lab16_Williams.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "lab16_Williams.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Lab 16: PCA \n",
        "\n",
        "##Kobie Williams \n",
        "\n",
        "###4/19/2022 \n",
        "\n",
        "####The purpose of this file is to experiment with using PCA for dimensionality reduction. "
      ],
      "metadata": {
        "id": "xdJN1k5nFpnR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "gEP9J74xDfrk"
      },
      "outputs": [],
      "source": [
        "#3\n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://drive.google.com/uc?export=download&id=1vqMBid4r0C8apwYcpBQfVQ_TwEUNWY8r')"
      ],
      "metadata": {
        "id": "FSn9-9BIGfL2"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#4\n",
        "#Splitting data from columns 0-29 to x \n",
        "X = df.iloc[:, 0:30] \n",
        "\n",
        "#Sending column 30 to y \n",
        "y = df.iloc[:,30:31] \n",
        "\n",
        "y.head(5)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3h17rpTrGiE_",
        "outputId": "ce1549ef-f3e5-4c4c-b9d7-16862e7088c9"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    30\n",
              "0  0.0\n",
              "1  0.0\n",
              "2  1.0\n",
              "3  0.0\n",
              "4  0.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3980d5c5-99b5-4b5e-93d2-dc2cb6d87428\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>30</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3980d5c5-99b5-4b5e-93d2-dc2cb6d87428')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3980d5c5-99b5-4b5e-93d2-dc2cb6d87428 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3980d5c5-99b5-4b5e-93d2-dc2cb6d87428');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier \n",
        "from sklearn.metrics import accuracy_score "
      ],
      "metadata": {
        "id": "lPXSdSnJIkh2"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#5\n",
        "#Intrinsic Dimensionality \n",
        "#Creating so X has 30 components  \n",
        "from sklearn.decomposition import PCA \n",
        "pca = PCA(n_components=30) \n",
        "pca.fit(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QLjLogCeJTqc",
        "outputId": "bec6dc63-3ae3-4499-9230-96ef7c8064a5"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PCA(n_components=30)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Showing the components \n",
        "pca.components_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wIGVftlnSFpd",
        "outputId": "1bca544a-0269-467d-e95e-15e9f2a7eed8"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-2.01673245e-01, -4.07025390e-02, -6.78240092e-02,\n",
              "        -2.04821656e-04,  4.56637997e-02, -7.28180401e-03,\n",
              "         4.60060450e-01, -2.89849333e-03,  3.35174829e-01,\n",
              "         1.69378172e-01,  4.01470172e-01,  5.83859634e-02,\n",
              "         6.23427248e-02, -3.98692842e-03, -2.34380290e-02,\n",
              "         1.59839809e-01,  1.03420136e-03, -2.18463368e-01,\n",
              "         3.19116911e-01,  3.40455185e-02, -1.60905814e-01,\n",
              "         4.37918157e-03, -4.15064052e-01, -2.05409998e-01,\n",
              "        -5.88102188e-04, -9.53809138e-03,  1.33789791e-03,\n",
              "         1.64985629e-02,  1.09948614e-01,  9.62067024e-03],\n",
              "       [-2.57385403e-01,  3.60582444e-01, -5.23060133e-02,\n",
              "         4.18334758e-04, -1.70609755e-01, -1.41820230e-03,\n",
              "        -7.46593533e-03,  5.83527358e-03,  3.19016541e-02,\n",
              "         4.59563492e-02, -2.18409991e-01, -3.15869531e-01,\n",
              "         2.05745945e-01, -5.16395538e-03, -3.19669791e-01,\n",
              "        -7.07946478e-02, -7.76492496e-03, -2.05810195e-01,\n",
              "         2.65396890e-01, -8.41466944e-02,  2.43246119e-01,\n",
              "         4.57136278e-03, -9.54052903e-02,  1.22822833e-01,\n",
              "         7.73244301e-03, -3.39985097e-04, -1.02414566e-03,\n",
              "        -2.71612873e-01, -4.41001736e-01,  2.00828213e-03],\n",
              "       [ 2.71204638e-01,  2.96155628e-02, -1.39592606e-01,\n",
              "        -1.52732148e-03,  4.52842428e-01, -1.25664098e-03,\n",
              "         1.10394484e-01,  5.91932331e-03, -1.56093913e-01,\n",
              "         3.07549727e-01, -4.83062223e-02,  3.82924387e-01,\n",
              "        -3.06871536e-01,  2.87359672e-03, -1.64511884e-01,\n",
              "        -2.17935840e-01, -6.16126331e-03, -2.90286836e-02,\n",
              "         2.25492691e-01,  2.06740389e-01, -9.10406202e-02,\n",
