ML_Project.ipynb

{
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  "metadata": {
    "colab": {
      "name": "ML_Project",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tHMhDM8EXv_H",
        "colab_type": "text"
      },
      "source": [
        "To read more about Datset can be found at http://archive.ics.uci.edu/ml/datasets/Heart+Disease"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iDIIAR49cGoK",
        "colab_type": "text"
      },
      "source": [
        "## Import Libraries and Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IgMnp63jX5Hw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import sys\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sklearn\n",
        "import matplotlib\n",
        "import keras\n",
        "import seaborn as sns\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lrrHu1sjswk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from pandas.plotting import scatter_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLjMPAPyj-4C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import the heart disease dataset\n",
        "url = \"http://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data\"\n",
        "\n",
        "# the names will be the names of each column in our pandas DataFrame\n",
        "names = ['age',\n",
        "        'sex',\n",
        "        'cp',\n",
        "        'trestbps',\n",
        "        'chol',\n",
        "        'fbs',\n",
        "        'restecg',\n",
        "        'thalach',\n",
        "        'exang',\n",
        "        'oldpeak',\n",
        "        'slope',\n",
        "        'ca',\n",
        "        'thal',\n",
        "        'class']\n",
        "\n",
        "# read the csv\n",
        "cleveland = pd.read_csv(url, names=names)\n",
        "data=cleveland"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zursb6YE-wR9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgvSVf9Dj_vO",
        "colab_type": "code",
        "outputId": "0297e325-6cae-4eda-c4da-819ab62f478a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print ((cleveland.shape))\n",
        "print (cleveland.loc[1])"
      ],
      "execution_count": 645,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(303, 14)\n",
            "age          67\n",
            "sex           1\n",
            "cp            4\n",
            "trestbps    160\n",
            "chol        286\n",
            "fbs           0\n",
            "restecg       2\n",
            "thalach     108\n",
            "exang         1\n",
            "oldpeak     1.5\n",
            "slope         2\n",
            "ca          3.0\n",
            "thal        3.0\n",
            "class         2\n",
            "Name: 1, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6-SIriC5kD7M",
        "colab_type": "code",
        "outputId": "3a6caa57-e6f6-4b16-e9c2-b03bb2e53e89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 771
        }
      },
      "source": [
        "cleveland.loc[280:]"
      ],
      "execution_count": 646,
      "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>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>57.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>335.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281</th>\n",
              "      <td>47.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>253.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>179.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>282</th>\n",
              "      <td>55.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>205.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>283</th>\n",
              "      <td>35.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>122.0</td>\n",
              "      <td>192.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284</th>\n",
              "      <td>61.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>161.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285</th>\n",
              "      <td>58.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>318.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>286</th>\n",
              "      <td>58.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>225.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>146.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.8</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>58.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>?</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>288</th>\n",
              "      <td>56.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>221.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>163.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289</th>\n",
              "      <td>56.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>240.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>169.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>290</th>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>212.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>291</th>\n",
              "      <td>55.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>342.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>44.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>169.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.8</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>63.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>63.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>124.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>41.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>157.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>182.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>59.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>164.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>241.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>123.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>45.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>264.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>68.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>57.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>38.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>173.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>?</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      age  sex   cp  trestbps   chol  ...  oldpeak  slope   ca  thal  class\n",
              "280  57.0  1.0  4.0     110.0  335.0  ...      3.0    2.0  1.0   7.0      2\n",
              "281  47.0  1.0  3.0     130.0  253.0  ...      0.0    1.0  0.0   3.0      0\n",
              "282  55.0  0.0  4.0     128.0  205.0  ...      2.0    2.0  1.0   7.0      3\n",
              "283  35.0  1.0  2.0     122.0  192.0  ...      0.0    1.0  0.0   3.0      0\n",
              "284  61.0  1.0  4.0     148.0  203.0  ...      0.0    1.0  1.0   7.0      2\n",
              "285  58.0  1.0  4.0     114.0  318.0  ...      4.4    3.0  3.0   6.0      4\n",
              "286  58.0  0.0  4.0     170.0  225.0  ...      2.8    2.0  2.0   6.0      2\n",
              "287  58.0  1.0  2.0     125.0  220.0  ...      0.4    2.0    ?   7.0      0\n",
              "288  56.0  1.0  2.0     130.0  221.0  ...      0.0    1.0  0.0   7.0      0\n",
              "289  56.0  1.0  2.0     120.0  240.0  ...      0.0    3.0  0.0   3.0      0\n",
              "290  67.0  1.0  3.0     152.0  212.0  ...      0.8    2.0  0.0   7.0      1\n",
              "291  55.0  0.0  2.0     132.0  342.0  ...      1.2    1.0  0.0   3.0      0\n",
              "292  44.0  1.0  4.0     120.0  169.0  ...      2.8    3.0  0.0   6.0      2\n",
              "293  63.0  1.0  4.0     140.0  187.0  ...      4.0    1.0  2.0   7.0      2\n",
              "294  63.0  0.0  4.0     124.0  197.0  ...      0.0    2.0  0.0   3.0      1\n",
              "295  41.0  1.0  2.0     120.0  157.0  ...      0.0    1.0  0.0   3.0      0\n",
              "296  59.0  1.0  4.0     164.0  176.0  ...      1.0    2.0  2.0   6.0      3\n",
              "297  57.0  0.0  4.0     140.0  241.0  ...      0.2    2.0  0.0   7.0      1\n",
              "298  45.0  1.0  1.0     110.0  264.0  ...      1.2    2.0  0.0   7.0      1\n",
              "299  68.0  1.0  4.0     144.0  193.0  ...      3.4    2.0  2.0   7.0      2\n",
              "300  57.0  1.0  4.0     130.0  131.0  ...      1.2    2.0  1.0   7.0      3\n",
              "301  57.0  0.0  2.0     130.0  236.0  ...      0.0    2.0  1.0   3.0      1\n",
              "302  38.0  1.0  3.0     138.0  175.0  ...      0.0    1.0    ?   3.0      0\n",
              "\n",
              "[23 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 646
