k: 27, Train/Test Accuracy: 0.300/0.220, 0.519\n",
      "k: 29, Train/Test Accuracy: 0.301/0.228, 0.529\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_accs = []\n",
    "test_accs = []\n",
    "\n",
    "for n in range(1, 30, 2):\n",
    "    knn = KNeighborsClassifier(n_neighbors=n)\n",
    "    knn.fit(X_train, y_train)\n",
    "    train_acc = knn.score(X_train, y_train)\n",
    "    test_acc = knn.score(X_test, y_test)\n",
    "    avg_acc = train_acc + test_acc\n",
    "    train_accs.append(train_acc)\n",
    "    test_accs.append(test_acc)\n",
    "    print(f\"k: {n}, Train/Test Accuracy: {train_acc:.3f}/{test_acc:.3f}, {avg_acc:.3f}\")\n",
    "\n",
    "plt.plot(range(1, 30, 2), train_accs, marker='o')\n",
    "plt.plot(range(1, 30, 2), test_accs, marker='x')\n",
    "plt.xlabel(\"Number of neighbors (n)\")\n",
    "plt.ylabel(\"Accuracy Score\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "creating a KNN (k-Nearest Neighbors) classifier with k=19, fitting the classifier on the training data, and then calculating the accuracy score on the test data.  then defining several new data points and predicting their classes using the trained classifier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "hQ_f7hk2wK15",
    "outputId": "4a9d83a9-b2f2-4e45-f351-005dc4b5e92a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k=19 Test Acc: 0.435\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=19)\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "print('k=19 Test Acc: %.3f' % knn.score(X_test_scaled, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6rxEQR54XXm3"
   },
   "outputs": [],
   "source": [
    "snew_data = 0.2, 0.2, 10, -30, 1, 0.05, 0.8, 0.8, 0.5, 0.2, 0.1, 222222\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "6IyaQn39KVto",
    "outputId": "54cd6505-9b40-4c1f-ff02-6af908d75eef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Rap' 'Dance/Electronic' 'Rap' 'Dance/Electronic' 'Dance/Electronic'\n",
      " 'Dance/Electronic' 'New Age' 'Country' 'New Age' 'Pop' 'Country' 'Rock'\n",
      " 'Rap' 'Country' 'Dance/Electronic' 'R&B' 'New Age' 'Pop' 'Country'\n",
      " 'Country' 'Dance/Electronic' 'Country' 'Dance/Electronic'\n",
      " 'Dance/Electronic' 'Country' 'Rap' 'Pop' 'Pop' 'Country' 'Pop' 'Pop'\n",
      " 'Rock' 'R&B' 'Country' 'Pop' 'Dance/Electronic' 'Dance/Electronic'\n",
      " 'Dance/Electronic' 'Pop' 'Rap' 'Rock' 'Rap' 'Rap' 'Rock'\n",
      " 'Dance/Electronic' 'Rap' 'Rap' 'Country' 'New Age' 'New Age' 'Rock'\n",
      " 'Rock' 'Rock' 'Rap' 'Dance/Electronic' 'Dance/Electronic' 'Country'\n",
      " 'New Age' 'New Age' 'Country' 'Country' 'Country' 'Rap' 'Pop' 'Pop' 'Pop'\n",
      " 'Rock' 'Pop' 'Rock' 'Rock' 'New Age' 'Country' 'Country' 'Rap' 'Rap'\n",
      " 'Country' 'Country' 'Country' 'New Age' 'Country' 'Dance/Electronic'\n",
      " 'Country' 'New Age' 'R&B' 'Dance/Electronic' 'Country' 'New Age'\n",
      " 'Dance/Electronic' 'Rap' 'Pop' 'Country' 'Country' 'Rap' 'Country' 'Rock'\n",
      " 'Rock' 'Dance/Electronic' 'Dance/Electronic' 'Rock' 'Pop' 'Rap' 'R&B'\n",
      " 'Pop' 'Dance/Electronic' 'Dance/Electronic' 'Country' 'Pop' 'Country'\n",
      " 'Rock' 'Rock' 'Rap' 'Country' 'New Age' 'Dance/Electronic'\n",
      " 'Dance/Electronic' 'Country' 'Dance/Electronic' 'R&B' 'Country' 'Rock'\n",
      " 'R&B' 'R&B' 'Country' 'Country' 'Country' 'Rock' 'New Age' 'New Age'\n",
      " 'Country' 'Country' 'Country' 'Dance/Electronic' 'R&B' 'Country'\n",
      " 'Country' 'Country' 'Rap' 'R&B' 'New Age' 'Dance/Electronic' 'New Age'\n",
      " 'Pop' 'New Age' 'New Age' 'Dance/Electronic' 'Dance/Electronic' 'Country'\n",
      " 'Pop' 'Pop' 'Pop' 'R&B' 'Rap' 'Dance/Electronic' 'Country' 'Country'\n",
      " 'Pop' 'Country' 'Pop' 'Rap' 'Rock' 'Pop' 'Rock' 'New Age' 'Country' 'Pop'\n",
      " 'Country' 'Pop' 'Country' 'Rock' 'Country' 'Country' 'Pop' 'Pop'\n",
      " 'Country' 'Rap' 'Country' 'Country' 'Country' 'Rock' 'Pop' 'Country'\n",
      " 'Country' 'R&B' 'Dance/Electronic' 'Country' 'R&B' 'Rap' 'Country'\n",
      " 'Country' 'Country' 'Country' 'Dance/Electronic' 'Dance/Electronic'\n",
      " 'Country' 'Country' 'Country' 'Rap' 'Dance/Electronic' 'Country' 'R&B'\n",
      " 'Country' 'Country' 'Rap' 'New Age' 'Pop' 'R&B' 'Pop' 'New Age' 'New Age'\n",
      " 'Rock' 'Rock' 'Country' 'Dance/Electronic' 'Dance/Electronic' 'Country'\n",
      " 'Rock' 'R&B' 'Rock' 'Country' 'Country' 'Country' 'R&B' 'R&B' 'Country'\n",
      " 'Rap' 'Rock' 'New Age' 'R&B' 'Rap' 'Pop' 'Rock' 'Dance/Electronic' 'Rap'\n",
      " 'Pop' 'Dance/Electronic' 'Pop' 'Dance/Electronic' 'Country' 'Country'\n",
      " 'New Age' 'Rap' 'Dance/Electronic' 'Dance/Electronic' 'Rock' 'Country'\n",
      " 'R&B' 'Rap' 'R&B' 'New Age' 'Rap' 'R&B' 'Rap' 'Country' 'Pop' 'Rock'\n",
      " 'Dance/Electronic' 'Country' 'Country' 'Pop' 'R&B' 'Country'\n",
      " 'Dance/Electronic' 'Dance/Electronic' 'Rock' 'Pop' 'Country' 'New Age'\n",
      " 'Rap' 'New Age' 'Pop' 'Country' 'Dance/Electronic' 'Country'\n",
      " 'Dance/Electronic' 'New Age' 'Pop' 'Dance/Electronic' 'Rap' 'Rap'\n",
      " 'New Age' 'New Age' 'New Age' 'Pop' 'Country' 'Pop' 'Rock' 'Country'\n",
      " 'Country' 'New Age' 'Rap' 'Rap' 'R&B' 'Rap' 'Rock' 'Pop' 'Rap'\n",
      " 'Dance/Electronic' 'New Age' 'Country' 'Pop' 'Country' 'Pop' 'Pop'\n",
      " 'Dance/Electronic' 'Pop' 'Country' 'Rock' 'New Age' 'Country' 'Pop' 'Pop'\n",
      " 'Country' 'Dance/Electronic' 'Country' 'Dance/Electronic' 'Rap' 'Rock'\n",
      " 'Rap' 'New Age' 'Rap' 'Rap' 'Dance/Electronic' 'Country' 'Country'\n",
      " 'Country' 'R&B' 'New Age' 'Rock' 'R&B' 'R&B' 'Country' 'Pop' 'R&B'\n",
      " 'Dance/Electronic' 'New Age' 'Dance/Electronic' 'Country' 'Rap'\n",
      " 'Dance/Electronic' 'Rap' 'Pop' 'Rap' 'Rap' 'Country' 'New Age' 'New Age'\n",
      " 'New Age' 'Pop' 'R&B' 'Pop' 'Rock' 'Country' 'Rap' 'Country' 'R&B' 'Rap'\n",
      " 'Country' 'Dance/Electronic' 'Rap' 'Country' 'Country' 'Pop' 'Rap' 'Pop'\n",
      " 'R&B' 'New Age' 'Country' 'Rap' 'Rap' 'Rock' 'Rap' 'New Age'\n",
      " 'Dance/Electronic' 'Rap' 'Rap' 'Rap' 'Pop' 'New Age' 'Rap' 'Rap'\n",
      " 'Dance/Electronic' 'Pop' 'Rock' 'Country' 'Dance/Electronic' 'Rock'\n",
      " 'New Age' 'Pop' 'Pop' 'Pop' 'Dance/Electronic' 'Country' 'Pop' 'Pop'\n",
      " 'Country' 'Rap' 'New Age' 'R&B' 'Pop' 'Country' 'Pop' 'Rap' 'New Age'\n",
      " 'Country' 'Country' 'Pop' 'R&B' 'Rock' 'Country' 'Dance/Electronic'\n",
      " 'New Age' 'Dance/Electronic' 'Dance/Electronic' 'Pop' 'Rock' 'R&B' 'Pop'\n",
      " 'Rock' 'Dance/Electronic' 'New Age' 'Country' 'Rock' 'Rap' 'Rap' 'Rap'\n",