              "         8.78457342e-03,  2.94396058e-02,  1.32588594e-01,\n",
              "        -3.89742708e-03,  2.09965690e-03,  6.56886124e-03,\n",
              "        -1.65798267e-01, -3.20766934e-01, -2.03225335e-02],\n",
              "       [-4.10109954e-02, -1.29019733e-02,  4.14059523e-01,\n",
              "        -1.51423112e-03,  1.90061492e-01, -1.84165241e-02,\n",
              "        -8.11140741e-02,  2.20487467e-02,  1.08267742e-01,\n",
              "        -1.99616504e-02, -3.42742224e-01, -3.07281394e-01,\n",
              "        -4.21558626e-01,  1.10072603e-02,  2.20829876e-01,\n",
              "        -2.24473512e-01, -4.30110437e-03, -3.55915106e-01,\n",
              "         1.81758391e-01, -6.23082263e-02, -1.26382987e-01,\n",
              "         6.43527964e-03, -1.32552307e-01, -1.11933009e-03,\n",
              "        -8.41021086e-03, -1.03635088e-02, -1.54852994e-02,\n",
              "        -9.80035575e-02,  2.73677842e-01,  7.36713099e-03],\n",
              "       [ 4.78225007e-02,  3.21111070e-01, -2.22023814e-01,\n",
              "        -1.96373062e-02, -1.15753484e-02, -3.46077529e-03,\n",
              "        -1.29245839e-01, -8.16817157e-04,  1.38072042e-01,\n",
              "        -2.11424624e-01,  5.45980002e-02, -1.76577140e-01,\n",
              "         3.13269677e-02,  1.94556381e-02,  3.97442588e-01,\n",
              "         1.14745829e-01, -1.90387923e-02,  2.39503994e-01,\n",
              "         2.09316926e-01,  4.37585814e-01, -3.39623181e-01,\n",
              "         3.31148349e-04,  1.22829901e-01, -4.12815447e-02,\n",
              "        -2.36715784e-02, -2.33619346e-03, -5.02220926e-03,\n",
              "        -3.63102686e-01, -8.23779483e-03,  4.43488162e-04],\n",
              "       [-1.75111409e-03, -5.76665966e-03, -1.07150075e-03,\n",
              "         1.89370861e-01, -4.37851106e-03, -2.21032079e-01,\n",
              "         3.34998016e-03,  1.22776795e-01, -3.61825096e-03,\n",
              "         3.42689660e-03,  3.70739108e-03,  6.01774656e-03,\n",
              "         6.81114259e-03,  1.00947742e-01, -1.08716039e-02,\n",
              "         1.64016342e-03, -2.42802111e-01,  1.11727021e-04,\n",
              "        -7.13998119e-03, -8.65069133e-03,  9.18855576e-03,\n",
              "         2.66375806e-01, -8.36678641e-04,  5.00446624e-04,\n",
              "        -7.09079334e-01,  4.51767795e-01, -6.01144554e-02,\n",
              "         8.80191417e-03, -3.17996442e-03,  2.21359123e-01],\n",
              "       [ 1.17897579e-03, -6.31790429e-03, -4.47189652e-03,\n",
              "        -1.36450620e-01, -5.90807149e-03,  1.32085228e-01,\n",
              "         7.95971861e-04,  4.76515369e-01, -3.22897460e-03,\n",
              "        -1.16415156e-03,  7.96765140e-03,  7.51466159e-03,\n",
              "         8.53711835e-03, -4.83045134e-02, -3.34896816e-03,\n",
              "         5.60968816e-03, -4.37529987e-01,  7.82389500e-03,\n",
              "        -1.07997273e-02, -3.61322516e-03,  4.28926906e-03,\n",
              "         5.50505823e-01,  3.28326946e-03, -1.43388254e-03,\n",
              "         1.19069947e-01, -3.79336351e-01, -2.79124707e-01,\n",
              "         9.22714431e-03,  1.37674339e-03, -5.83755872e-02],\n",
              "       [-7.44005606e-05,  3.22789427e-03,  6.76231339e-04,\n",
              "        -4.39170925e-02, -2.16155923e-03, -3.04618973e-01,\n",
              "        -4.91444882e-03, -1.65934227e-01,  6.04554240e-04,\n",
              "        -4.77086955e-03, -3.06259269e-03, -5.46168580e-03,\n",
              "        -1.87380261e-07, -7.33970444e-01,  5.58874734e-03,\n",
              "         4.07915911e-04,  2.28347567e-01,  2.25956271e-03,\n",
              "        -3.22374345e-04,  2.56408010e-03, -2.19246637e-03,\n",
              "         2.21813327e-01,  2.93344993e-03,  1.53631952e-04,\n",
              "        -2.87957902e-01, -2.16023391e-01,  3.91705643e-02,\n",
              "        -3.14870914e-03,  1.93757653e-03, -3.26183235e-01],\n",
              "       [-7.02776012e-03, -6.62571310e-03,  8.90182560e-03,\n",
              "        -2.35753662e-01, -6.00585705e-03,  7.89807491e-02,\n",
              "         2.39636961e-03, -1.07940845e-01,  7.39001117e-04,\n",
              "         1.08969327e-03, -2.00052529e-03, -4.16672293e-03,\n",
              "         2.73087132e-03,  2.94777886e-01, -7.29365951e-03,\n",
              "        -4.45440570e-04, -3.68128738e-01, -8.54292349e-03,\n",
              "        -5.20100347e-03, -1.38954857e-02,  9.25226471e-03,\n",
              "        -1.52213640e-01, -5.66441874e-03, -1.07436605e-03,\n",