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OMBGEli6Yfh7",
        "colab_type": "text"
      },
      "source": [
        "### Data Cleaning:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WapzA-YlYDZm",
        "colab_type": "text"
      },
      "source": [
        "Data Cleaning: Drop rows with \"?\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1u9wFSdjkIwx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = data[~data.isin(['?'])]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhX1keppk9mY",
        "colab_type": "code",
        "outputId": "dc23f32b-98ad-41c1-f8e7-80271758b5ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data = data.dropna(axis=0)\n",
        "data.shape"
      ],
      "execution_count": 648,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(297, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 648
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tEq0V-mp2Voh",
        "colab_type": "code",
        "outputId": "85c68706-4fa9-4b96-cff1-694523759831",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print (data.shape)\n",
        "print (data.dtypes)"
      ],
      "execution_count": 649,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(297, 14)\n",
            "age         float64\n",
            "sex         float64\n",
            "cp          float64\n",
            "trestbps    float64\n",
            "chol        float64\n",
            "fbs         float64\n",
            "restecg     float64\n",
            "thalach     float64\n",
            "exang       float64\n",
            "oldpeak     float64\n",
            "slope       float64\n",
            "ca           object\n",
            "thal         object\n",
            "class         int64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXQm_-LAYLDg",
        "colab_type": "text"
      },
      "source": [
        "# Data Visualization:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74Kuhiz4YRhP",
        "colab_type": "text"
      },
      "source": [
        "Pie Chart and Bar Plot to understand frequency for each class:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M362Uc37FRQo",
        "colab_type": "code",
        "outputId": "4f2b8343-ed86-4c25-b482-25b400a1671c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "\n",
        "\n",
        "# create two plots side by side\n",
        "f, ax = plt.subplots(1,2,figsize=(14,6))\n",
        "data['class'].value_counts().plot.pie(autopct='%1.2f%%', ax=ax[0], shadow=True)\n",
        "ax[0].set_title('Class')\n",
        "ax[0].set_ylabel('')\n",
        "sns.countplot('class', data=data, ax=ax[1])\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 650,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BRktY_aHYZau",
        "colab_type": "text"
      },
      "source": [
        "Plotting Histograms:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GLfF4ITAmfWi",
        "colab_type": "code",
        "outputId": "77b60d56-42fa-44f7-e7ea-cfb678fc6c55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        }
      },
      "source": [
        "data.hist(figsize = (20, 12))\n",
        "plt.show()"
      ],
      "execution_count": 651,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x864 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aXDJ1ggNaNKT",
        "colab_type": "text"
      },
      "source": [
        "create swarmplot inside the violinplot for each class:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4eW90xDXWOOd",
        "colab_type": "code",
        "outputId": "f6f2ae1b-6183-4bf7-95cb-0c13bbacf50b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "\n",
        "\n",
        "plt.figure(figsize=(13,7))\n",
        "plt.subplot(121)\n",
        "sns.violinplot(x=\"class\", y=\"thalach\", data=data, inner=None)\n",
        "sns.swarmplot(x='class', y=\"thalach\", data=data, color='w', alpha=0.5)\n",
        "plt.subplot(122)\n",
        "sns.swarmplot(x='class', y='age', data=data)\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 652,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_L2ZOzq0akAf",
        "colab_type": "text"
      },
      "source": [
        "Heatmap Generation:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HmPpUampmpb_",
        "colab_type": "code",
        "outputId": "c20462e8-39c6-4b05-cec3-d50715761a8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "plt.figure(figsize = (14, 10)) \n",
        "sns.heatmap(data.corr(), cmap='Reds',annot=True, linecolor='Yellow', linewidths=2.0)\n",
        "plt.show()"
      ],
      "execution_count": 653,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "POytox6uaqyi",
        "colab_type": "text"
      },
      "source": [
        "catplot between sex and distribution of target class frequency based on sex:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZIMuBA7mnDYw",
        "colab_type": "code",
        "outputId": "351e229e-215b-462c-fe3a-f44ef385dbbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "source": [
        "#plt.figure(figsize = (50, 20)) \n",
        "\n",
        "sns.catplot(data=data, kind='count', x='sex',hue='class',height=5, aspect=2)\n",
        "plt.show()"
      ],
      "execution_count": 654,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 762.375x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HlJpZIMKciWi",
        "colab_type": "text"
      },
      "source": [
        "catplot between distribution of target class frequency based on number of major vessels (0-3) colored by flourosopy:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kW7CXah72Kv2",
        "colab_type": "code",
        "outputId": "1eacc618-af30-4a30-d567-1102953e10ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "source": [
        "\n",
        "sns.catplot(data=data, kind='count', x='ca',hue='class',height=6, aspect=2)\n",
        "plt.figure(figsize = (50, 20)) \n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 655,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 906.375x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3600x1440 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t4qjWwxMcwNN",
        "colab_type": "text"
      },
      "source": [
        "catplot between distribution of target class frequency based on chest pain type"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqaGEhB-ZfE3",
        "colab_type": "code",
        "outputId": "9c0126ef-53ef-4bff-be64-2ee3d9e21410",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "source": [
        "\n",
        "sns.catplot(data=data, kind='count', x='cp',hue='class',height=6, aspect=2)\n",
        "plt.xlabel(\"1: typical angina          2: atypical angina          3: non-anginal pain          4: asymptomatic\")\n",
        "plt.figure(figsize = (50, 20)) \n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 656,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 906.375x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3600x1440 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GKrZKeK5dw_2",
        "colab_type": "text"
      },
      "source": [
        "Scatter Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y2L1prglI9RF",
        "colab_type": "code",
        "outputId": "718970f1-c7ad-4e08-a052-3dce986689ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "# create another figure\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "\n",
        "# scatter with positive examples\n",
        "plt.scatter(data.age[data.sex==0],\n",
        "            data.trestbps[data.sex==0],\n",
        "            c=\"salmon\");\n",
        "\n",
        "# scatter with negative exaples\n",
        "plt.scatter(data.age[data.sex==1],\n",
        "            data.trestbps[data.sex==1],\n",
        "            c=\"lightblue\");\n",
        "plt.title(\"Scatter plot between Age and Max Heart Rate with respect to sex\")\n",
        "plt.xlabel(\"Age\")\n",
        "plt.ylabel(\"Max Heart Rate\")\n",
        "plt.legend([\"Male\", \"Female\"]);"
      ],
      "execution_count": 657,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4umfiDze5_w",
        "colab_type": "text"
      },
      "source": [
        "crosstab creation:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VcfNYDGBMKtz",
        "colab_type": "code",
        "outputId": "30fc7074-d19a-43aa-ded2-fd800f2fec53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "\n",
        "\n",
        "# Making it more visual\n",
        "\n",
        "pd.crosstab(data.cp,data.sex).plot(kind=\"bar\", figsize=(10,6), color=[\"salmon\",\"lightblue\"])\n",
        "\n",
        "plt.title(\"Distribution of Chest Pain type based on sex\")\n",
        "plt.xlabel(\"1: typical angina     2: atypical angina     3: non-anginal pain     4: asymptomatic\")\n",
