HeartDiseaseClassification.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "HeartDiseaseClassification.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/parthatom/HeartDiseaseClassification/blob/master/HeartDiseaseClassification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvr3TBa_J0uw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "1234d9a0-9ef5-4d0c-934f-dcdadd9560d8"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A10Dw1SgJ9sp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "96778f8b-4110-4ad7-f699-6cd2e5893ce6"
      },
      "source": [
        "!unzip /content/drive/\"My Drive\"/Data/Heart_Disease_UCI/heart-disease-uci.zip -d data"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/My Drive/Data/Heart_Disease_UCI/heart-disease-uci.zip\n",
            "  inflating: data/heart.csv          \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LIWF_IecHQMu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torchvision\n",
        "import sys\n",
        "import os\n",
        "from pathlib import Path\n",
        "import sklearn\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cp71Ox85N3fN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import model_selection"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PtqBrweZHdLQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_path = Path('/','content','data')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jXFb_yHpLujh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "ccf46863-cb4c-4a60-ab7d-ab7f51065554"
      },
      "source": [
        "df_train = pd.read_csv(os.path.join(data_path,\"heart.csv\"))\n",
        "df_train.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yf1vGL3jL6Mm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "b244b51d-fbbc-491c-cfa4-d1b9bd3e3c1b"
      },
      "source": [
        "df_train.describe()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp  ...          ca        thal      target\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean    54.366337    0.683168    0.966997  ...    0.729373    2.313531    0.544554\n",
              "std      9.082101    0.466011    1.032052  ...    1.022606    0.612277    0.498835\n",
              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     47.500000    0.000000    0.000000  ...    0.000000    2.000000    0.000000\n",
              "50%     55.000000    1.000000    1.000000  ...    0.000000    2.000000    1.000000\n",
              "75%     61.000000    1.000000    2.000000  ...    1.000000    3.000000    1.000000\n",
              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000    1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UK5SnuKWSiCV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "221cc5cd-8c42-4f90-d870-150884020ef6"
      },
      "source": [
        "df_train['target'].values"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1UevxxOQLdL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "f6f5174b-05b0-40a2-8121-d445fc42adf2"
      },
      "source": [
        "pos = df_train[df_train['target']==1]\n",
        "total_male_pos = len(pos[pos['sex']==1])\n",
        "neg = df_train[df_train['target']==0]\n",
        "total_male_neg = len(neg[neg['sex']==1])\n",
        "print(total_male_neg, total_male_pos)\n",
        "pos = df_train[df_train['target']==1]\n",
        "total_female_pos = len(pos[pos['sex']==0])\n",
        "neg = df_train[df_train['target']==0]\n",
        "total_female_neg = len(neg[neg['sex']==0])\n",