              "        -3.59494019e-01, -2.86058387e-01,  5.22765104e-01,\n",
              "         9.36430555e-03,  4.69098136e-03, -4.42471025e-01],\n",
              "       [-7.78286573e-04, -1.24533367e-03, -1.85905435e-03,\n",
              "        -2.61186953e-01, -2.46960896e-04,  1.33225570e-01,\n",
              "         3.52428522e-03,  6.25559997e-01,  1.35388084e-03,\n",
              "         1.48425322e-03,  4.30403764e-03,  2.53476678e-03,\n",
              "         1.90363503e-03, -3.38564383e-01, -1.35605611e-03,\n",
              "         1.90861478e-03, -3.83185916e-02,  1.00967223e-04,\n",
              "         5.12933992e-04, -8.48157140e-05, -2.53381961e-04,\n",
              "        -5.82711824e-01, -2.10767925e-03, -1.39340133e-03,\n",
              "        -1.53357860e-01,  1.84109470e-01, -7.37376749e-02,\n",
              "         1.51506739e-03,  8.25130589e-05, -6.43614499e-02],\n",
              "       [-2.02274714e-04, -4.40408831e-03, -1.27007081e-03,\n",
              "        -8.05752827e-01, -1.93280764e-03, -2.47461433e-01,\n",
              "         2.36842921e-03, -2.73732169e-02, -5.39577109e-03,\n",
              "         4.83555237e-03,  9.80438117e-04,  6.80622814e-03,\n",
              "         4.66652810e-03,  2.83951440e-01, -1.23117891e-02,\n",
              "        -7.41892337e-04,  3.49873095e-01, -4.32915989e-04,\n",
              "        -5.73455201e-03, -7.74768199e-03,  9.59078577e-03,\n",
              "         1.23366769e-01,  2.47382738e-04,  2.32341678e-03,\n",
              "        -1.24956256e-01, -1.49896513e-02, -1.85215991e-01,\n",
              "         6.75561546e-03, -6.34466549e-03,  1.40686624e-01],\n",
              "       [-5.44791011e-03,  1.96684494e-03,  7.63900585e-03,\n",
              "        -1.44887760e-01,  3.59888470e-05,  6.81371657e-01,\n",
              "         9.94052494e-04, -3.27169693e-02,  7.83162732e-03,\n",
              "        -2.66864750e-03, -2.97361448e-03, -1.10054358e-02,\n",
              "        -4.86530357e-03, -5.72342037e-02,  7.61009988e-03,\n",
              "        -6.72278657e-04,  2.53797077e-01, -8.24373511e-03,\n",
              "         6.90986581e-03, -1.41119912e-04, -5.03664093e-03,\n",
              "         3.70723243e-01, -6.49806282e-03, -3.15165238e-03,\n",
              "        -2.92690735e-02,  4.51041568e-01,  1.16342265e-01,\n",
              "        -3.40592893e-03,  8.48175041e-03, -3.00107536e-01],\n",
              "       [-2.87200678e-03,  1.56306935e-03, -3.28853331e-03,\n",
              "         9.41423466e-02, -6.89480788e-03, -5.13914549e-01,\n",
              "        -4.46347330e-04,  4.08960450e-01,  3.97672139e-03,\n",
              "        -5.70904779e-03,  5.87800395e-03, -3.61867039e-03,\n",
              "         7.00035625e-03,  1.82724609e-01,  5.71507656e-03,\n",
              "         6.55961297e-03,  1.40760470e-01,  5.12554830e-03,\n",
              "        -1.88690038e-03,  2.43077126e-03, -2.74279297e-03,\n",
              "         1.75582698e-01,  4.65036275e-04, -3.43400523e-03,\n",
              "         3.43633084e-01,  3.08329202e-01,  3.15564854e-01,\n",
              "         2.57393953e-04,  4.31652068e-03, -4.03303354e-01],\n",
              "       [-2.00021712e-04,  4.18704753e-03, -6.47324751e-03,\n",
              "         3.48453911e-01, -3.02421948e-03,  1.56166691e-01,\n",
              "        -5.02918252e-04,  3.67181009e-01, -1.16231508e-03,\n",
              "        -5.91080360e-04,  2.24066203e-03,  1.07759195e-03,\n",
              "         6.06903750e-03,  2.70404622e-01, -2.66753665e-03,\n",
              "         2.33474113e-03,  5.94171714e-01,  4.63074503e-03,\n",
              "         1.98424217e-04,  2.67171365e-03,  1.44968967e-03,\n",
              "         1.94295724e-02,  2.45163476e-03,  1.07889315e-03,\n",
              "        -3.22818519e-01, -4.32144975e-01,  3.70798657e-02,\n",
              "        -2.61846895e-03, -6.59597194e-03,  1.68327476e-02],\n",
              "       [-4.55141415e-03, -1.55046520e-03, -3.93148439e-04,\n",
              "         1.37438435e-01, -4.13814284e-03, -5.01011607e-02,\n",
              "         4.50091453e-03, -1.51306701e-01,  2.24736222e-03,\n",
              "         1.33464395e-03,  4.22840990e-03, -1.70428327e-04,\n",
              "         5.32787616e-03,  2.37238601e-01, -4.62653631e-03,\n",
              "         2.78574609e-03, -1.80825172e-02, -2.32892861e-03,\n",
              "        -3.96292684e-04, -4.41415711e-03,  3.49766926e-03,\n",
              "        -1.73330358e-01, -4.21758073e-03, -1.75640712e-03,\n",
              "        -8.87798965e-02,  6.26918522e-02, -6.99366237e-01,\n",