        "plt.ylabel(\"No of people\")\n",
        "plt.legend([\"Male\", \"Female\"]);\n",
        "plt.xticks(rotation=0);\n",
        "\n"
      ],
      "execution_count": 658,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgOL055lM1vK",
        "colab_type": "code",
        "outputId": "74f34b3f-f376-41e1-9a04-67dc7be83ebc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "\n",
        "\n",
        "pd.crosstab(data.fbs, data.sex).plot(kind=\"bar\", figsize=(10,6), color=[\"salmon\",\"lightblue\"])\n",
        "plt.title(\"Distribution of Fasting sugar Frequency based on Sex\")\n",
        "plt.xlabel(\"0 = False, 1 = True\")\n",
        "plt.ylabel(\"No of people\")\n",
        "plt.legend([\"Female\",\"Male\"]);\n",
        "plt.xticks(rotation=0);\n",
        "\n"
      ],
      "execution_count": 659,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5/PDDeec737li/o022oh58+Zx1FFHMWfOHD75yU+yYMECTjvtNJYsWQLAqaeeyvz585k3bx4nn3zyivHLXXPNNZx99tn88Ic/5IorrmDKlCl8+ctfXud9sctSkiRNSEO7LBcsWMCCBQvYb7/9AHjggQfYeuutV0w/6KCDANhll12YNWvWimmPfexjWbhwIVOnTuXkk0/mvPPOA2DhwoVcd911TJ06dcU6vvvd7zJ//nye8pSnAE0ofNSjHrXO+2IgkyRJk0JVMWvWLC655JJhp2+88cYAPOQhD1nxfPnwsmXLuOiii/jOd77DJZdcwmabbcY+++yz0u0rqoq5c+fy/ve/f1RrN5BpvfG1a2/vuoRx50U7br36mSRpgthxxx1ZvHgxl1xyCU972tNYunQpv/zlL5k1a1Zfy99111084hGPYLPNNuMXv/gFl1566Urz7LvvvsyZM4djjjmGRz3qUdx5553cc889bLfddutUu+eQSZKkSWGjjTbi3HPP5e1vfzu77bYbu+++Oz/60Y/6Xn7//fdn2bJl7LTTThx77LHsvffeK82z88478773vY/nPe957Lrrruy3334rLhxYF6mqdV5JV2bPnl3z5s3rugxNELaQrcwWMklr65prrmGnnXbquoxxa7jjk2R+Vc0ebn5byCRJkjpmIJMkSeqYgUySJKljBjJJkqSOGcgkSZI6ZiCTJEnqmIFMkiRNSEk47LDDVgwvW7aMadOmceCBB65yuYsuumi184w179QvSZLW2Wjf67Gf+yRuvvnmLFiwgPvuu49NN92UCy+8kOnTp49qHWPFFjJJkjRhHXDAAXzjG98A4Mwzz+TlL3/5immXXXYZT3va09hjjz14+tOfzrXXXrvS8vfeey+HH344e+21F3vssQfnn3/+mNXey0AmSZImrEMOOYSzzjqL+++/nyuvvJKnPvWpK6Y98YlP5OKLL+byyy/nve99L+94xztWWv6kk07iOc95Dpdddhnf+973eOtb38q99947lrsA2GUpSZImsF133ZWbbrqJM888kwMOOOBB0+666y7mzp3LddddRxKWLl260vLf/va3ueCCC/jwhz8MwP33388tt9wy5l8LZSCTJEkT2kEHHcRb3vIWLrroIpYsWbJi/AknnMCzn/1szjvvPG666Sb22WeflZatKr761a+y4447jmHFKxtYl2WSU5PckWTBkPFvSPKLJFcl+Yee8ccluT7JtUmeP6i6JEnS5HL44Ydz4oknsssuuzxo/F133bXiJP/TTjtt2GWf//zn80//9E9UFQCXX375QGsdySDPITsN2L93RJJnA3OA3apqFvDhdvzOwCHArHaZTyWZMsDaJEnSJDFjxgyOPvrolca/7W1v47jjjmOPPfZg2bJlwy57wgknsHTpUnbddVdmzZrFCSecMOhyh5XliXAgK09mAl+vqie1w+cAp1TVd4bMdxxAVb2/Hf4W8O6qumRV6589e3bNmzdvAJVrMhrtS7Ing34uK5ek4VxzzTVjfp7VRDLc8Ukyv6pmDzf/WF9l+QTgmUl+nOT7SZ7Sjp8OLOyZb1E7biVJjkwyL8m8xYsXD7hcSZKkwRvrQLYB8Ehgb+CtwDlJsiYrqKpTqmp2Vc2eNm3aIGqUJEkaU2MdyBYBX6vGZcCfga2AW4Fte+ab0Y6TJEma9MY6kP0r8GyAJE8ANgJ+A1wAHJJk4yTbAzsAl41xbZIkaQ0M8jz0iWxtjsvA7kOW5ExgH2CrJIuAE4FTgVPbW2H8CZhbTdVXtSf8Xw0sA15XVQ8MqjZJkrRuNtlkE5YsWcLUqVNZw7OPJrWqYsmSJWyyySZrtNzAAllVvXyESYcNN7KqTgJOGlQ9kiRp9MyYMYNFixbhBXYr22STTZgxY8YaLeOd+iVJ0hrbcMMN2X777bsuY9Lwy8UlSZI6ZiCTJEnqmIFMkiSpYwYySZKkjhnIJEmSOmYgkyRJ6piBTJIkqWMGMkmSpI4ZyCRJkjpmIJMkSeqYgUySJKljBjJJkqSOGcgkSZI6ZiCTJEnqmIFMkiSpYwYySZKkjhnIJEmSOmYgkyRJ6piBTJIkqWMGMkmSpI4ZyCRJkjpmIJMkSeqYgUySJKljBjJJkqSOGcgkSZI6ZiCTJEnqmIFMkiSpYwMLZElOTXJHkgXDTHtzkkqyVTucJCcnuT7JlUn2HFRdkiRJ480gW8hOA/YfOjLJtsDzgFt6Rr8A2KF9HAl8eoB1SZIkjSsDC2RV9QPgzmEmfQx4G1A94+YAX6zGpcCWSbYeVG2SJEnjyZieQ5ZkDnBrVf1syKTpwMKe4UXtuOHWcWSSeUnmLV68eECVSpIkjZ0xC2RJNgPeAbxrXdZTVadU1eyqmj1t2rTRKU6SJKlDG4zhth4HbA/8LAnADOCnSfYCbgW27Zl3RjtOkiRp0huzFrKq+nlVPaqqZlbVTJpuyT2r6lfABcAr26st9wbuqqrbx6o2SZKkLg3ythdnApcAOyZZlOSIVcz+TeAG4Hrgs8DfDaouSZKk8WZgXZZV9fLVTJ/Z87yA1w2qFkmSpPHMO/VLkiR1zEAmSZLUMQOZJElSxwxkkiRJHTOQSZIkdcxAJkmS1DEDmSRJUscMZJIkSR0zkEmSJHXMQCZJktQxA5kkSVLHDGSSJEkdM5BJkiR1zEAmSZLUMQOZJElSxwxkkiRJHTOQSZIkdcxAJkmS1DEDmSRJUscMZJIkSR0zkEmSJHXMQCZJktQxA5kkSVLHDGSSJEkdM5BJkiR1zEAmSZLUMQOZJElSxwYWyJKcmuSOJAt6xn0oyS+SXJnkvCRb9kw7Lsn1Sa5N8vxB1SVJkjTeDLKF7DRg/yHjLgSeVFW7Ar8EjgNIsjNwCDCrXeZTSaYMsDZJkqRxY2CBrKp+ANw5ZNy3q2pZO3gpMKN9Pgc4q6r+WFU3AtcDew2qNkmSpPGky3PIDgf+vX0+HVjYM21RO06SJGnS6ySQJXknsAz48lose2SSeUnmLV68ePSLkyRJGmNjHsiSvAo4EDi0qqodfSuwbc9sM9pxK6mqU6pqdlXNnjZt2kBrlSRJGgtjGsiS7A+8DTioqv7QM+kC4JAkGyfZHtgBuGwsa5MkSerKBoNacZIzgX2ArZIsAk6kuapyY+DCJACXVtVRVXVVknOAq2m6Ml9XVQ8MqjZJkqTxZGCBrKpePszoz69i/pOAkwZVjyRJ0njlnfolSZI6ZiCTJEnqmIFMkiSpYwYySZKkjhnIJEmSOmYgkyRJ6piBTJIkqWMGMkmSpI4ZyCRJkjpmIJMkSeqYgUySJKljBjJJkqSOGcgkSZI6ZiCTJEnqmIFMkiSpYwYySZKkjhnIJEmSOmYgkyRJ6piBTJIkqWMGMkmSpI4ZyCRJkjrWVyBL8ldJXt0+n5Zk+8GWJUmStP5YbSBLciLwduC4dtSGwBmDLEqSJGl90k8L2QuBg4B7AarqNmCLQRYlSZK0PuknkP2pqgoogCSbD7YkSZKk9Us/geycJJ8Btkzyf4DvAJ8dbFmSJEnrjw1WN0NVfTjJfsDdwI7Au6rqwoFXJkmStJ5YbSADaAOYIUySJGkARuyyTHJPkruHedyT5O7VrTjJqUnuSLKgZ9wjk1yY5Lr25yPa8UlycpLrk1yZZM/R2T1JkqTxb8RAVlVbVNXDhnlsUVUP62PdpwH7Dxl3LPDdqtoB+G47DPACYIf2cSTw6TXdEUmSpImq3xvD7pnk6CRvSLJHP8tU1Q+AO4eMngOc3j4/HTi4Z/wXq3EpzQUEW/ezHUmSpImunxvDvosmPE0FtgJOS3L8Wm7v0VV1e/v8V8Cj2+fTgYU98y1qxw1Xz5FJ5iWZt3jx4rUsQ5Ikafzo56T+Q4Hdqup+gCQfAK4A3rcuG66qSlJrsdwpwCkAs2fPXuPlJUmSxpt+uixvAzbpGd4YuHUtt/fr5V2R7c872vG3Atv2zDdjHbYhSZI0ofQTyO4CrkpyWpIvAAuA37VXRZ68htu7AJjbPp8LnN8z/pXt1ZZ7A3f1dG1KkiRNav10WZ7XPpa7qJ8VJzkT2AfYKski4ETgAzR3/j8CuBl4aTv7N4EDgOuBPwCv7mcbkiRJk0E/d+o/PclGwBPaUddW1dI+lnv5CJP2HWbeAl63unVKkiRNRqsNZEn2obnK8iYgwLZJ5ra3tZAkSdI66qfL8iPA86rqWoAkTwDOBJ48yMIkSZLWF/2c1L/h8jAGUFW/BDYcXEmSJEnrl35ayOYl+RxwRjt8KDBvcCVJkiStX/oJZK+lOeH+6Hb4YuBTA6tIkiRpPdPPVZZ/TPJp4Bu9XZeSJEkaHf18l+VBNF+V9B/t8O5JLhh0YZIkSeuLfk7qPxHYC/gdQFVdAWw/yKIkSZLWJ/0EsqVVddeQcX6ptyRJ0ijp56T+q5K8ApiSZAeak/t/NNiyJEmS1h/9tJC9AZgF/JHmhrB3A/93kEVJkiStT/q5yvIPwDuTfLAZrHsGX5YkSdL6o5+rLJ+S5OfAlcDPk/wsiV+bJEmSNEr6OYfs88DfVdXFAEn+CvgCsOsgC5MkSVpf9HMO2QPLwxhAVf0XsGxwJUmSJK1f+mkh+36Sz9Cc0F/Ay4CLkuwJUFU/HWB9kiRJk14/gWy39ueJQ8bvQRPQnjOqFUmSJK1n+rnK8tljUYgkSdL6qp9zyCRJkjRABjJJkqSOjRjIkryk/ekXiUuSJA3QqlrIjmt/fnUsCpEkSVpfreqk/iVJvg1sn+SCoROr6qDBlSVJkrT+WFUg+2tgT+BLwEfGphyNhqXveXPXJYxPh7yl6wokSRrWiIGsqv4EXJrk6VW1OMlD2/G/H7PqJEmS1gP9XGX56CSXA1cBVyeZn+RJA65LkiRpvdFPIDsFeFNVbVdVjwHe3I6TJEnSKOgnkG1eVd9bPlBVFwGbr8tGkxyT5KokC5KcmWSTJNsn+XGS65OcnWSjddmGJEnSRNFPILshyQlJZraP44Eb1naDSaYDRwOzq+pJwBTgEOCDwMeq6vHAb4Ej1nYbkiRJE0k/gexwYBrwNZp7km3VjlsXGwCbJtkA2Ay4neZLys9tp58OHLyO25AkSZoQ+vly8d/StGiNiqq6NcmHgVuA+4BvA/OB31XVsna2RcD00dqmJEnSeDbm32WZ5BHAHGB7YBua89H2X4Plj0wyL8m8xYsXD6hKSZKksdPFl4s/F7ixqhZX1VKartBnAFu2XZgAM4Bbh1u4qk6pqtlVNXvatGljU7EkSdIAdRHIbgH2TrJZkgD7AlcD3wNe3M4zFzi/g9okSZLG3GoDWZIZSc5LsjjJHUm+mmTG2m6wqn5Mc/L+T4GftzWcArwdeFOS64GpwOfXdhuSJEkTyWpP6ge+APwL8JJ2+LB23H5ru9GqOhE4ccjoG4C91nadkiRJE1U/XZbTquoLVbWsfZxGcxsMSZIkjYJ+AtmSJIclmdI+DgOWDLowSZKk9UW/N4Z9KfArmhu4vhh49SCLkiRJWp/0c2PYm4GDxqAWSZKk9dKIgSzJu1axXFXV/xtAPZIkSeudVbWQ3TvMuM1pvvR7KmAgkyRJGgUjBrKq+sjy50m2AN5Ic+7YWcBHRlpOkiRJa2aV55AleSTwJuBQ4HRgz/bLxiVJkjRKVnUO2YeAF9HcRX+Xqvr9mFUlSZK0HlnVbS/eDGwDHA/cluTu9nFPkrvHpjxJkqTJb1XnkHXxxeOSJEnrHUOXJElSxwxkkiRJHTOQSZIkdcxAJkmS1DEDmSRJUscMZJIkSR0zkEmSJHXMQCZJktQxA5kkSVLHDGSSJEkdM5BJkiR1zEAmSZLUMQOZJElSxwxkkiRJHTOQSZIkdcxAJkmS1DEDmSRJUsc6CWRJtkxybpJfJLkmydOSPDLJhUmua38+oovaJEmSxlpXLWQfB/6jqp4I7AZcAxwLfLeqdgC+2w5LkiRNemMeyJI8HPifwOcBqupPVfU7YA5wejvb6cDBY12bJElSF7poIdseWAx8IcnlST6XZHPg0VV1ezvPr4BHD7dwkiOTzEsyb/HixWNUsiRJ0uB0Ecg2APYEPl1VewD3MqR7sqoKqOEWrqpTqmp2Vc2eNm3awIuVJEkatC4C2SJgUVX9uB0+lyag/TrJ1gDtzzs6qN34LcAAAAoNSURBVE2SJGnMjXkgq6pfAQuT7NiO2he4GrgAmNuOmwucP9a1SZIkdWGDjrb7BuDLSTYCbgBeTRMOz0lyBHAz8NKOapMkSRpTnQSyqroCmD3MpH3HuhZJkqSuead+SZKkjhnIJEmSOmYgkyRJ6piBTJIkqWMGMkmSpI4ZyCRJkjpmIJMkSeqYgUySJKljBjJJkqSOGcgkSZI6ZiCTJEnqmIFMkiSpYwYySZKkjhnIJEmSOmYgkyRJ6piBTJIkqWMGMkmSpI4ZyCRJkjpmIJMkSeqYgUySJKljBjJJkqSOGcgkSZI6ZiCTJEnqmIFMkiSpYwYySZKkjhnIJEmSOmYgkyRJ6lhngSzJlCSXJ/l6O7x9kh8nuT7J2Uk26qo2SZKksdRlC9kbgWt6hj8IfKyqHg/8Fjiik6okSZLGWCeBLMkM4K+Bz7XDAZ4DnNvOcjpwcBe1SZIkjbWuWsj+EXgb8Od2eCrwu6pa1g4vAqZ3UZgkSdJYG/NAluRA4I6qmr+Wyx+ZZF6SeYsXLx7l6iRJksZeFy1kzwAOSnITcBZNV+XHgS2TbNDOMwO4dbiFq+qUqppdVbOnTZs2FvVKkiQN1JgHsqo6rqpmVNVM4BDgP6vqUOB7wIvb2eYC5491bZIkSV0YT/chezvwpiTX05xT9vmO65EkSRoTG6x+lsGpqouAi9rnNwB7dVmPJElSF8ZTC5kkSdJ6yUAmSZLUMQOZJElSxwxkkiRJHTOQSZIkdcxAJkmS1DEDmSRJUscMZJIkSR0zkEmSJHXMQCZJktQxA5kkSVLHOv0uS0mSJrqvXXt71yWMOy/aceuuS5hwbCGTJEnqmIFMkiSpYwYySZKkjhnIJEmSOmYgkyRJ6piBTJIkqWMGMkmSpI4ZyCRJkjpmIJMkSeqYgUySJKljBjJJkqSOGcgkSZI6ZiCTJEnqmIFMkiSpYwYySZKkjhnIJEmSOjbmgSzJtkm+l+TqJFcleWM7/pFJLkxyXfvzEWNdmyRJUhe6aCFbBry5qnYG9gZel2Rn4Fjgu1W1A/DddliSJGnSG/NAVlW3V9VP2+f3ANcA04E5wOntbKcDB491bZIkSV3o9ByyJDOBPYAfA4+uqtvbSb8CHj3CMkcmmZdk3uLFi8ekTkmSpEHqLJAleSjwVeD/VtXdvdOqqoAabrmqOqWqZlfV7GnTpo1BpZIkSYPVSSBLsiFNGPtyVX2tHf3rJFu307cG7uiiNkmSpLHWxVWWAT4PXFNVH+2ZdAEwt30+Fzh/rGuTJEnqwgYdbPMZwN8CP09yRTvuHcAHgHOSHAHcDLy0g9okSZLG3JgHsqr6LyAjTN53LGuRJEkaD7xTvyRJUscMZJIkSR0zkEmSJHXMQCZJktQxA5kkSVLHDGSSJEkdM5BJkiR1zEAmSZLUMQOZJElSx7r46iRJ0gS09D1v7rqE8emQt3RdgSYBW8gkSZI6ZiCTJEnqmIFMkiSpYwYySZKkjhnIJEmSOmYgkyRJ6piBTJIkqWMGMkmSpI4ZyCRJkjpmIJMkSeqYgUySJKljBjJJkqSOGcgkSZI6ZiCTJEnqmIFMkiSpYwYySZKkjhnIJEmSOjbuAlmS/ZNcm+T6JMd2XY8kSdKgjatAlmQK8EngBcDOwMuT7NxtVZIkSYM1rgIZsBdwfVXdUFV/As4C5nRckyRJ0kCNt0A2HVjYM7yoHSdJkjRpbdB1AWsqyZHAke3g75Nc22U9mkDe/dGtgN90XYakSca/LerfdiNNGG+B7FZg257hGe24FarqFOCUsSxKk0OSeVU1u+s6JE0u/m3RaBhvXZY/AXZIsn2SjYBDgAs6rkmSJGmgxlULWVUtS/J64FvAFODUqrqq47IkSZIGalwFMoCq+ibwza7r0KRkV7ekQfBvi9ZZqqrrGiRJktZr4+0cMkmSpPWOgUyTyuq+eivJxknObqf/OMnMsa9S0kST5NQkdyRZMML0JDm5/dtyZZI9x7pGTWwGMk0afX711hHAb6vq8cDHgA+ObZWSJqjTgP1XMf0FwA7t40jg02NQkyYRA5kmk36+emsOcHr7/Fxg3yQZwxolTUBV9QPgzlXMMgf4YjUuBbZMsvXYVKfJwECmyaSfr95aMU9VLQPuAqaOSXWSJjO/+k/rxEAmSZLUMQOZJpPVfvVW7zxJNgAeDiwZk+okTWb9/P2RRmQg02TSz1dvXQDMbZ+/GPjP8mZ8ktbdBcAr26st9wbuqqrbuy5KE8e4u1O/tLZG+uqtJO8F5lXVBcDngS8luZ7mBN1DuqtY0kSR5ExgH2CrJIuAE4ENAarqn2m+YeYA4HrgD8Cru6lUE5V36pckSeqYXZaSJEkdM5BJkiR1zEAmSZLUMQOZJElSxwxkkiRJHTOQSRqYJPsnuTbJ9UmOHYX1zUxyX5Ireh4brWLeBeu6zSHrfEmSq5L8OcnsUVjfJ9t9uHrIfr14NOqVNHF4HzJJA5FkCvBJYD+a7/X7SZILqurqdVz1f1fV7utc4NpZALwI+MxorKyqXgdNeAS+PnS/kmzQfueqpEnOFjJJg7IXcH1V3VBVfwLOAuaM9kbalrCLk/y0fTx9mHlmJbmsbX26MskO7fjDesZ/pg2RI6qqa6rq2tHehyG17tPuzwXA1UNb+pK8Jcm72+ePS/IfSea3yzxxkLVJGhxbyCQNynRgYc/wIuCpQ2dK8lbg0GGW/0FVHT3M+McluaJ9/kPgrcB+VXV/G7TOBIZ2Jx4FfLyqvtx2cU5JshPwMuAZVbU0yafaOr7Y/y6uLMkWwMUjTH5Fny2EewJPqqob29azkZwCHFVV1yV5KvAp4DlrUq+k8cFAJqlTVfUh4ENrsMiDuiyTPBz4RJLdgQeAJwyzzCXAO5PMAL7WBph9gSfTdKUCbArcsZa7sUJV3QOsa5fqZVV146pmSPJQ4OnAV9r6ATZex+1K6oiBTNKg3Aps2zM8ox33IGvRQjbUMcCvgd1oTsO4f+gMVfUvSX4M/DXwzSSvAQKcXlXH9bGNvo1SC9m9Pc+X8eDTSzZpfz4E+F2H59NJGkUGMkmD8hNghyTb0wSxQ4BXDJ1pLVrIhno4sKiq/pxkLs0Xyz9IkscCN1TVyUkeA+wKfBs4P8nHquqOJI8Etqiqm5N8EfhEVV22psWMUgtZr18Dj0oyFfg9cCDwH1V1d5Ibk7ykqr6Sppls16r62ShuW9IY8aR+SQPRXh34euBbwDXAOVV11QA29SlgbpKfAU/kwa1Ly70UWNCee/Yk4IttS9XxwLeTXAlcCGzdzr8rcNvQlSR5YZJFwNOAbyT51qjvzRBVtRR4L3BZW+MveiYfChzR7vtVDOCiCUljI1XVdQ2SNG4keRjw+ap6Sde1SFp/GMgkSZI6ZpelJElSxwxkkiRJHTOQSZIkdcxAJkmS1DEDmSRJUscMZJIkSR0zkEmSJHXs/wOIyPEhk+k8oQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_hKTgUVNi9T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mNoJo7K3fwAT",
        "colab_type": "text"
      },
      "source": [
        "Plotting catplot with two variables:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5hfMXqWnuVc",
        "colab_type": "code",
        "outputId": "6e3ca9d9-30d6-45f1-dc97-045b94399929",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "sns.catplot(data=data, kind='count', x='class', col='sex',row='slope', palette='Reds')\n",
        "plt.show()"
      ],
      "execution_count": 660,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x1080 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qrIb3VL-53jX",
        "colab_type": "code",
        "outputId": "27f62cc0-2e27-4d3f-dd9f-522bc7000faa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        }
      },
      "source": [
        "sns.catplot(data=data, kind='count', x='class', col='sex',row='exang', palette='Blues')\n",
        "plt.show()"
      ],
      "execution_count": 661,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zc3qFRtwgHC1",
        "colab_type": "text"
      },
      "source": [
        "Plotting countplot:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pEfogAr8S7a8",
        "colab_type": "code",
        "outputId": "1ff3cc8e-4f31-4d9c-f698-104c03a9f15b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.countplot(data[\"ca\"])"
      ],
      "execution_count": 662,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe57908bb38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 662
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "St5Ad4IZgAom",
        "colab_type": "code",
        "outputId": "0dbab7c9-7934-448d-ad4c-7cfd06d2b2be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.countplot(data[\"sex\"])"
      ],
      "execution_count": 663,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe579027908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 663
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ANoGU4dzgPKy",
        "colab_type": "code",
        "outputId": "dcab9b97-107a-4a73-d1f2-0490e478e6bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.countplot(data[\"fbs\"]) "
      ],
      "execution_count": 664,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe578ff0ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 664
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oSMfOpiQvTPIK5i/sdnOSVwEPVtWngL8B/F5hLWuePpKOnduArwMrgD+rqv9Mshl4b5L/BX4A+ExBy5ovNEuSGk8fSZIaoyBJaoyCJKkxCpKkxihIkhqjIElqjIIkqfk/MSd/Mhc4wEQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "36ZrGGCdgTHK",
        "colab_type": "code",
        "outputId": "a73bbe3c-9f8d-4ddb-b599-005dc54d047f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.countplot(data[\"restecg\"])"
      ],
      "execution_count": 665,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe578f59828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 665
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AHN6JEcPgfAY",
        "colab_type": "code",
        "outputId": "752fe8cb-50dc-4985-b0f4-a6d318c6da73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.countplot(data[\"exang\"])"
      ],
      "execution_count": 666,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe578f0eeb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 666
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J8vUJ9TYNDgf+u+8mloO865S+W9A9+bu54K25Pz7lsXsaWG6hsFdVtQ5Y13cfB6Ik81U113cf0u783Zyc5Xb6aBtw9ND6UV1NkjQByy0UvgysSvK4JA8BTgLW99yTJE2NZXX6qKp2JTkd+FfgIOD8qrq257amiafltFz5uzkhqaq+e5AkLRPL7fSRJKlHhoIkqTEUptDephJJ8tAkl3TjX0wyO/kuNY2SnJ9kR5LNexhPkvd0v5tXJ3n6pHs80BkKU2bEqUROBb5TVT8LnA38zWS71BS7ADhhifETgVXday1wzgR6miqGwvQZZSqRNcCF3fLHgeOT3C9fo5SWUlVXArctscka4KIauAo4NMmKyXQ3HQyF6bPYVCJH7mmbqtoF3A48aiLdSUsb5fdX+8FQkCQ1hsL0GWUqkbZNkoOBnwZunUh30tKcCmfMDIXpM8pUIuuBhWlCXw78W/ktRy0P64FXd3chPQu4vaq2993UgWRZTXOh8dvTVCJJzgTmq2o9cB7wD0m2MLjod1J/HWuaJLkYOA44PMlW4K3AgwGq6gPA5cCLgC3AD4DX9tPpgctpLiRJjaePJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgrQXSV6Z5EtJNiX5YJJndnP5PyzJw5Ncm+QpSQ5JsiHJV5Jck2RNt/9skuuTnNtt+5kkP9GN/WL3WZuSvHNPzxGQJsVQkJaQ5OeA3waeXVWrgbuAJzGYbuGvgL8F/rGqNgM/BF5aVU8HngecNTTl+Crg/VX1ZOC7wG929Q8Bpw19ttQrp7mQlnY88Azgy93f958AdgBnMphH6ofAH3TbBvjrJM8F7mYwpfMR3dg3q2pTt7wRmE1yKPCIqvpCV/8I8OLx/nOkpRkK0tICXFhVb75HcfBgl0MYzMvzMOD7wO8AM8AzqupHSW7qxgDuHNr9LgbhIi07nj6SlrYBeHmSRwMkeWSSxwIfBP4C+DA/flzpTwM7ukB4HvDYpT64qr4L3JHkmV3JiQfVO48UpCVU1XVJ/hz4TJIHAT8CPgX8qKo+0j3z+vNJns8gID6d5BpgHvjaCD/iVODcJHcDn2XwlDupN86SKvUoySFV9T/d8hnAiqp6Y89taYp5pCD169eSvJnB/8Wbgdf0246mnUcKkqTGC82SpMZQkCQ1hoIkqTEUJEmNoSBJav4fSe2TqW9+bIIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5nzunYK8g1ZB",
        "colab_type": "code",
        "outputId": "0f41d2c0-f5f8-4d68-891d-467343628273",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.countplot(data[\"slope\"])"
      ],
      "execution_count": 667,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe578eee780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 667
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R13nJ4bohCod",
        "colab_type": "code",
        "outputId": "5e7f25f5-34f2-4910-f9df-6b0c77c1fbf0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.countplot(data[\"thal\"])"
      ],
      "execution_count": 668,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe578e49240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 668
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3cLfxAu3fdAD",
        "colab_type": "text"
      },
      "source": [
        "Plotting univariate distributions:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xrqkB5oPhBpS",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rh2-Ck4BRa4s",
        "colab_type": "code",
        "outputId": "b837a0fa-1d58-4bab-df8c-270af8c47ed2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.distplot(data[\"cp\"])"
      ],
      "execution_count": 669,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe578e220f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 669
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KKwc4jmKa6Bd",
        "colab_type": "code",
        "outputId": "25fddd99-9faa-42b5-aa8f-1c6dab1f3a01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.distplot(data[\"ca\"])"
      ],
      "execution_count": 670,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe578db63c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 670
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFv8ZwQj4aiq",
        "colab_type": "code",
        "outputId": "f36224a7-9270-437b-e98c-e6c01a395dfe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.distplot(data[\"thal\"])"
      ],
      "execution_count": 671,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe578d34080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 671
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "em73ZXcMaxyn",
        "colab_type": "code",