      " 'Pop' 'Rock' 'Rock' 'New Age' 'Pop' 'Dance/Electronic' 'Country' 'Rap'\n",
      " 'New Age' 'Pop' 'New Age' 'Rock' 'Pop' 'Rap' 'Country' 'Pop' 'New Age'\n",
      " 'R&B' 'Rock' 'Country' 'Rock' 'Pop' 'Country' 'R&B' 'Rap' 'Country' 'Rap'\n",
      " 'Dance/Electronic' 'Country' 'Country' 'Pop' 'Country' 'Rock' 'Country'\n",
      " 'Rap']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "new_data = [[0.765, 0.307, 11, -10.472, 1, 0.0328, 0.727, .00000215,0.109,0.257,119.836, 206109]]\n",
    "new_data_1 = [[0.111, 0.777, 2, -2, 0, 0.0111, 0.111, .00111, 0.9, 0.9, 50, 333333]]\n",
    "new_data_2 = [[0.2, 0.2, 10, -30, 1, 0.05, 0.8, 0.8, 0.5, 0.2, 0.1, 222222]]\n",
    "new_data_3 = [[0.79317033,0.91557169,1.06375534,0.73925307,0.67278438,2.26520775,-1.03279864,-0.48199241,-0.35157953,-1.57235678,-0.91074136,-0.63348198]]\n",
    "new_data_4 = [[-1.14087944,1.24642326,1.06375534,0.41127868,0.67278438,-0.47284276,-1.07621207,1.42227523,0.16892679,-0.22550447,-0.98815799,0.81730637]]\n",
    "new_data_5 = [[0.446,0.239,0,-13.945,1,0.0316,0.5510,0.000004,0.0697,0.298,189.507,336453]]\n",
    "predicted_class = knn.predict(X_test_scaled)\n",
    "print(predicted_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "MDWqZGNKTWoC",
    "outputId": "0149c68c-2f69-4c44-9be6-514c59d4fd68"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.43478260869565216\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "# save the model to disk\n",
    "filename = 'audio_features_model.csv'\n",
    "pickle.dump(knn, open(filename, 'wb')) \n",
    "\n",
    "# save scalar\n",
    "pickle.dump(X_scaler, open(\"scaler.pickle\", \"wb\"))\n",
    "\n",
    "# load the model from disk\n",
    "loaded_model = pickle.load(open(filename, 'rb'))\n",
    "result = loaded_model.score(X_test_scaled, y_test)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1mnP1kesx1ht"
   },
   "source": [
    "## Now trying with neural networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wIA9VOfOx52z"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "M54bTWuux9F5",
    "outputId": "fc99bc7c-e45b-4ee7-f123-353eb5ac1009"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1837, 12) (1837,)\n"
     ]
    }
   ],
   "source": [
    "X = ML_df.drop(\"genre_name\", axis=1)\n",
    "y = ML_df[\"genre_name\"]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YyU3ozfqx9xW"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 439
    },
    "colab_type": "code",
    "id": "QmZtnAHCnIK9",
    "outputId": "05d5ea3c-13b6-463f-922d-6ac958a30f6e"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>0.617</td>\n",
       "      <td>0.378</td>\n",
       "      <td>9</td>\n",
       "      <td>-8.540</td>\n",
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       "    <tr>\n",
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       "      <td>0.3050</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.2840</td>\n",
       "      <td>124.992</td>\n",
       "      <td>172933</td>\n",
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       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>0.773</td>\n",
       "      <td>0.955</td>\n",
       "      <td>2</td>\n",
       "      <td>-6.461</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0413</td>\n",
       "      <td>0.0273</td>\n",
       "      <td>0.827000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.3040</td>\n",
       "      <td>130.003</td>\n",
       "      <td>329792</td>\n",
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       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>0.538</td>\n",
       "      <td>0.471</td>\n",
       "      <td>4</td>\n",
       "      <td>-9.840</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0955</td>\n",
       "      <td>0.0946</td>\n",
       "      <td>0.018500</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.3070</td>\n",
       "      <td>93.070</td>\n",
       "      <td>142000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>0.790</td>\n",
       "      <td>0.647</td>\n",
       "      <td>10</td>\n",
       "      <td>-5.983</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1360</td>\n",
       "      <td>0.0515</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.6540</td>\n",
       "      <td>87.999</td>\n",
       "      <td>211000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>0.428</td>\n",
       "      <td>0.443</td>\n",
       "      <td>2</td>\n",
       "      <td>-8.955</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.5250</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>87.934</td>\n",
       "      <td>180954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>0.544</td>\n",
       "      <td>0.826</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.848</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0288</td>\n",
       "      <td>0.0631</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.5960</td>\n",
       "      <td>142.029</td>\n",
       "      <td>205579</td>\n",
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       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>0.770</td>\n",
       "      <td>0.347</td>\n",
       "      <td>2</td>\n",
       "      <td>-10.265</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0623</td>\n",
       "      <td>0.0511</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.0583</td>\n",
       "      <td>84.957</td>\n",
       "      <td>235477</td>\n",
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       "    <tr>\n",
       "      <th>1769</th>\n",
       "      <td>0.378</td>\n",
       "      <td>0.173</td>\n",
       "      <td>7</td>\n",
       "      <td>-18.001</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0553</td>\n",
       "      <td>0.9890</td>\n",
       "      <td>0.917000</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.0955</td>\n",
       "      <td>79.903</td>\n",
       "      <td>294760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1671</th>\n",
       "      <td>0.516</td>\n",
       "      <td>0.330</td>\n",
       "      <td>8</td>\n",
       "      <td>-15.598</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0350</td>\n",
       "      <td>0.5210</td>\n",
       "      <td>0.864000</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>101.306</td>\n",
       "      <td>278173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1377 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      danceability  energy  key  loudness  mode  speechiness  acousticness   \n",
       "856          0.617   0.378    9    -8.540     1       0.0315        0.4280  \\\n",