        "print(total_female_neg, total_female_pos)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "114 93\n",
            "24 72\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SphYAe0OToE0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df_train.copy()\n",
        "df = df.drop(['target'], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uG-9GTTfV_eq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "27fdafbe-ec8f-43af-e43e-8d38f31329ec"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp  ...       slope          ca        thal\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean    54.366337    0.683168    0.966997  ...    1.399340    0.729373    2.313531\n",
              "std      9.082101    0.466011    1.032052  ...    0.616226    1.022606    0.612277\n",
              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     47.500000    0.000000    0.000000  ...    1.000000    0.000000    2.000000\n",
              "50%     55.000000    1.000000    1.000000  ...    1.000000    0.000000    2.000000\n",
              "75%     61.000000    1.000000    2.000000  ...    2.000000    1.000000    3.000000\n",
              "max     77.000000    1.000000    3.000000  ...    2.000000    4.000000    3.000000\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MD8d3w3FVygX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def normalize(df):\n",
        "    result = df.copy()\n",
        "    for feature_name in df.columns:\n",
        "      if(feature_name == 'sex'):\n",
        "        pass\n",
        "      else:\n",
        "        max_value = df[feature_name].max()\n",
        "        min_value = df[feature_name].min()\n",
        "        result[feature_name] = (df[feature_name] - min_value) / (max_value - min_value)\n",
        "    return result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HYu6LPjoV45f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = normalize(df)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kl4TILq_UiU3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "f07750d5-34d7-4b26-833e-50aaad247747"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.528465</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.322332</td>\n",
              "      <td>0.354941</td>\n",
              "      <td>0.274575</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.264026</td>\n",
              "      <td>0.600358</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>0.167678</td>\n",
              "      <td>0.699670</td>\n",
              "      <td>0.182343</td>\n",
              "      <td>0.771177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.189210</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>0.344017</td>\n",
              "      <td>0.165454</td>\n",
              "      <td>0.118335</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.262930</td>\n",
              "      <td>0.174849</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>0.187270</td>\n",
              "      <td>0.308113</td>\n",
              "      <td>0.255652</td>\n",
              "      <td>0.204092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.385417</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.245283</td>\n",
              "      <td>0.194064</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.477099</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.541667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.339623</td>\n",