        "outputId": "bd4b938b-3f81-47f4-a76c-04747db6c151",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.distplot(data[\"slope\"])"
      ],
      "execution_count": 672,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe578ca2588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 672
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DiL4oOp0boeA",
        "colab_type": "code",
        "outputId": "8cfefb18-9dea-4c37-e03d-45f6c909e768",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.distplot(data[\"oldpeak\"])"
      ],
      "execution_count": 673,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe578e66a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 673
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1XpclIlEU6BewbhCNn8fi5inDWfbZa5k2KoMHn36D//2brXR0aQhGpD9QoF/A+qpjZKYmMD4vzetS+o2RGSk8+cm53HfdGH722n4+/MN1NLS2e12WyKCnQL+A9VUNzCnO1il7PYSCAf7x3ZN5+K4ZbDpwnPd/dy3VDSe9LktkUNNpi+dR29zGvmMnuXfeaK9L8dz5zof/+NXF/PTV/bz7kdV8bH7xOacPuGduUW+VJyKoh35efz7/fHAfEL2Q0TlDuO+6MQQCxuOr9rK3XmfAiHghpkA3s4VmVmFmlWb2pbOsf9DMtpvZFjN70cx80aVdV3WMtKQQU0ame11Kvzc8PZn7rxtDekoCP167j22HmrwuSWTQuWCgm1kQeAy4BZgCLDazKT2abQZKnXPTgWeAb8S7UC+sr2pg9ugsQprLJCaZqYncf+0YRmYk8+S6A5Tva/C6JJFBJZakKgMqnXN7nXPtwFPA7dENnHMrnHNnjoi9BhTEt8y+19Dazq7aExpuuUipSSE+cc0Yxg9P49nNB3l17zGvSxIZNGIJ9HygOup1TWTZuXwC+MPZVpjZfWZWbmbldXV1sVfpgVf3dAfRVWN1/vnFSgwFuHfuaCaPTOe3bxxi9e7+/W8t4hdxHUsws3uBUuA/zrbeOfe4c67UOVeam9u/p6JdU1nP0KQQ0zUZ1SUJBQPcU1bEFfkZ/GHrEV7aedTrkkR8L5ZAPwgURr0uiCx7GzO7CfgnYJFz7nR8yvPOK3vqmTsmW+PnlyEYMO4qLWRmYSYv7Kjlm8srcM55XZaIb8WSVhuA8WZWYmaJwN3A0ugGZjYT+D7dYT7gu2I1jSfZf+wk88cN87qUAS8YMO6YXcCc4mweXVHJv/9+h0JdpJdc8MIi51ynmT0ALAeCwI+cc9vM7CtAuXNuKd1DLGnAryMzEh5wzi3qxbp71SuV3ePnCvT4CJjx3itHMXVUOk+sqeJ0Z5j/s2iqrr4VibOYrhR1zi0DlvVY9lDU85viXJen1lTWkzs0SfO3xJGZ8S/vmUJyQpD/enkPbR1dfO2O6QQV6iJxo0v/e3DO8cqeY8wflzMo5z/vTWbGFxdOJDkhwLdf2E17V5j//MAMHacQiRMFeg+7ak9Qf+I088dquKU3mBmfu2kCSaEgX39+J6c7wnxn8UwSQwp1kculn6Ie1lTWAzB/vAK9N33qhrE8dNsUnt92hL/5+UbaOrq8LklkwFOg97Cy4ihjcoeQn3n2GQMlfv7qmhL+/X3TeGnnUT7503JOtSvURS6HAj1K6+lO1u1t4MaJeV6XMmh8aO5ovvmBGaytrGfxD16j/sSAv4RBxDMK9ChrK+tp7wpz4yQFel+6c3YB37t3NjuPNPP+777CnjpNvytyKRToUVZUHCUtKURpsSbk6mvvmjqCp+67ipPtnbzvsbWsrBjw16eJ9DkFeoRzjhU767h2/DCdceGRKwszee5/zSc/K5WP/3gD311ZqatKRS6Ckiti++FmjjS3sUDDLZ4qzE5lyaeu4tYrRvKN5yv4xE/KOaZxdZGYKNAjVkRmA7xhYv+eBXIwSE0M8f8Wz+TL75nCmt313PLIalbt0hS8IheiQI94aedRphdkkDc02etShO4LkD42v4TffHo+Q5NDfORH6/nCM2/QdLLD69JE+i0FOnC46RSbDhzn5snDvS5FepgyKp3f/+21fOqGsSzZdJB3PPwyT5dXEw5rbF2kJwU68PsthwG4bcYojyuRs0lOCPLFhZP4n0/PpzA7hS88s4XbH1vLK5X1OmgqEkVzuQC/23KYqaPSKRk2xOtS5Dym5Wdw56wCxuelsXxbLfc8sY6SYUO4cVIeY4YNiXkytXvmFvVypSLeGPSBXt1wkterj/PFhZO8LsX3nlx34LK3YWZcWZjF1FEZbNjXwMsVdfxwTRUjM5K5emwO0wsySdDsjTJIDfpA/92Z4ZbpIz2uRC5GQjDA1WOHMac4m9erj7O2sp4lmw7y/NYjlJXkUFaSTUZKgtdlivQpBfqWQ8wozKQwO9XrUuQSJAQDzCnOpnR0FnvqWnllTz0rK46ysuIo44enMasoi8kj09Vrl0FhUAf63roTbDvUzD/fOtnrUuQymRnj8tIYl5fGsROn2XSgkU0HjvPUhmqSEwJML8hkdlEWBVmaRVP8a1AH+q/KqwkGjNum6+wWP8lJS+LmKSN4x+Th7K1r7Q73/Y2sr2ogd2gSLac7ef/MfPLSdc2B+MugDfTTnV38uryGmybnMSJDP9h+FIjqtS+aMYo3DzaxcX8jX/vDTr7x/E6un5DLHbMLuGnycJITgl6XK3LZBm2gP7/1CA2t7dw7b7TXpUgfSE4IMqc4mznF2cwbk82STTU8u+kgDzy5maHJIW6bPoo7ZuUze3SW7iUrA9agDfSfv7af4pxU3Tt0EBqTm8bfv2sSD948kVf3HOPZTTX8ZvNBfrn+AKNzUnn/zALePytfB8plwBmUgb7zSDMb9jXyj++eRCCg3thgFQwY14wfxjXjh/GV93by/NYjLNlYw7de2MW3XthFWUk2d84q4JYrRjA0WadASv83KAP9Z6/uJzEU4AOzC70uRfqJtKQQd84u4M7ZBdQ0nuQ3mw+yZNNBvrBkCw8t3cq7po7gjlkFzB83jKA6AdJPDbpAP3T8FL8ur+GO2flkDUn0uhzphwqyUnngxvF8esE4NlcfZ8nGGn77xiH+5/VDDE9P4r0z81k0YxRTRqZrvF36lUEX6I+uqMTheODG8V6XIv2cmTGrKItZRVk89J4pvLjjKM9uquGJ1VV8/+W9FGWnsnDaCBZOG8GVBZkavhPPDapAr244ydMbqllcVkR+pi4wGawuZ06ZGycNp6wkhx2Hmtl2uIkfrq7i8VV7SU8OMWVUBlNHpVOcMySmYRlNEibxNqgC/Tsv7iYQMD69YJzXpcgAlpYUYk5JNnNKsjnV3sXOI81sO9TMxv0NvLb3GCkJQcblpTFh+FAmDE/TAVXpM4Mm0LcebOLZzQf56FXFupBI4iYlMcjMoixmFmXR3hmmoraFiiMt7K5t4c2DTQCMykhm/PChTBg+lKLsVB1UlV4zKAK9vTPM3z+zhewhifztO9Q7l96RGApwRX4GV+RnEHaOI01t7KptYVdtC6t31/HyrjqSEwKMy+3uvd84SVcpS3wNikD/3so97DjczOMfnk1mqs5skd4XMGNUZgqjMlO4YWIep9q7qKw7we5IwG891Myzmw8ycfhQrp+Yy3Xjc5lTkkVSSFMQyKXzfaDvONzMoyt2s2jGKN45dYTX5cgglZIYfKv37pzjSHMbaUkhXt5Vx4/X7uPxVXtJSQgyb0w210/I5fqJeRTnpOq0SLkovg702uY2/von5WSmJvLlRVO9LkcE6D4dcmRGCvfMLeL+68fSerqT1/YeY9Wu7mGZFRV18NvtFGandIf7hDyuGptDWpKvf1wlDnz7P6SlrYOP/fcGGk+28/T9V5Gti4ikn+l5+uTEEelMHJHOsROn2X30BLtqW3h6Qw0/f+0AQTOKclKZkJfG+OFDGZmRjJnp1Ed5G18GetOpDu7/WTm7alv44UdLmZaf4XVJIjHLSUsiJy2JeWNy6AyH2X/sJLtrT7D7aAvLt9eyfHstQ5NCjMtLIzUxyPxxw8gdmuR12dIP+C7Q99Sd4JM/Kae68ST/+YEZ3DAxz+uSRC5ZKBBgbG4aY3PTWMgImts6qKw9wa6jLVTUtvC5X70OwJSR6Vw7fhjXjs+ltDhL87sPUr4J9K6w46kNB/jaH3aSGAzwi7+eR1lJttdlicRVenICs0ZnMWt0FmHnmFGQyarddazeXceP1lbx/VV7SQoFKCvJZt6YHEpHZzGjMFMBP0gM+EDv7AqzoqKO//xjBTuPtFBWks3Dd82gIEtzWYu/Bcy4oiCDKwoy+PSCcbSe7mR9VQOrdtextrKe/1heAUBC0JiWn8HsoiymjEpn8sh0xuamkRjSjbP9JqZAN7OFwCNAEHjCOfe1HuuTgJ8Cs4FjwAedc/viW+qftXV0sWl/I6t21/PsphqOtpwmPzOF735oFrdMG6FTvWRQGpIUYsGkPBZM6h5mPH6ynY37G9mwr5GN+xv46Wv7ae8MA90hPy6ve2qCgqwUCrJSyc9MoSArhbz0ZIYkBvvk5ygcdrR3hekMOzo6w3SEw3R0OTq7whiGGZh1//IK2NtfG91DUkkJARKDAU2ORgyBbmZB4DHgZqAG2GBmS51z26OafQJodM6NM7O7ga8DH+yNgv97bRVfXbaT9q4wwYBx/YRc7p5TyIJJeSQE1eOQwSWWicaKslMpyk5l0Yx86k+c5khTG4eb2jjSfIqXd9XR0tZJV9i97T2hgJGekkBmSgLpKQkkJwRICAYIBaz7a9AIh6Ez7OgKh6lpPEWXc4TDEHaOsHN0haMePV+HXaRd/D6LoBkpiUESQwGSQoG3viaFgiSFAiQnBElOCJCUECQ5FCQpIUByqHvZmXXJ0esSgpHXf36eFAqQEAoQOPNLJeqXTcDAOd72S6mjy9HS1kHjyXYaWjtobG2n4WQ775wynJlFWfH75iNi6aGXAZXOub0AZvYUcDsQHei3A1+OPH8GeNTMzDkXx3+ubpNHpvPRq0dz1dgc5hRna+IjkRgFA8bw9GSGpyczI+reLneVFlDbcpqahpPUNJ6i/sRpmk510HSqg+OnOmg+1cHpjjAnOjvp7HJ0RHrUQTOCASMUNNo6uggEuoMtIWAEA4G31gcCRiiyLnjmeaD7+Zk2b3uYvdXbdg7OxIhzEMa9bXnYdf9S6YzUNC4vjdOdXbR3hjndGeZ0R5jTnV2c7gxzsr2ThtYwbZ1dnO4I09bR1f3oDP/FL7TeFAoYRdmpngV6PlAd9boGmHuuNs65TjNrAnKA+uhGZnYfcF/k5Qkzq7iUogeQYfT4DOQt+mzOrU8/mw/11Y7iwxf/b+75Ktxz6W8/553t+/SgqHPuceDxvtynl8ys3DlX6nUd/ZE+m3PTZ3Nu+mzOL5ZB54NA9M03CyLLztrGzEJABt0HR0VEpI/EEugbgPFmVmJmicDdwNIebZYCH408vxN4qTfGz0VE5NwuOOQSGRN/AFhO92mLP3LObTOzrwDlzrmlwA+Bn5lZJdBAd+jLIBpeugT6bM5Nn8256bM5D1NHWkTEH3TitoiITyjQRUR8QoHeS8xsoZlVmFmlmX3J63r6CzMrNLMVZrbdzLaZ2We9rqm/MbOgmW02s995XUt/YmaZZvaMme00sx1mdpXXNfU3GkPvBZHpEnYRNV0CsLjHdAmDkpmNBEY65zaZ2VBgI/BefTZ/ZmYPAqVAunPuNq/r6S/M7CfAaufcE5Ez7lKdc8e9rqs/UQ+9d7w1XYJzrh04M13CoOecO+yc2xR53gLsoPtKYwHMrAC4FXjC61r6EzPLAK6j+4w6nHPtCvO/pEDvHWebLkGh1YOZFQMzgXXeVtKvfBv4AhD2upB+pgSoA/47Mhz1hJkN8bqo/kaBLp4wszRgCfA551yz1/X0B2Z2G3DUObfR61r6oRAwC/iec24m0Aro2FQPCvTeEct0CYOWmSXQHea/cM4963U9/ch8YJGZ7aN7mO5GM/u5tyX1GzVAjXPuzF9zz9Ad8BJFgd47YpkuYVCy7rsm/BDY4Zx72Ot6+hPn3D845wqcc8V0/595yTl3r8dl9QvOuSNAtZlNjCx6B2+fwlvwwS3o+qNzTZfgcVn9xXzgw8CbZvZ6ZNk/OueWeViTDAyfAX4R6STtBT7ucT39jk5bFBHxCQ25iIj4hAJdRMQnFOgiIj6hQBcR8QkFuoiITyjQxdfM7MQ5lv/YzO6M874+ZmaPxnObIhdDgS4i4hMKdPENM3vQzLZGHp/rsc7M7NHIHPUvAHlR6/aZ2TfM7E0zW29m4yLLc81siZltiDzmR5aXmdmrkUmiXom6ejF6f7dG2gzr5W9b5C26UlR8wcxm033l4FzAgHVm9nJUk/cBE4EpwHC6Lxv/UdT6JufcFWb2EbpnPLwNeAT4lnNujZkV0X3l72RgJ3Bt5Irgm4D/C9wRVcv7gAeBdzvnGnvlGxY5CwW6+MU1wHPOuVYAM3sWuDZq/XXAL51zXcAhM3upx9+P6foAAAEoSURBVPt/GfX1W5HnNwFTuqefASA9MktkBvATMxsPOCAhajs30n1zindqFknpawp0kW7uLM8DwDznXFt0w8iBzxXOufdF5nRfGbV6DzAGmACU91axImejMXTxi9XAe80sNXLjg/dFlp2xCvhg5H6dI4EFPd7/waivr0ae/5HuCaEAMLMrI08z+PN0yB/rsZ39dA+//NTMpl76tyNy8RTo4guR29r9GFhP9x2QnnDObY5q8hywm+6x85/y59A+I8vMtgCfBf4usuxvgVIz22Jm24G/iSz/BvBVM9vMWf7Kdc7tBD4E/NrMxsbh2xOJiWZblEEvckOJUudcvde1iFwO9dBFRHxCPXQREZ9QD11ExCcU6CIiPqFAFxHxCQW6iIhPKNBFRHzi/wOKcwXqz6gKPQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5FMWxrb9DkTV",
        "colab_type": "code",
        "outputId": "52b3f262-9c4c-48f4-c124-a7028d110ea4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "y = data[\"sex\"]\n",
        "\n",
        "sns.barplot(data[\"exang\"],y)\n"
      ],
      "execution_count": 674,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe578b93be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 674
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2M4q00EZTVX7",
        "colab_type": "code",
        "outputId": "26cab2f3-8a81-4776-e08f-f0cc915ef41c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.barplot(data[\"ca\"],y)"
      ],
      "execution_count": 675,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe578b51748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 675
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qx5OcCnypd03/BcB3RjmgNCwPDUlT5xPAX1bVqcAHgSNHPI80FEMgHZqvA7+e5ASA3qGhY/nRZZzfP9kflGYaDw1Jh6B3ldk/AW5L8jxwN/BR4OYkTzAeiqUjHFEamieLJalxHhqSpMYZAklqnCGQpMYZAklqnCGQpMYZAklqnCGQpMb9Py7Hzz/kiUXxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXCWrOeFR9hy",
        "colab_type": "code",
        "outputId": "9c09b0a0-f854-44b6-a3a7-73baa166e65c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.barplot(data[\"exang\"],y)\n"
      ],
      "execution_count": 676,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe578ad0828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 676
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFj28bN8TT-g",