       "927          0.883   0.657    8    -5.748     1       0.3050        0.0603   \n",
       "444          0.773   0.955    2    -6.461     1       0.0413        0.0273   \n",
       "860          0.538   0.471    4    -9.840     0       0.0955        0.0946   \n",
       "846          0.790   0.647   10    -5.983     0       0.1360        0.0515   \n",
       "...            ...     ...  ...       ...   ...          ...           ...   \n",
       "1253         0.428   0.443    2    -8.955     0       0.1010        0.5250   \n",
       "1339         0.544   0.826    1    -3.848     1       0.0288        0.0631   \n",
       "1185         0.770   0.347    2   -10.265     1       0.0623        0.0511   \n",
       "1769         0.378   0.173    7   -18.001     1       0.0553        0.9890   \n",
       "1671         0.516   0.330    8   -15.598     1       0.0350        0.5210   \n",
       "\n",
       "      instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "856           0.000092     0.110   0.2200  141.967       216063  \n",
       "927           0.000000     0.128   0.2840  124.992       172933  \n",
       "444           0.827000     0.125   0.3040  130.003       329792  \n",
       "860           0.018500     0.116   0.3070   93.070       142000  \n",
       "846           0.000000     0.248   0.6540   87.999       211000  \n",
       "...                ...       ...      ...      ...          ...  \n",
       "1253          0.000018     0.219   0.2000   87.934       180954  \n",
       "1339          0.000015     0.113   0.5960  142.029       205579  \n",
       "1185          0.000003     0.087   0.0583   84.957       235477  \n",
       "1769          0.917000     0.113   0.0955   79.903       294760  \n",
       "1671          0.864000     0.107   0.1090  101.306       278173  \n",
       "\n",
       "[1377 rows x 12 columns]"
      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DnTDs2EKx9TS"
   },
   "outputs": [],
   "source": [
    "X_scaler = MinMaxScaler().fit(X_train)\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5LwgbDnUx8uZ"
   },
   "outputs": [],
   "source": [
    "# Step 1: Label-encode data set\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoder.fit(y_train)\n",
    "encoded_y_train = label_encoder.transform(y_train)\n",
    "encoded_y_test = label_encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "efwmIlNeyL07"
   },
   "outputs": [],
   "source": [
    "# Step 2: Convert encoded labels to one-hot-encoding\n",
    "y_train_categorical = to_categorical(encoded_y_train)\n",
    "y_test_categorical = to_categorical(encoded_y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UFs37xCMyL53"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n1diB67pyLxn"
   },
   "outputs": [],
   "source": [
    "# Create model and add layers\n",
    "model = Sequential()\n",
    "model.add(Dense(units=7, activation='relu', input_dim=12))\n",
    "# model.add(Dense(units=7, activation='sigmoid'))\n",
    "# model.add(Dense(units=7, activation='sigmoid'))\n",
    "# model.add(Dense(units=, activation='sigmoid'))\n",
    "model.add(Dense(units=7, activation='relu'))\n",
    "# model.add(Dense(units=7, activation='relu'))\n",
    "# model.add(Dense(units=7, activation='relu'))\n",
    "# model.add(Dense(units=7, activation='relu'))\n",
    "# model.add(Dense(units=7, activation='relu'))\n",
    "model.add(Dense(units=7, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p3unpbV6ySa7"
   },
   "outputs": [],
   "source": [
    "# Compile and fit the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 374
    },
    "colab_type": "code",
    "id": "l5oXr3TIyVFf",
    "outputId": "6175b5a2-bfcc-421e-ff57-43451a1855a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "44/44 - 0s - loss: 1.9533 - accuracy: 0.1220 - 451ms/epoch - 10ms/step\n",
      "Epoch 2/10\n",
      "44/44 - 0s - loss: 1.9367 - accuracy: 0.1743 - 54ms/epoch - 1ms/step\n",
      "Epoch 3/10\n",
      "44/44 - 0s - loss: 1.9301 - accuracy: 0.2070 - 50ms/epoch - 1ms/step\n",
      "Epoch 4/10\n",
      "44/44 - 0s - loss: 1.9245 - accuracy: 0.2259 - 50ms/epoch - 1ms/step\n",
      "Epoch 5/10\n",
      "44/44 - 0s - loss: 1.9188 - accuracy: 0.2229 - 50ms/epoch - 1ms/step\n",
      "Epoch 6/10\n",
      "44/44 - 0s - loss: 1.9134 - accuracy: 0.2426 - 50ms/epoch - 1ms/step\n",
      "Epoch 7/10\n",
      "44/44 - 0s - loss: 1.9079 - accuracy: 0.2251 - 50ms/epoch - 1ms/step\n",
      "Epoch 8/10\n",
      "44/44 - 0s - loss: 1.9032 - accuracy: 0.2288 - 47ms/epoch - 1ms/step\n",
      "Epoch 9/10\n",
      "44/44 - 0s - loss: 1.8974 - accuracy: 0.2302 - 47ms/epoch - 1ms/step\n",
      "Epoch 10/10\n",
      "44/44 - 0s - loss: 1.8923 - accuracy: 0.2360 - 42ms/epoch - 947us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x279b35a3ad0>"
      ]
     },
     "execution_count": 517,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    X_train_scaled,\n",
    "    y_train_categorical,\n",
    "    epochs=10,\n",
    "    shuffle=True,\n",
    "    verbose=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "V9KSU-5WyZpf",
    "outputId": "8445c489-fd19-4e7f-9aa0-7c0d0ed5a7c5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 - 0s - loss: 1.8940 - accuracy: 0.2196 - 136ms/epoch - 9ms/step\n",
      "Normal Neural Network - Loss: 1.8939695358276367, Accuracy: 0.21956521272659302\n"
     ]
    }
   ],
   "source": [
    "model_loss, model_accuracy = model.evaluate(\n",
    "    X_test_scaled, y_test_categorical, verbose=2)\n",
    "print(\n",
    "    f\"Normal Neural Network - Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7oc05bYy2ZS8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 54ms/step\n"
     ]
    }
   ],
   "source": [
    "encoded_predictions = model.predict(X_test_scaled[:5]).argmax(axis=-1)\n",
    "prediction_labels = label_encoder.inverse_transform(encoded_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "6rz_Nkv8_9ez",
    "outputId": "0cb3478f-97be-456a-bb26-7073c333dd91"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted classes: ['Dance/Electronic' 'Country' 'Rock' 'Country' 'Rock']\n",
      "Actual Labels: ['Rock', 'Country', 'New Age', 'Rock', 'Rock']\n"
     ]
    }
   ],
   "source": [
    "print(f\"Predicted classes: {prediction_labels}\")\n",
    "print(f\"Actual Labels: {list(y_test[:5])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "asSyj52MdND-"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FW8rTySHdNZJ"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tL95p8ScdNLP"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M6OBYPsYdMzf"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aTKNNDiRdOit"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fZ1icdd1AIbI"