              "      <td>0.260274</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.625954</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.129032</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.666667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.433962</td>\n",
              "      <td>0.339041</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.725191</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.258065</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp  ...       slope          ca        thal\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean     0.528465    0.683168    0.322332  ...    0.699670    0.182343    0.771177\n",
              "std      0.189210    0.466011    0.344017  ...    0.308113    0.255652    0.204092\n",
              "min      0.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%      0.385417    0.000000    0.000000  ...    0.500000    0.000000    0.666667\n",
              "50%      0.541667    1.000000    0.333333  ...    0.500000    0.000000    0.666667\n",
              "75%      0.666667    1.000000    0.666667  ...    1.000000    0.250000    1.000000\n",
              "max      1.000000    1.000000    1.000000  ...    1.000000    1.000000    1.000000\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNbC5cFDMRA9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = model_selection.train_test_split(df,df_train['target'].values, test_size = 0.1, shuffle = True, random_state = 42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7IFXKjh-N0cf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "73ce4c71-218e-4b38-f5a1-86607a389676"
      },
      "source": [
        "X_train.head()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
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              "      <th>restecg</th>\n",
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              "      <th>thal</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>284</th>\n",
              "      <td>0.666667</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.433962</td>\n",
              "      <td>0.184932</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.511450</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.306452</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>0.687500</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.245283</td>\n",
              "      <td>0.321918</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.213740</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.290323</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.50</td>\n",
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              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>0.333333</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.094340</td>\n",
              "      <td>0.187215</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.587786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.483871</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.770833</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.528302</td>\n",
              "      <td>0.228311</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.328244</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.419355</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>0.708333</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.339623</td>\n",