        "colab_type": "code",
        "outputId": "be26d1f7-a9a7-4104-f056-e63b9942ae74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.barplot(data[\"slope\"],y)\n"
      ],
      "execution_count": 677,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe578aad6a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 677
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WqM2Zl4hPhh",
        "colab_type": "text"
      },
      "source": [
        "### Algorith Implementation:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ijme0gFEhhEr",
        "colab_type": "text"
      },
      "source": [
        "Neural Network:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iV5pofshob-B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# create X and Y datasets for training\n",
        "from sklearn import model_selection\n",
        "\n",
        "X = np.array(data.drop(['class'], 1))\n",
        "y = np.array(data['class'])\n",
        "\n",
        "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size = 0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kxd6IIQ6od44",
        "colab_type": "code",
        "outputId": "187021ff-05ae-4076-8e61-48db63ceb527",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "source": [
        "# convert the data to categorical labels\n",
        "from keras.utils.np_utils import to_categorical\n",
        "\n",
        "Y_train = to_categorical(y_train, num_classes=None)\n",
        "Y_test = to_categorical(y_test, num_classes=None)\n",
        "print (Y_train.shape)\n",
        "print (Y_train[:10])"
      ],
      "execution_count": 679,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(237, 5)\n",
            "[[1. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lehTsUNloijf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6mNeizVet8-",
        "colab_type": "code",
        "outputId": "c0207630-51f3-44f1-ed5e-77ae857f05ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        }
      },
      "source": [
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.optimizers import Adam\n",
        "\n",
        "# define a function to build the keras model\n",
        "def create_model():\n",
        "    # create model\n",
        "    model = Sequential()\n",
        "    model.add(Dense(8, input_dim=13, kernel_initializer='normal', activation='relu'))\n",
        "    model.add(Dense(4, kernel_initializer='normal', activation='relu'))\n",
        "    model.add(Dense(5, activation='softmax'))\n",
        "    \n",
        "    # compile model\n",
        "    adam = Adam(lr=0.001)\n",
        "    model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
        "    return model\n",
        "\n",
        "model = create_model()\n",
        "\n",
        "print(model.summary())"
      ],
      "execution_count": 680,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_13\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_43 (Dense)             (None, 8)                 112       \n",
            "_________________________________________________________________\n",
            "dense_44 (Dense)             (None, 4)                 36        \n",
            "_________________________________________________________________\n",
            "dense_45 (Dense)             (None, 5)                 25        \n",
            "=================================================================\n",
            "Total params: 173\n",
            "Trainable params: 173\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ss7XIVjRoJ31",
        "colab_type": "code",
        "outputId": "579abf2a-1b01-445d-d1be-0ee5f0f6b5b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.fit(X_train, Y_train, epochs=100, batch_size=10, verbose = 1)\n"
      ],
      "execution_count": 681,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "237/237 [==============================] - 0s 414us/step - loss: 1.4188 - accuracy: 0.5527\n",
            "Epoch 2/100\n",
            "237/237 [==============================] - 0s 118us/step - loss: 1.3669 - accuracy: 0.5485\n",
            "Epoch 3/100\n",
            "237/237 [==============================] - 0s 128us/step - loss: 1.3396 - accuracy: 0.5570\n",
            "Epoch 4/100\n",
            "237/237 [==============================] - 0s 121us/step - loss: 1.3424 - accuracy: 0.5274\n",
            "Epoch 5/100\n",
            "237/237 [==============================] - 0s 122us/step - loss: 1.3081 - accuracy: 0.5570\n",
            "Epoch 6/100\n",
            "237/237 [==============================] - 0s 137us/step - loss: 1.2923 - accuracy: 0.5570\n",
            "Epoch 7/100\n",
            "237/237 [==============================] - 0s 134us/step - loss: 1.2810 - accuracy: 0.5570\n",
            "Epoch 8/100\n",
            "237/237 [==============================] - 0s 134us/step - loss: 1.2719 - accuracy: 0.5570\n",
            "Epoch 9/100\n",
            "237/237 [==============================] - 0s 131us/step - loss: 1.2500 - accuracy: 0.5570\n",
            "Epoch 10/100\n",
            "237/237 [==============================] - 0s 123us/step - loss: 1.2306 - accuracy: 0.5570\n",
            "Epoch 11/100\n",
            "237/237 [==============================] - 0s 147us/step - loss: 1.2280 - accuracy: 0.5570\n",
            "Epoch 12/100\n",
            "237/237 [==============================] - 0s 141us/step - loss: 1.2109 - accuracy: 0.5527\n",
            "Epoch 13/100\n",
            "237/237 [==============================] - 0s 137us/step - loss: 1.2019 - accuracy: 0.5570\n",
            "Epoch 14/100\n",
            "237/237 [==============================] - 0s 137us/step - loss: 1.1840 - accuracy: 0.5570\n",
            "Epoch 15/100\n",
            "237/237 [==============================] - 0s 212us/step - loss: 1.1846 - accuracy: 0.5570\n",
            "Epoch 16/100\n",
            "237/237 [==============================] - 0s 137us/step - loss: 1.1770 - accuracy: 0.5570\n",
            "Epoch 17/100\n",
            "237/237 [==============================] - 0s 138us/step - loss: 1.1698 - accuracy: 0.5570\n",
            "Epoch 18/100\n",
            "237/237 [==============================] - 0s 140us/step - loss: 1.1683 - accuracy: 0.5654\n",
            "Epoch 19/100\n",
            "237/237 [==============================] - 0s 141us/step - loss: 1.1366 - accuracy: 0.5654\n",
            "Epoch 20/100\n",
            "237/237 [==============================] - 0s 135us/step - loss: 1.1331 - accuracy: 0.5570\n",
            "Epoch 21/100\n",
            "237/237 [==============================] - 0s 142us/step - loss: 1.1294 - accuracy: 0.5570\n",
            "Epoch 22/100\n",
            "237/237 [==============================] - 0s 136us/step - loss: 1.1131 - accuracy: 0.5738\n",
            "Epoch 23/100\n",
            "237/237 [==============================] - 0s 124us/step - loss: 1.1051 - accuracy: 0.5696\n",
            "Epoch 24/100\n",
            "237/237 [==============================] - 0s 115us/step - loss: 1.0907 - accuracy: 0.5696\n",
            "Epoch 25/100\n",
            "237/237 [==============================] - 0s 117us/step - loss: 1.1089 - accuracy: 0.5654\n",
            "Epoch 26/100\n",
            "237/237 [==============================] - 0s 115us/step - loss: 1.0811 - accuracy: 0.5738\n",
            "Epoch 27/100\n",
            "237/237 [==============================] - 0s 113us/step - loss: 1.0831 - accuracy: 0.5654\n",
            "Epoch 28/100\n",
            "237/237 [==============================] - 0s 116us/step - loss: 1.0595 - accuracy: 0.5823\n",
            "Epoch 29/100\n",
            "237/237 [==============================] - 0s 120us/step - loss: 1.0542 - accuracy: 0.5907\n",
            "Epoch 30/100\n",
            "237/237 [==============================] - 0s 129us/step - loss: 1.0660 - accuracy: 0.5823\n",
            "Epoch 31/100\n",
            "237/237 [==============================] - 0s 147us/step - loss: 1.0578 - accuracy: 0.5823\n",
            "Epoch 32/100\n",
            "237/237 [==============================] - 0s 117us/step - loss: 1.0385 - accuracy: 0.5823\n",
            "Epoch 33/100\n",
            "237/237 [==============================] - 0s 163us/step - loss: 1.0280 - accuracy: 0.5823\n",
            "Epoch 34/100\n",
            "237/237 [==============================] - 0s 142us/step - loss: 1.0292 - accuracy: 0.5865\n",
            "Epoch 35/100\n",
            "237/237 [==============================] - 0s 119us/step - loss: 1.0213 - accuracy: 0.5865\n",
            "Epoch 36/100\n",
            "237/237 [==============================] - 0s 121us/step - loss: 1.0166 - accuracy: 0.5949\n",