   },
   "outputs": [],
   "source": [
    "# model.save(\"Downloads/audio_features_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MIxu40MuAIQO"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "model = load_model(\"Downloads/audio_features_model.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GjngLz821shw"
   },
   "source": [
    "Trying with logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DOCxSUCh1upX"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "0jbDUHAi12Fx",
    "outputId": "142509ae-90c8-4a0b-cc16-3a6508d2fc33"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1837, 12) (1837,)\n"
     ]
    }
   ],
   "source": [
    "# Assign X (data) and y (target)\n",
    "X = ML_df.drop(\"genre_name\", axis=1)\n",
    "y = ML_df[\"genre_name\"]\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R2rn7Dha2L5e"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "6s5LJjS_2TPH",
    "outputId": "c958fd78-5429-4ec9-cf54-ba1bfc50cd6b",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 525,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\n",
    "classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "SLKtgu322cUk",
    "outputId": "15397ae9-0a72-4bd4-b346-201c41b4abe7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-20 {color: black;background-color: white;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-20 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-20 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 526,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "gYkR_y7J2cCV",
    "outputId": "faaebd73-518f-4eb2-9e00-d4ea1d504852"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Score: 0.15468409586056645\n",
      "Testing Data Score: 0.15434782608695652\n"
     ]
    }
   ],
   "source": [
    "print(f\"Training Data Score: {classifier.score(X_train, y_train)}\")\n",
    "print(f\"Testing Data Score: {classifier.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "A_pwYpPV2ub6",
    "outputId": "e214a8b6-96a6-41e1-f9aa-e20a980df258"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 Predictions:   ['Dance/Electronic' 'Dance/Electronic' 'Dance/Electronic'\n",
      " 'Dance/Electronic' 'Dance/Electronic' 'Dance/Electronic'\n",
      " 'Dance/Electronic' 'Dance/Electronic' 'Dance/Electronic'\n",
      " 'Dance/Electronic']\n",
      "First 10 Actual labels: ['Rap', 'Pop', 'R&B', 'New Age', 'Dance/Electronic', 'Pop', 'R&B', 'Rap', 'Dance/Electronic', 'Rap']\n"
     ]
    }
   ],
   "source": [
    "predictions = classifier.predict(X_test)\n",
    "print(f\"First 10 Predictions:   {predictions[:10]}\")\n",
    "print(f\"First 10 Actual labels: {y_test[:10].tolist()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "id": "cXyAslvi27Jr",
    "outputId": "c954feba-5fc7-4ac2-b316-0bfd87112b5d"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prediction</th>\n",
       "      <th>Actual</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>Dance/Electronic</td>\n",
       "      <td>Rap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dance/Electronic</td>\n",
       "      <td>Pop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dance/Electronic</td>\n",
       "      <td>R&amp;B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dance/Electronic</td>\n",
       "      <td>New Age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dance/Electronic</td>\n",
       "      <td>Dance/Electronic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>Dance/Electronic</td>\n",
       "      <td>Pop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Dance/Electronic</td>\n",
       "      <td>R&amp;B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>Dance/Electronic</td>\n",
       "      <td>R&amp;B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>Dance/Electronic</td>\n",
       "      <td>Rap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>Dance/Electronic</td>\n",
       "      <td>Country</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>460 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Prediction            Actual\n",
       "0    Dance/Electronic               Rap\n",
       "1    Dance/Electronic               Pop\n",
       "2    Dance/Electronic               R&B\n",
       "3    Dance/Electronic           New Age\n",
       "4    Dance/Electronic  Dance/Electronic\n",
       "..                ...               ...\n",
       "455  Dance/Electronic               Pop\n",
       "456  Dance/Electronic               R&B\n",
       "457  Dance/Electronic               R&B\n",
       "458  Dance/Electronic               Rap\n",
       "459  Dance/Electronic           Country\n",
       "\n",
       "[460 rows x 2 columns]"
      ]
     },
     "execution_count": 529,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Prediction\": predictions, \"Actual\": y_test}).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y7J0z_kO0sw7"
   },
   "source": [
    "## Now trying with multiple linear models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241
    },
    "colab_type": "code",
    "id": "tS5IhwXh0y2h",
    "outputId": "31b99267-5eed-48fc-c703-4472514176e9"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>genre_name_Country</th>\n",
       "      <th>genre_name_Dance/Electronic</th>\n",
       "      <th>genre_name_New Age</th>\n",
       "      <th>genre_name_Pop</th>\n",
       "      <th>genre_name_R&amp;B</th>\n",
       "      <th>genre_name_Rap</th>\n",
       "      <th>genre_name_Rock</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.817</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.482</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.783</td>\n",
       "      <td>0.592</td>\n",
       "      <td>11</td>\n",
       "      <td>-8.332</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>0.4350</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.3470</td>\n",
       "      <td>0.726</td>\n",
       "      <td>120.018</td>\n",
       "      <td>174253</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   danceability  energy  key  loudness  mode  speechiness  acousticness   \n",
       "0         0.707   0.681    0    -4.325     1       0.0668        0.0632  \\\n",
       "1         0.644   0.735    8    -5.747     1       0.0391        0.0521   \n",
       "2         0.637   0.643    4    -6.571     1       0.0519        0.1300   \n",
       "3         0.817   0.440   10    -8.482     0       0.0734        0.0603   \n",