              "         3.49544045e-03, -1.37952152e-04, -6.06560516e-01],\n",
              "       [ 4.43683842e-02, -4.14859437e-02, -8.45133692e-02,\n",
              "         2.72013666e-17, -1.06222282e-03, -3.79894153e-17,\n",
              "         6.71298748e-01, -4.98392823e-17, -4.46510183e-01,\n",
              "         5.87546757e-03, -2.36020548e-01, -2.66312978e-01,\n",
              "         1.45230846e-01,  1.00285015e-16,  2.82003822e-01,\n",
              "        -7.65935777e-02, -1.09860362e-17,  6.21118187e-02,\n",
              "        -2.05081567e-01,  7.66726220e-02, -5.10558894e-02,\n",
              "         2.18919422e-17, -1.57448117e-01,  1.62323493e-01,\n",
              "         6.20810719e-17, -5.91068820e-17, -6.96659612e-17,\n",
              "        -8.63956171e-03, -1.97643525e-02,  1.11478689e-16],\n",
              "       [ 1.09365144e-01,  1.89922641e-02,  2.14606188e-01,\n",
              "        -2.75793928e-17,  3.84540815e-01, -1.92987987e-17,\n",
              "        -2.30740491e-01, -1.67983574e-17, -3.34439271e-01,\n",
              "        -3.98973774e-01,  1.69426575e-01,  1.30809361e-01,\n",
              "         4.98434427e-01,  1.92012205e-16,  6.28043756e-02,\n",
              "        -6.91275788e-02,  7.33115486e-17, -1.01574885e-01,\n",
              "         2.12546577e-01, -7.80910975e-02,  1.21781827e-03,\n",
              "         1.42572586e-17, -3.17964086e-01,  4.92135205e-02,\n",
              "        -2.75387352e-17, -1.14600170e-16, -2.31477164e-17,\n",
              "        -2.67140684e-02,  2.59597878e-02, -4.87890978e-17],\n",
              "       [ 0.00000000e+00,  4.72026755e-01,  4.42210309e-01,\n",
              "        -3.82723367e-17,  2.44614899e-01, -7.28583860e-17,\n",
              "         2.10669932e-01, -5.25838054e-18, -1.11634111e-01,\n",
              "         6.77824241e-02,  3.06768811e-01, -1.81812680e-01,\n",
              "        -2.99901164e-02, -2.29850861e-17, -2.59364741e-01,\n",
              "        -2.20928239e-02,  6.99310401e-18,  1.72536820e-01,\n",
              "        -2.29264099e-01, -6.94781277e-02, -8.36200077e-02,\n",
              "        -1.00288701e-16,  2.69070011e-01, -3.00421094e-01,\n",
              "         2.94902991e-17, -1.43114687e-17,  7.39425882e-17,\n",
              "        -7.62740617e-02,  4.38819647e-02,  1.21430643e-16],\n",
              "       [-0.00000000e+00,  3.25325870e-01, -2.49875459e-01,\n",
              "         3.68628739e-17,  4.02004150e-01,  1.21430643e-16,\n",
              "         1.62813480e-01,  7.24789152e-17,  3.84069968e-01,\n",
              "        -2.26883801e-01,  5.72975635e-02, -1.79445090e-03,\n",
              "        -8.61115200e-03,  1.04083409e-17,  2.11781310e-01,\n",
              "        -9.18344015e-04,  3.07913417e-17, -1.67620307e-01,\n",
              "        -1.21271699e-01, -1.98427146e-01,  2.30720497e-01,\n",
              "        -3.68628739e-17,  2.13800030e-01,  3.67532258e-01,\n",
              "         6.07153217e-18, -1.53523028e-16,  5.14996032e-17,\n",
              "         2.83069497e-01,  9.15383480e-03,  1.04083409e-16],\n",
              "       [-3.38385238e-01,  3.64191695e-02, -1.70225446e-02,\n",
              "         9.76188531e-17,  9.02927464e-02,  2.43078127e-16,\n",
              "         4.72474388e-02,  8.88774731e-17,  4.70109270e-02,\n",
              "         1.44628884e-01, -2.28103874e-01,  2.78402837e-01,\n",
              "         1.59655483e-01,  8.82540568e-17,  4.05128202e-01,\n",
              "        -3.26865132e-01,  8.57027936e-18,  2.69444028e-01,\n",
              "         1.71598499e-01, -2.50558016e-01,  1.68711012e-01,\n",
              "         2.20635142e-17,  1.25244263e-01, -4.56580610e-01,\n",
              "        -8.18572640e-17, -7.97972799e-17, -8.23993651e-18,\n",
              "         9.29594587e-03, -8.27064280e-02, -4.05491613e-17],\n",
              "       [ 0.00000000e+00,  2.39652482e-01, -2.32970486e-01,\n",
              "         2.21502504e-16, -2.50203969e-01,  9.86623977e-17,\n",
              "        -1.85045039e-01,  7.39696932e-17, -4.59620251e-01,\n",
              "         2.13111155e-01,  4.42994779e-01, -8.13614693e-02,\n",
              "        -2.12894996e-01,  5.52943108e-17,  3.04454447e-01,\n",
              "        -1.35452917e-01,  2.53567783e-17, -3.32219863e-01,\n",
              "         1.07868492e-01, -1.63039470e-01,  4.44139136e-02,\n",
              "        -7.39425882e-17,  6.69022532e-02, -3.10433935e-02,\n",
              "         4.33680869e-18,  7.37257477e-18, -1.76724954e-17,\n",
              "         1.38346463e-01,  2.03175160e-02, -2.25514052e-17],\n",