            "Epoch 37/100\n",
            "237/237 [==============================] - 0s 142us/step - loss: 1.0158 - accuracy: 0.5907\n",
            "Epoch 38/100\n",
            "237/237 [==============================] - 0s 136us/step - loss: 1.0054 - accuracy: 0.5823\n",
            "Epoch 39/100\n",
            "237/237 [==============================] - 0s 123us/step - loss: 1.0010 - accuracy: 0.5907\n",
            "Epoch 40/100\n",
            "237/237 [==============================] - 0s 123us/step - loss: 0.9950 - accuracy: 0.5992\n",
            "Epoch 41/100\n",
            "237/237 [==============================] - 0s 142us/step - loss: 0.9894 - accuracy: 0.5992\n",
            "Epoch 42/100\n",
            "237/237 [==============================] - 0s 137us/step - loss: 0.9894 - accuracy: 0.5949\n",
            "Epoch 43/100\n",
            "237/237 [==============================] - 0s 135us/step - loss: 0.9846 - accuracy: 0.5992\n",
            "Epoch 44/100\n",
            "237/237 [==============================] - 0s 158us/step - loss: 1.0062 - accuracy: 0.5865\n",
            "Epoch 45/100\n",
            "237/237 [==============================] - 0s 206us/step - loss: 0.9903 - accuracy: 0.6118\n",
            "Epoch 46/100\n",
            "237/237 [==============================] - 0s 120us/step - loss: 0.9747 - accuracy: 0.6076\n",
            "Epoch 47/100\n",
            "237/237 [==============================] - 0s 123us/step - loss: 0.9761 - accuracy: 0.5949\n",
            "Epoch 48/100\n",
            "237/237 [==============================] - 0s 121us/step - loss: 0.9693 - accuracy: 0.6076\n",
            "Epoch 49/100\n",
            "237/237 [==============================] - 0s 133us/step - loss: 0.9755 - accuracy: 0.6034\n",
            "Epoch 50/100\n",
            "237/237 [==============================] - 0s 144us/step - loss: 0.9817 - accuracy: 0.6034\n",
            "Epoch 51/100\n",
            "237/237 [==============================] - 0s 114us/step - loss: 0.9622 - accuracy: 0.5992\n",
            "Epoch 52/100\n",
            "237/237 [==============================] - 0s 115us/step - loss: 0.9665 - accuracy: 0.5907\n",
            "Epoch 53/100\n",
            "237/237 [==============================] - 0s 115us/step - loss: 0.9592 - accuracy: 0.6076\n",
            "Epoch 54/100\n",
            "237/237 [==============================] - 0s 123us/step - loss: 0.9751 - accuracy: 0.6160\n",
            "Epoch 55/100\n",
            "237/237 [==============================] - 0s 131us/step - loss: 0.9666 - accuracy: 0.6034\n",
            "Epoch 56/100\n",
            "237/237 [==============================] - 0s 132us/step - loss: 0.9711 - accuracy: 0.6034\n",
            "Epoch 57/100\n",
            "237/237 [==============================] - 0s 126us/step - loss: 0.9549 - accuracy: 0.6118\n",
            "Epoch 58/100\n",
            "237/237 [==============================] - 0s 132us/step - loss: 0.9683 - accuracy: 0.6118\n",
            "Epoch 59/100\n",
            "237/237 [==============================] - 0s 128us/step - loss: 0.9468 - accuracy: 0.6203\n",
            "Epoch 60/100\n",
            "237/237 [==============================] - 0s 123us/step - loss: 0.9531 - accuracy: 0.6456\n",
            "Epoch 61/100\n",
            "237/237 [==============================] - 0s 120us/step - loss: 0.9348 - accuracy: 0.6456\n",
            "Epoch 62/100\n",
            "237/237 [==============================] - 0s 128us/step - loss: 0.9514 - accuracy: 0.6287\n",
            "Epoch 63/100\n",
            "237/237 [==============================] - 0s 122us/step - loss: 0.9385 - accuracy: 0.6371\n",
            "Epoch 64/100\n",
            "237/237 [==============================] - 0s 128us/step - loss: 0.9357 - accuracy: 0.6498\n",
            "Epoch 65/100\n",
            "237/237 [==============================] - 0s 128us/step - loss: 0.9443 - accuracy: 0.6329\n",
            "Epoch 66/100\n",
            "237/237 [==============================] - 0s 120us/step - loss: 0.9319 - accuracy: 0.6582\n",
            "Epoch 67/100\n",
            "237/237 [==============================] - 0s 131us/step - loss: 0.9290 - accuracy: 0.6498\n",
            "Epoch 68/100\n",
            "237/237 [==============================] - 0s 156us/step - loss: 0.9293 - accuracy: 0.6371\n",
            "Epoch 69/100\n",
            "237/237 [==============================] - 0s 116us/step - loss: 0.9334 - accuracy: 0.6540\n",
            "Epoch 70/100\n",
            "237/237 [==============================] - 0s 118us/step - loss: 0.9239 - accuracy: 0.6540\n",
            "Epoch 71/100\n",
            "237/237 [==============================] - 0s 116us/step - loss: 0.9297 - accuracy: 0.6287\n",
            "Epoch 72/100\n",
            "237/237 [==============================] - 0s 134us/step - loss: 0.9305 - accuracy: 0.6582\n",
            "Epoch 73/100\n",
            "237/237 [==============================] - 0s 132us/step - loss: 0.9308 - accuracy: 0.6456\n",
            "Epoch 74/100\n",
            "237/237 [==============================] - 0s 113us/step - loss: 0.9320 - accuracy: 0.6287\n",
            "Epoch 75/100\n",
            "237/237 [==============================] - 0s 119us/step - loss: 0.9332 - accuracy: 0.6371\n",
            "Epoch 76/100\n",
            "237/237 [==============================] - 0s 123us/step - loss: 0.9107 - accuracy: 0.6582\n",
            "Epoch 77/100\n",
            "237/237 [==============================] - 0s 142us/step - loss: 0.9198 - accuracy: 0.6498\n",
            "Epoch 78/100\n",
            "237/237 [==============================] - 0s 110us/step - loss: 0.9404 - accuracy: 0.6329\n",
            "Epoch 79/100\n",
            "237/237 [==============================] - 0s 108us/step - loss: 0.9262 - accuracy: 0.6371\n",
            "Epoch 80/100\n",
            "237/237 [==============================] - 0s 121us/step - loss: 0.9285 - accuracy: 0.6329\n",
            "Epoch 81/100\n",
            "237/237 [==============================] - 0s 119us/step - loss: 0.9163 - accuracy: 0.6329\n",
            "Epoch 82/100\n",
            "237/237 [==============================] - 0s 111us/step - loss: 0.9198 - accuracy: 0.6329\n",
            "Epoch 83/100\n",
            "237/237 [==============================] - 0s 114us/step - loss: 0.9117 - accuracy: 0.6498\n",
            "Epoch 84/100\n",
            "237/237 [==============================] - 0s 109us/step - loss: 0.9048 - accuracy: 0.6582\n",
            "Epoch 85/100\n",
            "237/237 [==============================] - 0s 123us/step - loss: 0.9157 - accuracy: 0.6456\n",
            "Epoch 86/100\n",
            "237/237 [==============================] - 0s 124us/step - loss: 0.9118 - accuracy: 0.6582\n",
            "Epoch 87/100\n",
            "237/237 [==============================] - 0s 118us/step - loss: 0.9141 - accuracy: 0.6751\n",
            "Epoch 88/100\n",
            "237/237 [==============================] - 0s 121us/step - loss: 0.9078 - accuracy: 0.6456\n",
            "Epoch 89/100\n",
            "237/237 [==============================] - 0s 149us/step - loss: 0.9014 - accuracy: 0.6582\n",
            "Epoch 90/100\n",
            "237/237 [==============================] - 0s 130us/step - loss: 0.9050 - accuracy: 0.6414\n",
            "Epoch 91/100\n",
            "237/237 [==============================] - 0s 127us/step - loss: 0.8970 - accuracy: 0.6498\n",
            "Epoch 92/100\n",
            "237/237 [==============================] - 0s 121us/step - loss: 0.9069 - accuracy: 0.6624\n",
            "Epoch 93/100\n",
            "237/237 [==============================] - 0s 126us/step - loss: 0.9129 - accuracy: 0.6287\n",
            "Epoch 94/100\n",
            "237/237 [==============================] - 0s 124us/step - loss: 0.8897 - accuracy: 0.6498\n",
            "Epoch 95/100\n",
            "237/237 [==============================] - 0s 121us/step - loss: 0.9038 - accuracy: 0.6371\n",
            "Epoch 96/100\n",
            "237/237 [==============================] - 0s 125us/step - loss: 0.8937 - accuracy: 0.6498\n",
            "Epoch 97/100\n",
            "237/237 [==============================] - 0s 125us/step - loss: 0.9277 - accuracy: 0.6540\n",
            "Epoch 98/100\n",
            "237/237 [==============================] - 0s 111us/step - loss: 0.8884 - accuracy: 0.6329\n",
            "Epoch 99/100\n",
            "237/237 [==============================] - 0s 121us/step - loss: 0.9014 - accuracy: 0.6371\n",
            "Epoch 100/100\n",