       "4         0.783   0.592   11    -8.332     0       0.0331        0.4350   \n",
       "\n",
       "   instrumentalness  liveness  valence    tempo  duration_ms   \n",
       "0          0.000005    0.0322    0.646  117.999       200455  \\\n",
       "1          0.144000    0.1610    0.418   88.980       153947   \n",
       "2          0.000002    0.1420    0.533   97.008       200690   \n",
       "3          0.000001    0.3300    0.544  142.024       272113   \n",
       "4          0.000004    0.3470    0.726  120.018       174253   \n",
       "\n",
       "   genre_name_Country  genre_name_Dance/Electronic  genre_name_New Age   \n",
       "0               False                        False               False  \\\n",
       "1               False                        False               False   \n",
       "2               False                        False               False   \n",
       "3               False                        False               False   \n",
       "4               False                        False               False   \n",
       "\n",
       "   genre_name_Pop  genre_name_R&B  genre_name_Rap  genre_name_Rock  \n",
       "0            True           False           False            False  \n",
       "1            True           False           False            False  \n",
       "2            True           False           False            False  \n",
       "3            True           False           False            False  \n",
       "4            True           False           False            False  "
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Pandas get_dummies to convert categorical data\n",
    "ML_df = pd.get_dummies(ML_df)\n",
    "ML_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "HAPXDqco1PsI",
    "outputId": "0ce13bdc-3722-43b8-89da-9402e7d76b48"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1837, 12) (1837, 1)\n"
     ]
    }
   ],
   "source": [
    "X = ML_df[['danceability',\n",
    "                            'energy',\n",
    "                            'key',\n",
    "                            'loudness',\n",
    "                            'mode', \n",
    "                            'speechiness', \n",
    "                            'acousticness', \n",
    "                            'instrumentalness', \n",
    "                            'liveness', \n",
    "                            'valence', \n",
    "                            'tempo', \n",
    "                            'duration_ms']]\n",
    "y = y = ML_df['genre_name_Rock'].values.reshape(-1, 1)\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "vC__0rJ-4r4V",
    "outputId": "f531a46c-3e80-4e13-a2c5-feee2dbb0dcb"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
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       "      <th>speechiness</th>\n",
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       "      <th>duration_ms</th>\n",
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       "      <td>-6.571</td>\n",
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       "      <td>0.1300</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.1420</td>\n",
       "      <td>0.533</td>\n",
       "      <td>97.008</td>\n",
       "      <td>200690</td>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.817</td>\n",
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       "      <td>0.0603</td>\n",
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       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.783</td>\n",
       "      <td>0.592</td>\n",
       "      <td>11</td>\n",
       "      <td>-8.332</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>0.4350</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.3470</td>\n",
       "      <td>0.726</td>\n",
       "      <td>120.018</td>\n",
       "      <td>174253</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   danceability  energy  key  loudness  mode  speechiness  acousticness   \n",
       "0         0.707   0.681    0    -4.325     1       0.0668        0.0632  \\\n",
       "1         0.644   0.735    8    -5.747     1       0.0391        0.0521   \n",
       "2         0.637   0.643    4    -6.571     1       0.0519        0.1300   \n",
       "3         0.817   0.440   10    -8.482     0       0.0734        0.0603   \n",
       "4         0.783   0.592   11    -8.332     0       0.0331        0.4350   \n",
       "\n",
       "   instrumentalness  liveness  valence    tempo  duration_ms  genre_name_Rock  \n",
       "0          0.000005    0.0322    0.646  117.999       200455            False  \n",
       "1          0.144000    0.1610    0.418   88.980       153947            False  \n",
       "2          0.000002    0.1420    0.533   97.008       200690            False  \n",
       "3          0.000001    0.3300    0.544  142.024       272113            False  \n",
       "4          0.000004    0.3470    0.726  120.018       174253            False  "
      ]
     },
     "execution_count": 532,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping extra dummy columns\n",
    "\n",
    "\n",
    "ML_df = ML_df.drop(columns=['genre_name_Dance/Electronic',\n",
    "                            'genre_name_Country', \n",
    "                            'genre_name_New Age', \n",
    "                            'genre_name_Rap', \n",
    "                            'genre_name_R&B', \n",
    "                            'genre_name_Pop'])\n",
    "ML_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QdRw80Fd1P7a"
   },
   "outputs": [],
   "source": [
    "# Spliting the data into training and testing\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IAGlTr4_1QBS"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Creating a StandardScater model and fit it to the training data\n",
    "\n",
    "X_scaler = StandardScaler().fit(X_train)\n",
    "y_scaler = StandardScaler().fit(y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IypWee3l1P-h"
   },
   "outputs": [],
   "source": [
    "# Transforming the training and testing data using the X_scaler and y_scaler models\n",
    "\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)\n",
    "y_train_scaled = y_scaler.transform(y_train)\n",
    "y_test_scaled = y_scaler.transform(y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "jmBWDI_I1Pyu",
