kmeams.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,

   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, roc_auc_score\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import preprocessing\n",
    "\n",
    "# Import Data\n",
    "spotifyData = pd.read_csv('data1.csv')\n",
    "\n",
    "\n",
    "# Drop label and irrelevant classes\n",
    "x = spotifyData.drop(['target','song_title', 'serial_num', 'mode', 'key', 'duration_ms'], axis=1)\n",
    "y = spotifyData['target']\n",
    "\n",
    "# #normalizing data\n",
    "# stddevs = np.std(x,axis=0)+1e-6\n",
    "# x /= stddevs\n",
    "# x /= stddevs\n",
    "\n",
    "# x = x.values \n",
    "# min_max_scaler = preprocessing.MinMaxScaler()\n",
    "# x_scaled = min_max_scaler.fit_transform(x)\n",
    "# x = pd.DataFrame(x_scaled)"
   ]
  },
  {
   "cell_type": "code",

   "execution_count": null,

   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "      <th>artist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.833</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.021900</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>-8.795</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>150.062</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1990</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.006110</td>\n",
       "      <td>0.1370</td>\n",
       "      <td>-10.401</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>160.083</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.588</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0344</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>-7.148</td>\n",
       "      <td>0.2890</td>\n",
       "      <td>75.044</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.173</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.6040</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.0922</td>\n",
       "      <td>-15.236</td>\n",
       "      <td>0.0261</td>\n",
       "      <td>86.468</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.230</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.512000</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>-11.648</td>\n",
       "      <td>0.0694</td>\n",
       "      <td>174.004</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.904</td>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acousticness  danceability  energy  instrumentalness  liveness  loudness  \\\n",
       "0        0.0102         0.833   0.434          0.021900    0.1650    -8.795   \n",
       "1        0.1990         0.743   0.359          0.006110    0.1370   -10.401   \n",
       "2        0.0344         0.838   0.412          0.000234    0.1590    -7.148   \n",
       "3        0.6040         0.494   0.338          0.510000    0.0922   -15.236   \n",
       "4        0.1800         0.678   0.561          0.512000    0.4390   -11.648   \n",
       "\n",
       "   speechiness    tempo  time_signature  valence  artist  \n",
       "0       0.4310  150.062             4.0    0.286     449  \n",
       "1       0.0794  160.083             4.0    0.588     222  \n",
       "2       0.2890   75.044             4.0    0.173     449  \n",
       "3       0.0261   86.468             4.0    0.230      95  \n",
       "4       0.0694  174.004             4.0    0.904     628  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[\"artist\"] = x[\"artist\"].astype('category')\n",
    "x[\"artist\"] = x[\"artist\"].cat.codes\n",
    "x.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2, f_regression\n",
    "# instantiate SelectKBest to determine 20 best features\n",
    "best_features = SelectKBest(score_func=chi2, k=20)\n",
    "fit = best_features.fit(X,y)\n",
    "df_scores = pd.DataFrame(fit.scores_)\n",
    "df_columns = pd.DataFrame(X.columns)\n",
    "# concatenate dataframes\n",
    "feature_scores = pd.concat([df_columns, df_scores],axis=1)\n",
    "feature_scores.columns = ['Feature_Name','Score']  # name output columns\n",
    "print(feature_scores.nlargest(20,'Score'))  # print 20 best features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "#creating instance of one-hot-encoder\n",
    "encoder = OneHotEncoder(handle_unknown='ignore')\n",
    "\n",
    "#perform one-hot encoding on 'team' column \n",
    "encoder_df = pd.DataFrame(encoder.fit_transform(x[['artist']]).toarray())\n",
    "\n",
    "#merge one-hot encoded columns back with original DataFrame\n",
    "x = x.join(encoder_df)\n",
    "\n",
    "#drop 'artist' column\n",
    "x.drop('artist', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>...</th>\n",
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       "      <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",
       "      <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>2012</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2017 rows × 1343 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0     1     2     3     4     5     6     7     8     9     ...  1333  \\\n",
       "0      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "1      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "2      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "3      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "4      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "...    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...   ...   \n",
       "2012   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "2013   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "2014   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "2015   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "2016   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
       "\n",
       "      1334  1335  1336  1337  1338  1339  1340  1341  1342  \n",
       "0      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "1      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "2      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "3      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "4      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "...    ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "2012   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "2013   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "2014   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "2015   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "2016   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "\n",
       "[2017 rows x 1343 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",

   "execution_count": 11,

   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [

      "Confusion Matrix: \n",
      "[[188  12]\n",
      " [162  42]]\n",
      "\n",
      "Classification Report: \n",

      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.47      0.83      0.60       200\n",
      "           1       0.34      0.08      0.13       204\n",
      "\n",

      "    accuracy                           0.46       404\n",
      "   macro avg       0.41      0.46      0.37       404\n",
      "weighted avg       0.41      0.46      0.37       404\n",
      "\n"

     ]
    }
   ],
   "source": [
    "# Split data into training and testing sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=69)\n",
    "\n",
    "# Fit model\n",
    "kmeans = KMeans(n_clusters=2)\n",
    "model = kmeans.fit(x_train, y_train)\n",
    "# Test model\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "# See results\n",
    "print(\"Confusion Matrix: \")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(\"\\nClassification Report: \")\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(\"\\nAccuracy Score: \")\n",
    "print(accuracy_score(y_test, y_pred))\n",
    "print(\"\\nROC AUC Score: \")\n",
    "print(roc_auc_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 3,
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    "y_pred"
   ]
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