              "       [ 0.00000000e+00,  3.80904590e-02,  2.46081438e-01,\n",
              "         4.78810784e-17,  7.75439260e-03,  9.94213392e-17,\n",
              "        -1.53349912e-01, -4.16740210e-18,  1.52664430e-01,\n",
              "         5.62296657e-01,  1.59309891e-01,  3.36707721e-02,\n",
              "         1.94191188e-01,  9.42713789e-17,  2.59406269e-01,\n",
              "         2.61318212e-02,  1.26847419e-16,  1.96775939e-01,\n",
              "        -1.69304119e-01, -9.98665949e-02,  1.01377114e-01,\n",
              "         6.96464371e-17, -1.72550180e-01,  4.65389061e-01,\n",
              "        -4.04949511e-17,  2.38524478e-17,  9.13982431e-17,\n",
              "        -3.11956478e-01,  1.06873738e-01, -5.09575021e-18],\n",
              "       [-0.00000000e+00, -9.34912947e-02, -3.66068248e-01,\n",
              "         5.91567810e-17,  3.68500275e-01, -8.87961579e-17,\n",
              "        -3.01882411e-01, -3.77844457e-17,  8.33135782e-02,\n",
              "         1.94027990e-01,  2.14607950e-02, -4.24952165e-01,\n",
              "         3.21167744e-02, -6.87384177e-17, -5.29167568e-02,\n",
              "        -1.57552848e-01, -6.16174117e-17,  5.96976888e-02,\n",
              "        -3.68964023e-01,  4.79768285e-02, -4.13059855e-02,\n",
              "         1.26011397e-16, -3.49513488e-01, -2.91696348e-01,\n",
              "         5.57822018e-17, -6.15826834e-17, -3.88144378e-17,\n",
              "         1.01336891e-01, -1.21731889e-01,  7.58941521e-17],\n",
              "       [ 0.00000000e+00, -7.34649275e-02,  2.99758534e-01,\n",
              "         9.63313630e-17, -9.01663540e-02, -5.07948718e-17,\n",
              "         1.69035060e-02,  9.71987248e-17,  1.09174953e-01,\n",
              "        -2.04588630e-01,  1.42331079e-01,  1.48136427e-01,\n",
              "        -1.95632201e-01,  8.50014503e-17,  3.28750080e-01,\n",
              "         1.38886809e-01,  1.15505224e-16, -1.85610941e-01,\n",
              "        -3.44511660e-01,  3.15267547e-02,  2.69811035e-02,\n",
              "        -9.12627179e-17, -1.59565004e-01, -1.16320242e-01,\n",
              "        -2.75387352e-17, -9.20487644e-17, -1.92445886e-18,\n",
              "        -8.86419871e-02, -6.58031366e-01,  1.47885176e-16],\n",
              "       [ 0.00000000e+00,  3.05141559e-01, -1.36758127e-01,\n",
              "         5.42101086e-17,  1.06479144e-01,  2.59558000e-16,\n",
              "        -4.59897139e-02,  6.94973593e-17, -1.72296935e-01,\n",
              "         6.34166924e-02, -2.90013549e-01,  2.61992023e-01,\n",
              "        -9.80050765e-02,  3.25260652e-17,  2.41311424e-02,\n",
              "         5.28977146e-01, -2.25209120e-17, -1.82577559e-01,\n",
              "        -1.87337843e-01,  6.47278581e-02,  3.21547165e-01,\n",
              "        -2.47198095e-17, -1.99142471e-01, -2.42626102e-01,\n",
              "        -9.64939934e-17,  1.47451495e-17, -5.45353693e-17,\n",
              "        -2.19081513e-01,  2.75207374e-01, -9.62771529e-17],\n",
              "       [-0.00000000e+00, -4.08805544e-01, -7.61530229e-02,\n",
              "        -1.94505870e-16,  1.58820876e-01, -1.35742112e-16,\n",
              "         1.00996315e-01, -3.65918233e-17, -4.79678239e-02,\n",
              "        -1.75430976e-01,  3.00451344e-01, -2.47155145e-01,\n",
              "        -1.98273779e-01,  9.97465999e-18,  1.84824418e-02,\n",
              "         2.05305020e-02, -1.04252815e-17,  1.23516194e-01,\n",
              "         1.39490037e-01, -1.40074961e-02,  5.66373957e-01,\n",
              "        -1.64798730e-17,  1.52556754e-01, -5.31004969e-02,\n",
              "         4.42354486e-17, -2.90566182e-17,  2.88397778e-17,\n",
              "        -4.25657114e-01,  6.46422071e-02, -3.29597460e-17],\n",
              "       [ 0.00000000e+00, -7.74504295e-02, -2.37429804e-01,\n",
              "         8.29821238e-17, -7.83992309e-02, -7.30210163e-17,\n",
              "         6.38184855e-02, -4.86942301e-17,  8.58057481e-02,\n",
              "        -1.32549861e-01,  2.96739605e-02,  1.94649868e-01,\n",
              "         1.48868330e-01,  1.56125113e-17, -1.01371516e-01,\n",
              "        -3.07598130e-01, -4.00290830e-17, -3.44386810e-01,\n",
              "        -3.44518214e-01, -2.66485463e-01, -2.91398661e-01,\n",
              "         4.62683277e-17,  1.41401595e-01, -4.84479149e-02,\n",
              "        -1.17635936e-17, -2.00577402e-17, -6.06069014e-17,\n",
              "        -5.43863208e-01,  1.59065844e-01, -7.48099499e-17],\n",
              "       [ 0.00000000e+00,  6.21406754e-02,  1.26035887e-01,\n",