    "outputId": "c5d7ccd7-7a36-456f-9075-ed62b0281ec3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-21 {color: black;background-color: white;}#sk-container-id-21 pre{padding: 0;}#sk-container-id-21 div.sk-toggleable {background-color: white;}#sk-container-id-21 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-21 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-21 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-21 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-21 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-21 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-21 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-21 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-21 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-21 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-21 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-21 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-21 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-21 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-21 div.sk-item {position: relative;z-index: 1;}#sk-container-id-21 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-21 div.sk-item::before, #sk-container-id-21 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-21 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-21 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-21 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-21 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-21 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-21 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-21 div.sk-label-container {text-align: center;}#sk-container-id-21 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-21 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-21\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a LinearRegression model and fit it to the scaled training data\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    "model.fit(X_train_scaled, y_train_scaled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "colab_type": "code",
    "id": "uJHZepqU1Ph8",
    "outputId": "343dc486-dda2-4247-f362-4c2298ccf8fc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making predictions using a fitted model\n",
    "# Ploting the difference between the model predicted values and actual y values, versus the model predicted values\n",
    "\n",
    "predictions = model.predict(X_test_scaled)\n",
    "model.fit(X_train_scaled, y_train_scaled)\n",
    "plt.scatter(model.predict(X_train_scaled), model.predict(X_train_scaled) - y_train_scaled, c=\"blue\", label=\"Training Data\")\n",
    "plt.scatter(model.predict(X_test_scaled), model.predict(X_test_scaled) - y_test_scaled, c=\"orange\", label=\"Testing Data\")\n",
    "plt.legend()\n",
    "plt.hlines(y=0, xmin=y_test_scaled.min(), xmax=y_test_scaled.max())\n",
    "plt.title(\"Residual Plot\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "nvneLgai4VQS",
    "outputId": "6cbccad1-e865-4583-9346-689846eb4939"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.8834898505991294, R2: 0.09640602790084662\n"
     ]
    }
   ],
   "source": [
    "# Used X_test_scaled, y_test_scaled, and model.predict(X_test_scaled) to calculate MSE and R2\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "MSE = mean_squared_error(y_test_scaled, predictions)\n",
    "r2 = model.score(X_test_scaled, y_test_scaled)\n",
    "\n",
    "print(f\"MSE: {MSE}, R2: {r2}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "aO_x6lSt4YNl",
    "outputId": "63493f61-58ef-4b80-c653-a4f0cbf4aab1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.8816334373701857, R2: 0.09830468446408969\n"
     ]
    }
   ],
   "source": [
    "# LASSO model\n",
    "# Note: Use an alpha of .01 when creating the model for this activity\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "lasso = Lasso(alpha=.01).fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "predictions = lasso.predict(X_test_scaled)\n",
    "\n",
    "MSE = mean_squared_error(y_test_scaled, predictions)\n",
    "r2 = lasso.score(X_test_scaled, y_test_scaled)\n",
    "\n",
    "print(f\"MSE: {MSE}, R2: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "GJU66sbi4c9T",
    "outputId": "e57a378a-8df2-4668-9b95-bf3c853c245f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.8834897459043645, R2: 0.09640613497797779\n"
     ]
    }
   ],
   "source": [
    "# Ridge model\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "ridge = Ridge(alpha=.01).fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "predictions = ridge.predict(X_test_scaled)\n",
    "\n",
    "MSE = mean_squared_error(y_test_scaled, predictions)\n",
    "r2 = ridge.score(X_test_scaled, y_test_scaled)\n",
    "\n",
    "print(f\"MSE: {MSE}, R2: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "VLjeX1pk4gU-",
    "outputId": "04400048-388c-4f55-b188-0ec1d99ef3aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.8820972446617862, R2: 0.09783032307485406\n"
     ]
    }
   ],
   "source": [
    "# ElasticNet model\n",
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "elasticnet = ElasticNet(alpha=.01).fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "predictions = elasticnet.predict(X_test_scaled)\n",
    "\n",
    "MSE = mean_squared_error(y_test_scaled, predictions)\n",
    "r2 = elasticnet.score(X_test_scaled, y_test_scaled)\n",
    "\n",
    "print(f\"MSE: {MSE}, R2: {r2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QOSYpl0b-_OH"
   },
   "source": [
    "Trying with SVM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1XNrwZoY_C-E"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.707</td>\n",
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       "    <tr>\n",
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       "      <td>8</td>\n",
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       "      <td>0.0391</td>\n",
       "      <td>0.052100</td>\n",
       "      <td>0.144000</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.418</td>\n",
       "      <td>88.980</td>\n",
       "      <td>153947</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.637</td>\n",
       "      <td>0.643</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.571</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.1420</td>\n",
       "      <td>0.533</td>\n",
       "      <td>97.008</td>\n",
       "      <td>200690</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.817</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.482</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.060300</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.783</td>\n",
       "      <td>0.592</td>\n",
       "      <td>11</td>\n",
       "      <td>-8.332</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>0.435000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.3470</td>\n",