              "      <td>0.292237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.580153</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.225806</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          age  sex   cp  trestbps  ...   oldpeak  slope    ca      thal\n",
              "284  0.666667    1  0.0  0.433962  ...  0.306452    1.0  0.25  1.000000\n",
              "198  0.687500    1  0.0  0.245283  ...  0.290323    0.5  0.50  1.000000\n",
              "42   0.333333    1  0.0  0.094340  ...  0.483871    0.5  0.00  0.666667\n",
              "17   0.770833    0  1.0  0.528302  ...  0.419355    0.0  0.00  0.666667\n",
              "168  0.708333    1  0.0  0.339623  ...  0.225806    0.5  0.25  1.000000\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4JOapiI_UpjD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "b0c05ae1-822b-4453-d946-827efeed2770"
      },
      "source": [
        "X_train.describe()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>272.000000</td>\n",
              "      <td>272.000000</td>\n",
              "      <td>272.000000</td>\n",
              "      <td>272.000000</td>\n",
              "      <td>272.000000</td>\n",
              "      <td>272.000000</td>\n",
              "      <td>272.000000</td>\n",
              "      <td>272.000000</td>\n",
              "      <td>272.000000</td>\n",
              "      <td>272.000000</td>\n",
              "      <td>272.000000</td>\n",
              "      <td>272.000000</td>\n",
              "      <td>272.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.528646</td>\n",
              "      <td>0.680147</td>\n",
              "      <td>0.324755</td>\n",
              "      <td>0.346941</td>\n",
              "      <td>0.273654</td>\n",
              "      <td>0.147059</td>\n",
              "      <td>0.270221</td>\n",
              "      <td>0.605607</td>\n",
              "      <td>0.323529</td>\n",
              "      <td>0.168050</td>\n",
              "      <td>0.707721</td>\n",
              "      <td>0.183824</td>\n",
              "      <td>0.769608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.190133</td>\n",
              "      <td>0.467279</td>\n",
              "      <td>0.340525</td>\n",
              "      <td>0.157322</td>\n",
              "      <td>0.117300</td>\n",
              "      <td>0.354817</td>\n",
              "      <td>0.264008</td>\n",
              "      <td>0.168366</td>\n",
              "      <td>0.468685</td>\n",
              "      <td>0.186033</td>\n",
              "      <td>0.303812</td>\n",
              "      <td>0.259924</td>\n",
              "      <td>0.204559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.011416</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.129771</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.390625</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.245283</td>\n",
              "      <td>0.194064</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.511450</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.541667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.339623</td>\n",
              "      <td>0.259132</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.633588</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.129032</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.666667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.433962</td>\n",