              "         8.87826054e-17, -1.22677953e-01, -3.30681663e-18,\n",
              "         2.07272800e-02, -3.19297540e-17,  1.10319562e-01,\n",
              "         3.75676631e-02,  6.44914246e-02,  9.94860687e-02,\n",
              "         1.96799326e-01, -1.11672824e-17,  1.90178344e-03,\n",
              "        -4.20601232e-01,  3.22571322e-17, -2.57203769e-01,\n",
              "        -1.55511655e-01,  6.83420669e-01,  3.53470330e-01,\n",
              "         3.28513258e-17,  4.37628722e-02, -6.72056681e-02,\n",
              "         2.52077005e-17, -4.16333634e-17,  8.39714583e-17,\n",
              "         9.46011643e-02,  1.54184099e-01, -8.73866951e-17],\n",
              "       [-0.00000000e+00, -2.84823351e-01,  7.10585379e-02,\n",
              "         1.81983335e-16,  2.18876588e-01,  5.29090660e-17,\n",
              "        -3.86177616e-02, -1.05330241e-16, -3.71201051e-02,\n",
              "         3.03867432e-01, -8.11776629e-02, -1.64678600e-01,\n",
              "         3.61163788e-01,  4.09828421e-17,  1.22416648e-01,\n",
              "         3.50633998e-01, -2.07252022e-17, -3.99146898e-01,\n",
              "         6.86160653e-02,  8.87514441e-02, -1.10387381e-01,\n",
              "         1.00505541e-16,  5.05785581e-01, -1.06428131e-01,\n",
              "        -9.97465999e-18, -3.07913417e-17,  7.54604712e-17,\n",
              "         3.67747641e-02, -1.26686910e-01, -5.98479599e-17],\n",
              "       [-8.28862334e-01, -8.77445386e-02, -1.55976377e-02,\n",
              "         5.99021700e-17,  1.93886583e-01, -5.33427469e-17,\n",
              "        -9.08022452e-02, -1.21972744e-17, -2.27225815e-01,\n",
              "        -7.74267097e-02,  7.72792420e-02,  1.03650522e-01,\n",
              "        -1.48492408e-01, -1.99493200e-17, -7.88267502e-02,\n",
              "         4.97057673e-02, -2.55261849e-17,  1.90479085e-02,\n",
              "        -1.36174245e-01,  2.10062196e-01, -1.51034374e-01,\n",
              "        -5.29090660e-17,  5.25010064e-02,  2.54527270e-01,\n",
              "         3.90312782e-17,  4.33680869e-18,  2.16840434e-17,\n",
              "         2.11206186e-03,  2.72541551e-02, -1.47451495e-17]])"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#5\n",
        "pca.explained_variance_ratio_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2qHZHX9USPtd",
        "outputId": "a6be923a-b544-4502-f0e8-2e3abe1041ef"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.55577268e-01, 1.83839797e-01, 1.56723711e-01, 1.08710573e-01,\n",
              "       6.14423627e-02, 1.52089771e-02, 1.48198487e-02, 1.45211860e-02,\n",
              "       1.36668210e-02, 1.33087966e-02, 1.31794339e-02, 1.26573305e-02,\n",
              "       1.23703890e-02, 1.21244009e-02, 1.18491053e-02, 1.67033775e-32,\n",
              "       9.54194660e-33, 4.12461831e-33, 3.64145808e-33, 2.47860837e-33,\n",
              "       2.37957450e-33, 2.37957450e-33, 2.37957450e-33, 2.37957450e-33,\n",
              "       2.37957450e-33, 2.37957450e-33, 2.37957450e-33, 2.37957450e-33,\n",
              "       2.37957450e-33, 4.89195897e-34])"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###6\n",
        "\n",
        "What we see from this is that as the number of components goes up, how much sway they have over the variance goes down. "
      ],
      "metadata": {
        "id": "8gwhXtHOSWbQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#7\n",
        "#Visualization \n",
        "vis_pca = PCA(n_components=2) \n",
        "vis_pca.fit(X) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gBVhFBFYmGt2",
        "outputId": "42e2ad60-3f8b-4e9a-de21-b43e753cf49e"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PCA(n_components=2)"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_vis = vis_pca.transform(X)"
      ],
      "metadata": {
        "id": "Rga31L_XqTIE"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#8\n",
        "plt.scatter(X_vis[:, 0], X_vis[:, 1], c='r',alpha=0.2) \n",
        "plt.legend(['Original', 'Transformed'])\n",
        "plt.axis('equal')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "9s6EhWVJqgtb",
        "outputId": "439fff30-ef01-4aa2-b6a2-3873d8e31afb"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-18.929735824690265,\n",
              " 19.236637650710772,\n",
              " -12.09056587282429,\n",
              " 16.443814131161567)"
            ]
          },
          "metadata": {},
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###9\n",
        "We can infer from this plot that the classes are not well separated. It looks like one large cluster, there is also not much of a linear relationship."