       "      <td>0.726</td>\n",
       "      <td>120.018</td>\n",
       "      <td>174253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1832</th>\n",
       "      <td>0.352</td>\n",
       "      <td>0.796</td>\n",
       "      <td>7</td>\n",
       "      <td>-6.879</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0352</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.3050</td>\n",
       "      <td>0.426</td>\n",
       "      <td>179.379</td>\n",
       "      <td>179240</td>\n",
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       "    <tr>\n",
       "      <th>1833</th>\n",
       "      <td>0.603</td>\n",
       "      <td>0.740</td>\n",
       "      <td>5</td>\n",
       "      <td>-12.128</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0358</td>\n",
       "      <td>0.191000</td>\n",
       "      <td>0.969000</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.102</td>\n",
       "      <td>123.997</td>\n",
       "      <td>379083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1834</th>\n",
       "      <td>0.555</td>\n",
       "      <td>0.501</td>\n",
       "      <td>5</td>\n",
       "      <td>-14.892</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9330</td>\n",
       "      <td>0.793000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.322</td>\n",
       "      <td>64.438</td>\n",
       "      <td>152125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1835</th>\n",
       "      <td>0.603</td>\n",
       "      <td>0.409</td>\n",
       "      <td>5</td>\n",
       "      <td>-14.871</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0310</td>\n",
       "      <td>0.394000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.0633</td>\n",
       "      <td>0.141</td>\n",
       "      <td>97.188</td>\n",
       "      <td>354667</td>\n",
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       "    <tr>\n",
       "      <th>1836</th>\n",
       "      <td>0.631</td>\n",
       "      <td>0.926</td>\n",
       "      <td>10</td>\n",
       "      <td>-13.634</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0441</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.879000</td>\n",
       "      <td>0.0471</td>\n",
       "      <td>0.942</td>\n",
       "      <td>131.230</td>\n",
       "      <td>305333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1837 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      danceability  energy  key  loudness  mode  speechiness  acousticness   \n",
       "0            0.707   0.681    0    -4.325     1       0.0668      0.063200  \\\n",
       "1            0.644   0.735    8    -5.747     1       0.0391      0.052100   \n",
       "2            0.637   0.643    4    -6.571     1       0.0519      0.130000   \n",
       "3            0.817   0.440   10    -8.482     0       0.0734      0.060300   \n",
       "4            0.783   0.592   11    -8.332     0       0.0331      0.435000   \n",
       "...            ...     ...  ...       ...   ...          ...           ...   \n",
       "1832         0.352   0.796    7    -6.879     1       0.0352      0.000255   \n",
       "1833         0.603   0.740    5   -12.128     0       0.0358      0.191000   \n",
       "1834         0.555   0.501    5   -14.892     1       0.9330      0.793000   \n",
       "1835         0.603   0.409    5   -14.871     1       0.0310      0.394000   \n",
       "1836         0.631   0.926   10   -13.634     0       0.0441      0.000021   \n",
       "\n",
       "      instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "0             0.000005    0.0322    0.646  117.999       200455  \n",
       "1             0.144000    0.1610    0.418   88.980       153947  \n",
       "2             0.000002    0.1420    0.533   97.008       200690  \n",
       "3             0.000001    0.3300    0.544  142.024       272113  \n",
       "4             0.000004    0.3470    0.726  120.018       174253  \n",
       "...                ...       ...      ...      ...          ...  \n",
       "1832          0.000058    0.3050    0.426  179.379       179240  \n",
       "1833          0.969000    0.1100    0.102  123.997       379083  \n",
       "1834          0.000000    0.9100    0.322   64.438       152125  \n",
       "1835          0.490000    0.0633    0.141   97.188       354667  \n",
       "1836          0.879000    0.0471    0.942  131.230       305333  \n",
       "\n",
       "[1837 rows x 12 columns]"
      ]
     },
     "execution_count": 554,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "ML_df = df.drop(['artist_name', 'track_name', 'track_id', 'artist_href', 'track_href', 'genre_name'], axis=1)\n",
    "ML_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "colab_type": "code",
    "id": "cmMhX2KWB_DO",
    "outputId": "32c49319-0aa1-4b26-a64c-6819dd2f19ed"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.707</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.644</td>\n",
       "      <td>0.735</td>\n",
       "      <td>8</td>\n",
       "      <td>-5.747</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>0.0521</td>\n",
       "      <td>0.144000</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.418</td>\n",
       "      <td>88.980</td>\n",
       "      <td>153947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.637</td>\n",
       "      <td>0.643</td>\n",
       "      <td>4</td>\n",
       "      <td>-6.571</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0519</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.1420</td>\n",
       "      <td>0.533</td>\n",
       "      <td>97.008</td>\n",
       "      <td>200690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.817</td>\n",
       "      <td>0.440</td>\n",
       "      <td>10</td>\n",
       "      <td>-8.482</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0734</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.544</td>\n",
       "      <td>142.024</td>\n",
       "      <td>272113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.783</td>\n",
       "      <td>0.592</td>\n",
       "      <td>11</td>\n",
       "      <td>-8.332</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>0.4350</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.3470</td>\n",
       "      <td>0.726</td>\n",
       "      <td>120.018</td>\n",
       "      <td>174253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   danceability  energy  key  loudness  mode  speechiness  acousticness   \n",
       "0         0.707   0.681    0    -4.325     1       0.0668        0.0632  \\\n",