              "      <td>0.337900</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.725191</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.258065</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.924528</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp  ...       slope          ca        thal\n",
              "count  272.000000  272.000000  272.000000  ...  272.000000  272.000000  272.000000\n",
              "mean     0.528646    0.680147    0.324755  ...    0.707721    0.183824    0.769608\n",
              "std      0.190133    0.467279    0.340525  ...    0.303812    0.259924    0.204559\n",
              "min      0.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%      0.390625    0.000000    0.000000  ...    0.500000    0.000000    0.666667\n",
              "50%      0.541667    1.000000    0.333333  ...    0.500000    0.000000    0.666667\n",
              "75%      0.666667    1.000000    0.666667  ...    1.000000    0.250000    1.000000\n",
              "max      1.000000    1.000000    1.000000  ...    1.000000    1.000000    1.000000\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PTS0h7XrUsxr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "4170cb13-2723-4b88-9070-315071aed5f0"
      },
      "source": [
        "X_train.iloc[0].values"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.66666667, 1.        , 0.        , 0.43396226, 0.18493151,\n",
              "       0.        , 0.        , 0.51145038, 1.        , 0.30645161,\n",
              "       1.        , 0.25      , 1.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PbYAhTtYXMi6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a0c2111d-36ed-4727-a838-da11d4bd6403"
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "model = KMeans(n_clusters = 2, random_state = 42)\n",
        "kmeans = model.fit(X_train)\n",
        "y_pred_train = kmeans.labels_\n",
        "y_pred_test = kmeans.predict(X_test)\n",
        "train_accuracy = np.mean((y_pred_train == y_train).astype('float32'))\n",
        "test_accuracy = np.mean((y_pred_test == y_test).astype('float32'))\n",
        "print(f'train accuracy {train_accuracy*100:.3f}')\n",
        "print(f'test accuracy {test_accuracy*100:.3f}')"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train accuracy 72.059\n",
            "test accuracy 70.968\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fF4I-8YYZgo_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "dc5749a1-799d-46fe-a917-e97d0d297a21"
      },
      "source": [
        "y_pred_test"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0,\n",
              "       1, 1, 1, 1, 1, 0, 1, 1, 0], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qk3hOP4Zjzf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "transform_train = model.transform(X_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9U9_BR_mbi_A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "colors = ['y', 'r']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbZYTOuLdDHj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "df0d1970-27dd-4024-8b55-993dfd83a500"
      },
      "source": [
        "for i in range(len(transform_train)):\n",
        "  plt.scatter(transform_train[i][0], transform_train[i][1], c=colors[y_pred_train[i]])\n",
        "plt.show()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tBKVgBvWlCTt/VL+WoaHlJlw2xwQcm8rSD6fmIGHIxUbQ/wOA32/9fjuAlwH0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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQQSTjGddL-S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "03349174-8252-4489-aeb1-0372e6139790"