      ],
      "metadata": {
        "id": "Fb9Jsnymvmp2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#10\n",
        "from sklearn.model_selection import train_test_split \n",
        "Xtrain, Xtest, ytrain, ytest = train_test_split(X,y, test_size = .50, random_state=0)"
      ],
      "metadata": {
        "id": "qIPfnURqXTVt"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier "
      ],
      "metadata": {
        "id": "UbTBn752GKSS"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#11. Fit a KNN model\n",
        "kModel = KNeighborsClassifier(n_neighbors=1) \n",
        "kModel.fit(Xtrain, ytrain)\n"
      ],
      "metadata": {
        "id": "lT4VA992GVm0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f04b4d17-e4b0-48e0-bb3c-98627470f985"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=1)"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ypred = kModel.predict(Xtest)\n",
        "print(kModel.predict(Xtest))"
      ],
      "metadata": {
        "id": "AWfud0bsH8pz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "503f2800-2165-4fec-e06c-ce0fac1e40e3"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1. 1. 0. 1. 0. 0. 0. 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 1. 0. 0. 1. 1. 1. 1.\n",
            " 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 1. 1. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1.\n",
            " 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0.\n",
            " 1. 0. 0. 1. 1. 1. 0. 0. 1. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1.\n",
            " 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 0. 1. 1. 1. 1. 1. 1. 1. 0. 1. 0. 1. 0.\n",
            " 1. 1. 1. 0. 0. 1. 1. 1. 0. 1. 1. 1. 1. 1. 0. 1. 1. 0. 0. 1. 1. 1. 1. 0.\n",
            " 1. 1. 0. 1. 1. 1. 1. 1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 1. 1. 1. 1. 0. 0.\n",
            " 0. 0. 0. 0. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 0. 1. 0. 1. 1. 1. 1. 0. 0. 1.\n",
            " 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 1. 1. 0. 1. 0. 0. 1. 0. 1.\n",
            " 1. 0. 1. 1. 0. 0. 1. 1. 0. 1. 1. 0. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1. 1. 1.\n",
            " 0. 1. 0. 1. 0. 0. 0. 1. 1. 0. 1. 0. 1. 1. 1. 0. 0. 1. 0. 1. 0. 1. 1. 1.\n",
            " 1. 0. 0. 0. 1. 1. 1. 0. 0. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0.\n",
            " 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 1. 0. 1.\n",
            " 1. 0. 1. 1. 1. 1. 0. 0. 0. 1. 0. 1. 1. 0. 1. 0. 0. 0. 1. 1. 0. 1. 0. 0.\n",
            " 1. 1. 1. 0. 0. 1. 0. 1. 1. 1. 0. 0. 0. 0. 0. 1. 1. 0. 1. 1. 0. 1. 0. 0.\n",
            " 1. 0. 1. 0. 0. 0. 0. 1. 1. 1. 0. 1. 1. 1. 0. 1. 0. 1. 0. 0. 0. 0. 0. 1.\n",
            " 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 1. 0. 1. 1. 0. 1. 0. 0. 1. 0. 0. 1. 0.\n",
            " 0. 0. 1. 1. 1. 0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0.\n",
            " 1. 1. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1. 1. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 1.\n",
            " 1. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 1. 1.\n",
            " 1. 0. 1. 1. 1. 0. 1. 0. 0. 1. 1. 0. 0. 1. 0. 0. 1. 0. 1. 1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(ytest, ypred)"
      ],
      "metadata": {
        "id": "kkU9hN3qIEuU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6545627-25d9-4380-d290-579cf013d79b"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.94"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#12\n",
        "class_pca = PCA(n_components=30) "
      ],
      "metadata": {
        "id": "mm4N5aWkxQHm"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#13 \n",
        "class_pca.fit(Xtrain)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qz0oBzYazJ7W",
        "outputId": "ade94985-3e3d-439d-e6ed-479e6fdd6c1b"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PCA(n_components=30)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#14 \n",
        "Xtrain2 = class_pca.transform(Xtrain)"
      ],
      "metadata": {
        "id": "zqdl5veXz5DY"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Xtrain2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bk3qMGo00Bx4",
        "outputId": "12816bbb-ad19-4a07-c531-c95cfd59d215"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#15 \n",
        "class_pca.explained_variance_ratio_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HZcqBCAb0DDr",
        "outputId": "73b12690-2931-4d4e-e7e0-28916f91f9aa"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.56099673e-01, 1.94443180e-01, 1.49550250e-01, 1.04659495e-01,\n",
              "       6.31197147e-02, 1.63711790e-02, 1.53057128e-02, 1.40669538e-02,\n",
              "       1.36856747e-02, 1.33696801e-02, 1.28895254e-02, 1.23082152e-02,\n",
              "       1.20131890e-02, 1.13329458e-02, 1.07846108e-02, 8.48916977e-33,\n",
              "       5.10469628e-33, 4.60759506e-33, 2.89734126e-33, 2.17340688e-33,\n",
              "       2.17340688e-33, 2.17340688e-33, 2.17340688e-33, 2.17340688e-33,\n",
              "       2.17340688e-33, 2.17340688e-33, 2.17340688e-33, 2.17340688e-33,\n",
              "       2.17340688e-33, 9.54691688e-34])"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#16a\n",
        "t_kModel = KNeighborsClassifier(n_neighbors=1)\n",
        "t_kModel.fit(Xtrain2, ytrain) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rzaIIDXG0OTZ",