       "1         0.644   0.735    8    -5.747     1       0.0391        0.0521   \n",
       "2         0.637   0.643    4    -6.571     1       0.0519        0.1300   \n",
       "3         0.817   0.440   10    -8.482     0       0.0734        0.0603   \n",
       "4         0.783   0.592   11    -8.332     0       0.0331        0.4350   \n",
       "\n",
       "   instrumentalness  liveness  valence    tempo  duration_ms  \n",
       "0          0.000005    0.0322    0.646  117.999       200455  \n",
       "1          0.144000    0.1610    0.418   88.980       153947  \n",
       "2          0.000002    0.1420    0.533   97.008       200690  \n",
       "3          0.000001    0.3300    0.544  142.024       272113  \n",
       "4          0.000004    0.3470    0.726  120.018       174253  "
      ]
     },
     "execution_count": 555,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "feature_names = ML_df.columns\n",
    "ML_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FhCBnFbSCGCR"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(ML_df, target)\n",
    "     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e-bO_O9VQo_I"
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[557], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msvm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SVC \n\u001b[0;32m      4\u001b[0m model \u001b[38;5;241m=\u001b[39m SVC(kernel\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlinear\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 5\u001b[0m model\u001b[38;5;241m.\u001b[39mfit(X_train, y_train)\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\music\\Lib\\site-packages\\sklearn\\svm\\_base.py:252\u001b[0m, in \u001b[0;36mBaseLibSVM.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    249\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[LibSVM]\u001b[39m\u001b[38;5;124m\"\u001b[39m, end\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    251\u001b[0m seed \u001b[38;5;241m=\u001b[39m rnd\u001b[38;5;241m.\u001b[39mrandint(np\u001b[38;5;241m.\u001b[39miinfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mi\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mmax)\n\u001b[1;32m--> 252\u001b[0m fit(X, y, sample_weight, solver_type, kernel, random_seed\u001b[38;5;241m=\u001b[39mseed)\n\u001b[0;32m    253\u001b[0m \u001b[38;5;66;03m# see comment on the other call to np.iinfo in this file\u001b[39;00m\n\u001b[0;32m    255\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape_fit_ \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(X, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshape\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m (n_samples,)\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\music\\Lib\\site-packages\\sklearn\\svm\\_base.py:331\u001b[0m, in \u001b[0;36mBaseLibSVM._dense_fit\u001b[1;34m(self, X, y, sample_weight, solver_type, kernel, random_seed)\u001b[0m\n\u001b[0;32m    317\u001b[0m libsvm\u001b[38;5;241m.\u001b[39mset_verbosity_wrap(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose)\n\u001b[0;32m    319\u001b[0m \u001b[38;5;66;03m# we don't pass **self.get_params() to allow subclasses to\u001b[39;00m\n\u001b[0;32m    320\u001b[0m \u001b[38;5;66;03m# add other parameters to __init__\u001b[39;00m\n\u001b[0;32m    321\u001b[0m (\n\u001b[0;32m    322\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msupport_,\n\u001b[0;32m    323\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msupport_vectors_,\n\u001b[0;32m    324\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_support,\n\u001b[0;32m    325\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdual_coef_,\n\u001b[0;32m    326\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintercept_,\n\u001b[0;32m    327\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_probA,\n\u001b[0;32m    328\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_probB,\n\u001b[0;32m    329\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_status_,\n\u001b[0;32m    330\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_iter,\n\u001b[1;32m--> 331\u001b[0m ) \u001b[38;5;241m=\u001b[39m libsvm\u001b[38;5;241m.\u001b[39mfit(\n\u001b[0;32m    332\u001b[0m     X,\n\u001b[0;32m    333\u001b[0m     y,\n\u001b[0;32m    334\u001b[0m     svm_type\u001b[38;5;241m=\u001b[39msolver_type,\n\u001b[0;32m    335\u001b[0m     sample_weight\u001b[38;5;241m=\u001b[39msample_weight,\n\u001b[0;32m    336\u001b[0m     \u001b[38;5;66;03m# TODO(1.4): Replace \"_class_weight\" with \"class_weight_\"\u001b[39;00m\n\u001b[0;32m    337\u001b[0m     class_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_class_weight\u001b[39m\u001b[38;5;124m\"\u001b[39m, np\u001b[38;5;241m.\u001b[39mempty(\u001b[38;5;241m0\u001b[39m)),\n\u001b[0;32m    338\u001b[0m     kernel\u001b[38;5;241m=\u001b[39mkernel,\n\u001b[0;32m    339\u001b[0m     C\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mC,\n\u001b[0;32m    340\u001b[0m     nu\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnu,\n\u001b[0;32m    341\u001b[0m     probability\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprobability,\n\u001b[0;32m    342\u001b[0m     degree\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdegree,\n\u001b[0;32m    343\u001b[0m     shrinking\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshrinking,\n\u001b[0;32m    344\u001b[0m     tol\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtol,\n\u001b[0;32m    345\u001b[0m     cache_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache_size,\n\u001b[0;32m    346\u001b[0m     coef0\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoef0,\n\u001b[0;32m    347\u001b[0m     gamma\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gamma,\n\u001b[0;32m    348\u001b[0m     epsilon\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mepsilon,\n\u001b[0;32m    349\u001b[0m     max_iter\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_iter,\n\u001b[0;32m    350\u001b[0m     random_seed\u001b[38;5;241m=\u001b[39mrandom_seed,\n\u001b[0;32m    351\u001b[0m )\n\u001b[0;32m    353\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_warn_from_fit_status()\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Takes a very very long time to run\n",
    "\n",
    "from sklearn.svm import SVC \n",
    "model = SVC(kernel='linear')\n",
    "model.fit(X_train, y_train)"
   ]
  },
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    "colab_type": "code",
    "id": "An_OGIicsGhQ",
    "outputId": "1ec756b0-45e8-4fa1-a7e8-01308542c65e"
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  {
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