      },
      "source": [
        "for i in range(len(transform_train)):\n",
        "  plt.scatter(transform_train[i][0], transform_train[i][1], c=colors[y_train[i]])\n",
        "plt.show()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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y8wNQtCYZrdpRKeZg/bz0mdIP1Zk2Xpa0X4BzZsbZ+tYq0Nbi1ii91V864EKJ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EivrI0D-dPfm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "7153be32-930a-4a99-8691-33f873664c5f"
      },
      "source": [
        "from sklearn.manifold import TSNE\n",
        "tsne = TSNE(random_state = 42).fit_transform(X_train)\n",
        "for i in range(len(transform_train)):\n",
        "  plt.scatter(tsne[i][0], tsne[i][1], c=colors[y_pred_train[i]])\n",
        "plt.show()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aIgq1ulpzUxyXu7KUcVj6xdSOiYm5hioYbRD2bRM3ATRWy9jOubZ2BocPX2o43u33J4oo\npaIf1SGHDh1S586d63YziMgVLBsEdIqikz3Ys2dL8OfO6wRHjmwZgyyA7WPl8i5sbX0Ipe7EOt/w\ncMVN+dhiozScx/T+7N07ibW1Mx0L/CLyhlLqkPE+BnkiMlldrWFpaRJeT9dveLjS1KPNyuuvj1sG\nPCvGXnvwImR7vp1YB1kbHiVDKJXu3d68JfgawUHdLC+MYUGeOXkialKfEdoc4AEkDJrtmZiYsw54\nxqldb2Xzc9M5g5S6YwnwQPBTQDfr6RnkifpEkgqVsBmhWjn9BlqMjlaxd++k75xl7N07idHRaqza\n9aQljhsbtwDoSVHeIGsaulVPzyBP1AdM1SJLS8dw9qwknhGqmXv4WVhdreHq1VO+c27i6tVTWF2t\nhUxqqh93nKNIEqg3N9ewtPQ0Llw4gYmJORw5shX6+OYev/lcXps6vf4Nq2uI+oC5Z65TCqY1WKJy\n0rqH2xm2lMyFCydgGlL0167XLxCNDxT5e1Dq/4Wed2Njbft9KZcda+5dt60MYBPDwxU4zlFcvXqq\naZxgYmKuK+vfsCdP1AeieubBnLEOkrber3R0ApCt7Rsba02Bd2DAaRjgtKWdhoZGEad3711M7LyL\nx+Z2ID9wYKEh1eOvp+/G+jfsyRP1gTjVIv5gOjpaxU9+8he4cuV5NPaCBfv2He9oHXictnvK5Z0N\nbQub/er1vqNsbJh68CUAjWkcf+2+d2s+b+fXv2GQJyqIer14PYANDDhQCm6Pt7GsLyiY3z5wYAE/\n9VM/1/WJPubJTWbBYBl+gWhnXMGcp48K1uXyLmPaJ8v1bxjkiQqgedKSDmCNvVBvrRb/V822Bout\nR9pJ/tRLVI8+GCzjXSC8Hn34RTCOsGC9ulrD1taHxvv04HA2Ms/Ji8gTInJeRC6KyLNZn4+oH0WX\nPHoUhocr2LfvOEwliXk1OlqNGCcwX6hGR6u+/LjNFo4cUThyZAsHDy425NLLZcf4jIEBJ/FiZcvL\ns9ZZt161UBYyDfIiUgbwewC+AOARAL8kIo9keU6ifpQkp+sttmUqScyTYKmhHgC197RtM0pHR6s4\nfPiSNdB7ve+3357B0tKk+2mhBMc5igMH5o0lkhsbayiV7nEvAvEWKwv7N8py8DXrnvxnAFxUSi0r\nfQn7JoAnMz4nUd9JltNtXo0xbzscmer6zQOgWthmH56wmbNvvz2DK1eeg//Cd+XKc/jJT/7C8ElA\nX2g2Ntag1Mc4ePA0Dh++FHn+qH+jrAZfsw7yDwD4se/nd91j20RkSkTOici5a9euZdwcomKKMw0f\n8Cbu2JYqyM8OR/HTT0Dcks7G1I3ufe/dO4nl5Vk3wDe7ckX3zm058yQXx6h/o6wGX7teJ6+UOqmU\nOqSUOrRnz55uN4eoJzXnnnW+fWDAaUopRKUt8iD+BSdZSaeXujlyZAsTE3O4evVUxGDuJlZXa24p\naXtt9f6NBgaa8/xZbj6SdXXNewAe9P38KfcYEaUsSSWMaeXGPO1wZCt9LJcdDAzsbFpWWK80mazM\nM96nBXF76vFLT8N4/0adXIM+6yD/lwD2i8hD0MH9KQD/OuNzElGIxpLEfG50YVtC+MCBeQD1tl+4\ncAIbGzfhbQiSZJmAOD1wkXJETz/Z7N9gcD948HTm73umQV4ptSEivw7gFejPjy8opd7K8pxEFC0P\n9e9hbBei4Cxc02Bs2K5RfnFm0iq1gbCZsVGpIj2gexJeHb5I2X3NzqxbA3DTECLqEfUKmDgkcvVI\n065XSdx33+P49Kdftd4ft71pbMDCTUOIqKdFDX4GxcmThw2ENjKvnf/xxxdDn6V78NGyrmpikCei\n3Isa/PRLMog8OlrFZz97HQcPLhpnt7ZXchpvbZysq5oY5Kn7ajVgfBwolfTXWr5mXlL3JenttrKX\n6uhoFY89dr1pWYP2Sk6jd8/qRFUTgzx1V60GTE0BKyuAUvrr008Du3cz2NO2uL3dODNfw/jr6L1Z\nrGEzZcPs2zdlPF4u70TcpRDSwCBP3TU7C9w2DHytreng7w/0cXv8YY/jp4aeFGdGb1a9YtNM2TjB\n+cCBBezbNw3/QnD79k3jscc+bLiIZI3VNdRdpRKMe7h5KhXg0iUdjJ95BrjjW8VvaAh44QWg6vuP\n4n0y8F84RkaAk+4gmO2+an7LCUnT1SrNm5h4K2vmrda/k8KqaxjkqbvGx3WKJoxSOn2zZligynGA\n69ejX6/i5lVt9126FLPB1C16Vmvzv18aJYi9jiWUlF9zc8DgoP1+Ed07NwV4oPn4ZcsA3eXL4fdR\n7mW5dV5wWeO8LbvcDgZ5yk7c/LeEbKislM7bxzVmGaDbtct+n+045Ypt8LXdEkTTssbnz08VJtAz\nyFM2TFUzwYFUQAfwO+bdcraFpXOcQG3z3JzO1QfdvAkcPdp839CQfg7lXqtVLjZe731p6encr6/f\nDgZ5yoapaub27eZeebupkvn5xp+rVeDee5sfd/cu8K