        "outputId": "ada4d5f6-76b9-45b9-fb94-959bc113ade1"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=1)"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#16b\n",
        "Xtest2 = class_pca.transform(Xtest) "
      ],
      "metadata": {
        "id": "4i66wT1m1EA8"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#16c \n",
        "ypred2 = t_kModel.predict(Xtest2) "
      ],
      "metadata": {
        "id": "ApyP4eaz1czS"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "HznhKKvt11Vc"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(ytest,ypred2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "osQPPcV4160u",
        "outputId": "fb78f320-296c-49df-8a27-20fb38ab4b42"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.93      0.95      0.94       250\n",
            "         1.0       0.95      0.93      0.94       250\n",
            "\n",
            "    accuracy                           0.94       500\n",
            "   macro avg       0.94      0.94      0.94       500\n",
            "weighted avg       0.94      0.94      0.94       500\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###16d \n",
        "\n",
        "The accuracy on Xtest2 is surprising because we used a diffenent number of components than we did for the visualization but we got the same results for accuracy"
      ],
      "metadata": {
        "id": "NcESPXcG2XZ7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV "
      ],
      "metadata": {
        "id": "ppHuCMiN2G_9"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eXtrain, eXtest, eytrain, eytest = train_test_split(X,y, test_size=0.5,random_state=0)"
      ],
      "metadata": {
        "id": "5IdK-RsX9rFC"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#17 \n",
        "#Experimenting with different PCA transforms \n",
        "#Maybe create a for loop with different PCA transforms  \n",
        "for i in range(1,31): \n",
        "  exp_pca = PCA(n_components=i)\n",
        "  exp_pca.fit(eXtrain) \n",
        "  newXtrain = exp_pca.transform(eXtrain)\n",
        "  emodel = KNeighborsClassifier(n_neighbors=1)\n",
        "  emodel.fit(newXtrain,eytrain) \n",
        "  eXtest2 = exp_pca.transform(eXtest)\n",
        "  eypred2 = emodel.predict(eXtest2)\n",
        "  print('Experiment:', i)\n",
        "  print(accuracy_score(eytest,eypred2))\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B_D4mini4k-3",
        "outputId": "feadaf80-73b7-4ad4-9d55-83f6fc5894b0"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Experiment: 1\n",
            "0.55\n",
            "Experiment: 2\n",
            "0.59\n",
            "Experiment: 3\n",
            "0.892\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Experiment: 4\n",
            "0.896\n",
            "Experiment: 5\n",
            "0.958\n",
            "Experiment: 6\n",
            "0.958\n",
            "Experiment: 7\n",
            "0.962\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Experiment: 8\n",
            "0.96\n",
            "Experiment: 9\n",
            "0.956\n",
            "Experiment: 10\n",
            "0.946\n",
            "Experiment: 11\n",
            "0.95\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Experiment: 12\n",
            "0.952\n",
            "Experiment: 13\n",
            "0.948\n",
            "Experiment: 14\n",
            "0.946\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Experiment: 15\n",
            "0.94\n",
            "Experiment: 16\n",
            "0.94\n",
            "Experiment:"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " 17\n",
            "0.94\n",
            "Experiment: 18\n",
            "0.94\n",
            "Experiment: 19\n",
            "0.94\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Experiment: 20\n",
            "0.94\n",
            "Experiment: 21\n",
            "0.94\n",
            "Experiment: 22\n",
            "0.94\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Experiment: 23\n",
            "0.94\n",
            "Experiment: 24\n",
            "0.94\n",
            "Experiment: 25\n",
            "0.94\n",
            "Experiment: 26\n",
            "0.94\n",
            "Experiment: 27\n",
            "0.94\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Experiment: 28\n",
            "0.94\n",
            "Experiment: 29\n",
            "0.94\n",
            "Experiment: 30\n",
            "0.94\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_classification.py:198: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exp_pca = PCA(n_components=7)\n",
        "exp_pca.fit(eXtrain)\n",
        "exp_pca.explained_variance_ratio_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XmsMi5PfDcG3",
        "outputId": "5c073b85-7671-4f76-8b78-861bbf251639"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.35609967, 0.19444318, 0.14955025, 0.1046595 , 0.06311971,\n",
              "       0.01637118, 0.01530571])"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###18 \n",
        "####a: The best accuracy we can get is 96.2% accuracy \n",
        "####b: This is with 7 principal components \n",
        "####c: 35% is explained by the components "
      ],
      "metadata": {
        "id": "ZdGojCk2Bu3M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "raaFtADPA3OC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}