1vNQ/y5mhMisJ5VS7+SUtKCS5cOJE4zdLY\nezfL0/r67WCQp2zYet/BoJ5GqiSYEgrL39+923js7l3gxIn220Ado9THvu8/chcps6dZTPn2OMsM\n52l9/XYwyFP6wmrPg0F9bk6XMfqF5ej9HMecEkpqbY318j0iKjgH0yy2fHvU6pN5W1+/HQzylL6w\nnnEw/12t6jr1SkUH90oFOH26XvJo410YTCmhViQZ3KWuiZNC8T/GdFGI7sF3ZiZqp2S9aQj1I1u6\nBDBPOqpWzceDE5dEdI+9UtEXi2PH2m+rh2WUPWFgYFfoht5AY5olSV69VBopVHD3sCdP+eKVXR47\nBtxzj07J+Hv4SumJS9WqPZ9fsvxZO05zNY6HZZQ9IWqcPJhmSbLmTREDPMAgT1mwBVLbcU+w7HJt\nDfj4Yx3cvcDuZ8rnj4wAv/Zr5lLJ+Xl9Mz2HZZQ9YXPzhvW+gQGnIVCvrtawuXkrxqtKx9aR6QYG\neUrf/HzzLNbBweZyx6C4ZZceUz7/5ElgYUGvaeM/7q1xY3sO167pCWE9862tetWNN+AaldqJes0i\n4No1lI1aTQfny5d1KmRuLjqQ2hYrEwG2wrdyo/4QtWWft46NbZ0bk4MHF3u+Fx+2dg0HXikbtsHU\nMGNj5vp65svJ5QXjpaWnjfd7A61xB1wHBpyeD/BRmK6h/LDl2JkvJ5/R0Wrkbk1xUjCl0gj2749I\nIRYAgzzlB/PlFFPUOjbmTUYG3U27O7crUx4wXUP50kqah/qOF5yXl2exvn4Zw8NjDZuGRN3fTzjw\nSkTU47hpCBFRn2KQJyIqMAZ5IqICY5AnIiowBnkiogJjkCciKjAGeSKiAmOQJyIqMAZ5IqICY5An\nIiowBnkiogJjkC8Cb1/UUkl/rdXMx4io7zDIZ6kTgTa4L+rKCvDMM8BXv9p4bGqKgZ6oDzHIZ8UU\nfNMOtLUaMDnZvC/qnTvA3buNx8L2SvW/nv+iNDPDTwNEPa6tIC8ivygib4nIlogcCtz3dRG5KCLn\nReTz7TWzByXdlDop7yKyuRn/OSsr9qBtuig99xw/DRD1uLbWkxeRgwC2APwXAP9WKXXOPf4IgG8A\n+AyAfQBeBXBAKRUakQq1nnzWm1KPj5v3Qw0j0timoSHg3nuBGzd0e+NcMBwHuH492XmJKFOZrSev\nlFpSSp033PUkgG8qpdaVUn8L4CJ0wO8fts2n09qU+nK8jYq3BQM8oNM6a2v6eNxPBGtr7M0T9ZCs\ncvIPAPix7+d33WNNRGRKRM6JyLlr165l1JwuyHpT6qQXizR3AEsr5UREmYsM8iLyqoi8abg9mUYD\nlFInlVKHlFKH9uzZk8ZL5kPWm1KbLiIi6bx2lKSfIoioayI38lZKfa6F130PwIO+nz/lHusvWW5K\n7b3u7KwOumNjwNGjwKlTzQO+SZTLesxgbEzn3j/6qPkxaaWciChzWaVrXgbwlIgMi8hDAPYD+H5G\n5+q8vEw0qlaBS5d0UL50CVhYaPz0EMZxgMHBxmNDQ8B999V//uVfjk45ee+FCDAwoL/GeU/y8h4S\nFZ1SquUbgC9B59vXAawCeMV33yyAdwCcB/CFOK/36KOPqtxbXFRqZEQpneXWt5ERfTyr81UqSono\nr0nOU6k0ttO7VSrNr+04Sg0ONv9e09P285veizjvSaffQ6KCA3BO2eK07Y5u3HIf5BcXlSqXwwNn\nmudynGTB0/QatmAafH2ReL+X/8Jgey+i3pOoiw8RJRIW5PtzxuvMTD21MDCgf44SNfkozcFI71xr\na833JZlQZRv8BfTSB/7Xt1Xf+H+v4ISpqLJL23uS9DgRtc4W/btx60hPfnra3Iucng5/nq33mUUv\nNOpcItm+fvD38nrvcZ/DnjxRR4E9eR+vJxv3uCdsdmnS+veoQceoHu2uXfHPZRK3x1wq6cc+/XRr\ns2tt70nWcwiIaFv/BXlbiiEq9VAu2+9LUv8eZ+GyqCB+82Z71ShhJZDlsg7QO3boqh1bGieKUvb3\nJOs5BES0ra21a9LWkbVrBgbMAb1cBjY27M8LK0lM8h7a1pypVHQZJADs3m3Oxwc5DjA/nzw41mo6\nJ3/nTuPxwUHgxRf163n18q3y/z5ElKnM1q7pSVNTyY57KpVkx23iDDreuBHvtdbW9LrxSXv11Srw\nwgv6IuFxnHqAr9XaC/AiemIWwHp4om6zJeu7cetYCeX0dL38r1w2D7oG69Onp9Op7Y4z6Jh0kDPt\nAcuo8wfr53fsaH7M0FB67xkRhQLr5BOy1ZeHTQxq97XjTjLKotomyFYzD+iAHvy9TfX8XrtYRUOU\nubAgX6x0TVqpAduGH2fONC4j4M+Fxz13nEHHalXv+BR3wTFvIDWtvV5tA7M7dgC3btXb6r22bfxA\nWcYqVlaYviHqFFv078atrZ58mlPlw3qypp58FtP046ZsBgfrM1iDyxKUy+alCqLalcWnjbDUD9M3\nRG1BX6Rr0pxgY3utYPD3AlQWk3vCLjTezXHqAdKWMmm1XVFr5rQyOaqd9hCRVViQL066Js2p8ra1\n2lUg/eAtMdDqucNSKbaUSaWi27G4COzcCRw7Fp4yaaVdQPMKl8EyzTSXIOByBkSZKU6QT3O7PX/e\nHNA148EA7/HWck967qhJUWGzQk3PTSKN9eBbmXVrG2Pg+vRE2bF18btxy01OPuw1bSmTpOeOk+IJ\nlno+/nj7aZI0cuCm/H+c1BJLKokygb7IySvV3trrJnECquPUz+3Pi/vz5Sa2nLtXDpnGwKZXqx6s\n92/3PbK9Lzt21O+zjV+k/W9ERH0U5JOKCjhxBj/DgnJYLzWqJ99Kj91xwjf4aHd9+qj3xXsvuMok\nUUcxyJssLuqebrDn6w94cQJtVFC2Bbaoi0KcC0xY26PO1U7wjfpdoy4CRJSqsCBfnIHXpE6caF6g\n684dfdxjGvwM8tZoSVphEzUpKulgpFL2+0yTu0xtjDtxKmpQuGT5s+IAK1Hn2aJ/N24d7cmH9Yr9\nbGmOYO87yX6qcXLRreTkbT3yqE8FXnuSpJtMv0+re74SUVvAdI1B3CDvidrf1RTghobqFwjbQGQY\n0yJpYSkkWzok7DlpTuiyvUa5zABPlKGwIN9/68l7bGu2Ow5w/br5OaWSOS0ioicN1Wr1yVG7dunN\nPe7etbeh1TXXw9akn5urt2FsrL7b0tRUc8rGvx591O8WRxqvQUSJcT15k/l5vUlG0Je/bH9O1KQn\n/yzRnTvDAzzQ+kxPW0786FHzBCugOf+/uKgvZlFjAEny6GlOSCOidNi6+N24dbyEcno6WRolSd46\nTnVMOyWFppx4OymXNCaTZTEhjYgigTl5i1aCYtwB1Dgbb6Qd/NotXfRfKLzxh6QTljjZiajjGORt\nsqznNvVqvfNlFfzSGDxN0htnQCfKhbAg3785eSDbHHKwDt5x9GBs1EYg7Wx8Ela/Hpdtw5TZ2eZ2\nhi2wRkT5YIv+3bh1ZVmDTuSQ454n6RiB7VztrFUT99MNly4gyg0wXROiEykHW0D0Fjfz2mELsK3W\nmWe1OqZSXLqAKEfCgnx/p2uA6M0x0mArlVxbq6c3ZmfNNeYAsLmpNweZmUl23ripF7+4KR+WSxL1\nBAb5TggLfF7Ajdr4Qyng+eeT5bxb2bHKtGGKd2HwnzuN/D8RZY5BvhPCAt/lyzp4Rg3IAjrQh/XC\ng1rtbVer9SC+uamPBQdWoyH8hPIAAAc4SURBVBZYI6Jc6N9lDTrNtoyC12OOu4VfkiUCvAoYf8pm\nZCReMA5bOqGVpRiIKDNc1iBrtrJH/3EAGBpqfJ6X3kiyvEGcnLd33mPHgHvu0eWbSXvbYamedso8\niaizbCOy3bh1pbqmXbYKFtN+poODuqImWMkTdxeouCtXplEWGlYRZFpOuFTSvzMRdRxYQpmhsOV1\n49aRh10oki4zkFb9uq1NYWvrAwz0RF0QFuSZrmmXLa3hDVjGebytouXMGb2yZNgAaNz2JF3x0jaw\neuNG+PNOnkx2HiLKFAde22UboCyXzYE+bODSNFAqYq6ft72OrT2lUn3A1r+OfFK21/fL0d8UUT/I\nbOBVRP6jiPwfEfmRiPyxiNznu+/rInJRRM6LyOfbOU+u2erFp6aS15GbJi/ZAqapZ16rAbdumR/v\nr8hZWwMmJ3XFT9LB06h9b8vleK9DRJ1hy+PEuQH4eQAD7ve/A+B33O8fAfBXAIYBPATgHQDlqNfr\nyZy8UvalEZIumRBnDXpbjt22v2qpFO/1kgzOLi4qtWMHc/JEOYFObP8nIl8C8K+UUlUR+bp7AfkP\n7n2vAPgtpdTrYa/Rk+maNNlSIcGUjanWPU4aJUrSGviZGd2OzU3dg5+aAhYW2msDESXWqTr5ZwD8\nqfv9AwB+7LvvXfcYhbGlfo4fj55Z2upWgu28xsICsLGhL0AbGwzwRDk0EPUAEXkVwF7DXbNKqW+7\nj5kFsAEg8awYEZkCMAUAY/2+uJUXuIMbcccZIB0bM/fkHQf48EPgzp14r0FEhRIZ5JVSnwu7X0S+\nAuCfAXhc1XM/7wF40PewT7nHTK9/EsBJQKdroptccNVqa1Uvc3PmJQzm5/X3J07Ul1XYsUMHff9G\n41xcjKiQ2q2ueQLAvwPwL5RS/rKQlwE8JSLDIvIQgP0Avt/OuShC2IJh1Spw/Xp9ePTWLeDFF7m4\nGFEfaGvgVUQuQlfQeCtv/U+l1HH3vlnoPP0GgN9QSv2p+VXq+n7glYioBWEDr5HpmjBKqb8fct8c\nAH7+JyLqIi5rQERUYAzyREQFxiBPRFRgDPJERAXGIE9EVGAM8kREBcYgT0RUYLnaNERErgGIu5Ti\nbgDXM2xOWtjOdLGd6WI709WtdlaUUntMd+QqyCchIudsM7zyhO1MF9uZLrYzXXlsJ9M1REQFxiBP\nRFRgvRzkT3a7ATGxneliO9PFdqYrd+3s2Zw8ERFF6+WePBERRWCQJyIqsJ4L8iLyiyLylohsicgh\n3/FxEflYRH7o3p7PYzvd+74uIhdF5LyIfL5bbQwSkd8Skfd87+HRbrfJT0SecN+ziyLybLfbYyMi\nl0Tkr933MDe74IjICyLygYi86Tu2S0S+IyIX3K+f7GYb3TaZ2pmrv00ReVBE/kxE/sb9f37CPZ67\n97PngjyANwH8AoDvGe57Ryn1afd2vMPtCjK2U0QeAfAUgH8A4AkACyJS7nzzrP6z7z080+3GeNz3\n6PcAfAHAIwB+yX0v8+ofu+9hnmqmX4L+m/N7FsBrSqn9AF5zf+62l9DcTiBff5sbAH5TKfUIgJ8F\n8DX37zF372fPBXml1JJS6ny32xElpJ1PAvimUmpdKfW3AC4C+ExnW9eTPgPgolJqWSl1B8A3od9L\nikkp9T0ANwKHnwRwyv3+FIAvdrRRBpZ25opS6n2l1A/c7z8EsATgAeTw/ey5IB/hIRH53yLyXRF5\nrNuNsXgAwI99P7/rHsuLXxeRH7kfmbv+UdMn7++bnwLw30XkDRGZ6nZjIowqpd53v78KYLSbjYmQ\ny79NERkH8DMA/hdy+H7mMsiLyKsi8qbhFtZzex/AmFLqZwD8GwB/KCKfyGE7uyqizc8B+GkAn4Z+\nP/9TVxvbuz6rlPqH0Kmlr4nIP+p2g+JQup46rzXVufzbFJGdAP4bgN9QSt3035eX97OtjbyzopT6\nXAvPWQew7n7/hoi8A+AAgMwGvlppJ4D3ADzo+/lT7rGOiNtmEfmvAP4k4+Yk0dX3LQml1Hvu1w9E\n5I+hU02mMaQ8WBWR+5VS74vI/QA+6HaDTJRSq973efnbFJFB6ABfU0r9kXs4d+9nLnvyrRCRPd4A\npohMANgPYLm7rTJ6GcBTIjIsIg9Bt/P7XW4TAMD9o/R8CXrwOC/+EsB+EXlIRIagB69f7nKbmojI\nDhG51/sewM8jX+9j0MsAJt3vJwF8u4ttscrb36aICIA/ALCklPpd3135ez+VUj11g/4Hfhe6174K\n4BX3+L8E8BaAHwL4AYB/nsd2uvfNAngHwHkAX+j2e+pr12kAfw3gR9B/rPd3u02B9h0F8Lb73s12\nuz2WNk4A+Cv39lae2gngG9Cpjrvu3+avAHCgq0AuAHgVwK6ctjNXf5sAPgudivmRG3N+6P595u79\n5LIGREQFVph0DRERNWOQJyIqMAZ5IqICY5AnIiowBnkiogJjkCciKjAGeSKiAvv/KHvuYpWvXikA\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GFotnn1zeH-v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "17ea6e00-0da8-410a-c500-863c848ad9b9"
      },
      "source": [
        "for i in range(len(transform_train)):\n",
        "  plt.scatter(tsne[i][0], tsne[i][1], c=colors[y_train[i]])\n",
        "plt.show()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WCB7Iu67PbXLZ592nK806Fm43pQMH+DRASIdT70j+JQAfA/Bte6OI3AFgN4D3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\nAsz/B8qT19wgiEcjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7rxEaJUedxo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}