            "237/237 [==============================] - 0s 118us/step - loss: 0.9023 - accuracy: 0.6329\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7fe578aa39e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 681
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_P6JGX5jhpMj",
        "colab_type": "text"
      },
      "source": [
        "To improve accuracy convert into binary classification problem - heart disease or no heart disease\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ty4SHQ4KoTD4",
        "colab_type": "code",
        "outputId": "59637941-e3c6-42df-bf23-5c29c2121e0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Y_train_binary = y_train.copy()\n",
        "Y_test_binary = y_test.copy()\n",
        "\n",
        "Y_train_binary[Y_train_binary > 0] = 1\n",
        "Y_test_binary[Y_test_binary > 0] = 1\n",
        "\n",
        "print (Y_train_binary[:20])"
      ],
      "execution_count": 682,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mAJTfxMEpGN-",
        "colab_type": "code",
        "outputId": "7d2080ad-fea0-4e03-ff2f-fc08418c6338",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "# define a new keras model for binary classification\n",
        "def create_binary_model():\n",
        "    # create model\n",
        "    model = Sequential()\n",
        "    model.add(Dense(16, input_dim=13, kernel_initializer='normal', activation='relu'))\n",
        "    model.add(Dense(8, kernel_initializer='normal', activation='relu'))\n",
        "    model.add(Dense(4, kernel_initializer='normal', activation='relu'))\n",
        "\n",
        "    model.add(Dense(1, activation='sigmoid'))\n",
        "    \n",
        "    # Compile model\n",
        "    adam = Adam(lr=0.001)\n",
        "    model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
        "    return model\n",
        "\n",
        "binary_model = create_binary_model()\n",
        "\n",
        "print(binary_model.summary())"
      ],
      "execution_count": 683,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_14\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_46 (Dense)             (None, 16)                224       \n",
            "_________________________________________________________________\n",
            "dense_47 (Dense)             (None, 8)                 136       \n",
            "_________________________________________________________________\n",
            "dense_48 (Dense)             (None, 4)                 36        \n",
            "_________________________________________________________________\n",
            "dense_49 (Dense)             (None, 1)                 5         \n",
            "=================================================================\n",
            "Total params: 401\n",
            "Trainable params: 401\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dlY2fBFUpIny",
        "colab_type": "code",
        "outputId": "a867d235-d507-4161-e02d-42e2292678ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "# fit the binary model on the training data\n",
        "binary_model.fit(X_train, Y_train_binary, epochs=100, batch_size=10, verbose = 1)"
      ],
      "execution_count": 684,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "237/237 [==============================] - 0s 582us/step - loss: 0.7049 - accuracy: 0.4430\n",
            "Epoch 2/100\n",
            "237/237 [==============================] - 0s 129us/step - loss: 0.6877 - accuracy: 0.5612\n",
            "Epoch 3/100\n",
            "237/237 [==============================] - 0s 139us/step - loss: 0.6841 - accuracy: 0.5612\n",
            "Epoch 4/100\n",
            "237/237 [==============================] - 0s 134us/step - loss: 0.6845 - accuracy: 0.5612\n",
            "Epoch 5/100\n",
            "237/237 [==============================] - 0s 128us/step - loss: 0.6804 - accuracy: 0.5612\n",
            "Epoch 6/100\n",
            "237/237 [==============================] - 0s 134us/step - loss: 0.6786 - accuracy: 0.5612\n",
            "Epoch 7/100\n",
            "237/237 [==============================] - 0s 127us/step - loss: 0.6751 - accuracy: 0.5612\n",
            "Epoch 8/100\n",
            "237/237 [==============================] - 0s 138us/step - loss: 0.6716 - accuracy: 0.5612\n",
            "Epoch 9/100\n",
            "237/237 [==============================] - 0s 147us/step - loss: 0.6660 - accuracy: 0.5612\n",
            "Epoch 10/100\n",
            "237/237 [==============================] - 0s 134us/step - loss: 0.6599 - accuracy: 0.5612\n",
            "Epoch 11/100\n",
            "237/237 [==============================] - 0s 137us/step - loss: 0.6459 - accuracy: 0.5612\n",
            "Epoch 12/100\n",
            "237/237 [==============================] - 0s 128us/step - loss: 0.6264 - accuracy: 0.6624\n",
            "Epoch 13/100\n",
            "237/237 [==============================] - 0s 129us/step - loss: 0.5965 - accuracy: 0.7173\n",
            "Epoch 14/100\n",
            "237/237 [==============================] - 0s 128us/step - loss: 0.5630 - accuracy: 0.7300\n",
            "Epoch 15/100\n",
            "237/237 [==============================] - 0s 135us/step - loss: 0.5102 - accuracy: 0.7679\n",
            "Epoch 16/100\n",
            "237/237 [==============================] - 0s 125us/step - loss: 0.4900 - accuracy: 0.7595\n",
            "Epoch 17/100\n",
            "237/237 [==============================] - 0s 143us/step - loss: 0.5017 - accuracy: 0.7426\n",
            "Epoch 18/100\n",
            "237/237 [==============================] - 0s 124us/step - loss: 0.4804 - accuracy: 0.7342\n",
            "Epoch 19/100\n",
            "237/237 [==============================] - 0s 125us/step - loss: 0.5159 - accuracy: 0.7679\n",
            "Epoch 20/100\n",
            "237/237 [==============================] - 0s 147us/step - loss: 0.4849 - accuracy: 0.7553\n",
            "Epoch 21/100\n",
            "237/237 [==============================] - 0s 134us/step - loss: 0.4774 - accuracy: 0.7806\n",
            "Epoch 22/100\n",
            "237/237 [==============================] - 0s 143us/step - loss: 0.4809 - accuracy: 0.7511\n",
            "Epoch 23/100\n",
            "237/237 [==============================] - 0s 130us/step - loss: 0.4527 - accuracy: 0.7932\n",
            "Epoch 24/100\n",
            "237/237 [==============================] - 0s 153us/step - loss: 0.4463 - accuracy: 0.7932\n",
            "Epoch 25/100\n",
            "237/237 [==============================] - 0s 128us/step - loss: 0.4712 - accuracy: 0.7637\n",
            "Epoch 26/100\n",
            "237/237 [==============================] - 0s 125us/step - loss: 0.4811 - accuracy: 0.7806\n",
            "Epoch 27/100\n",
            "237/237 [==============================] - 0s 142us/step - loss: 0.4362 - accuracy: 0.7806\n",
            "Epoch 28/100\n",
            "237/237 [==============================] - 0s 137us/step - loss: 0.4432 - accuracy: 0.7932\n",
            "Epoch 29/100\n",
            "237/237 [==============================] - 0s 137us/step - loss: 0.4580 - accuracy: 0.7806\n",
            "Epoch 30/100\n",
            "237/237 [==============================] - 0s 138us/step - loss: 0.4791 - accuracy: 0.7637\n",
            "Epoch 31/100\n",
            "237/237 [==============================] - 0s 133us/step - loss: 0.4625 - accuracy: 0.7975\n",
            "Epoch 32/100\n",
            "237/237 [==============================] - 0s 134us/step - loss: 0.4441 - accuracy: 0.7975\n",
            "Epoch 33/100\n",
            "237/237 [==============================] - 0s 127us/step - loss: 0.4224 - accuracy: 0.8143\n",
            "Epoch 34/100\n",
            "237/237 [==============================] - 0s 131us/step - loss: 0.4260 - accuracy: 0.8059\n",
            "Epoch 35/100\n",
            "237/237 [==============================] - 0s 132us/step - loss: 0.4348 - accuracy: 0.8101\n",
            "Epoch 36/100\n",
            "237/237 [==============================] - 0s 142us/step - loss: 0.4132 - accuracy: 0.8228\n",
            "Epoch 37/100\n",
            "237/237 [==============================] - 0s 145us/step - loss: 0.4295 - accuracy: 0.8143\n",
            "Epoch 38/100\n",
            "237/237 [==============================] - 0s 149us/step - loss: 0.4407 - accuracy: 0.7932\n",
            "Epoch 39/100\n",
            "237/237 [==============================] - 0s 135us/step - loss: 0.4612 - accuracy: 0.7806\n",
            "Epoch 40/100\n",
            "237/237 [==============================] - 0s 129us/step - loss: 0.4399 - accuracy: 0.8059\n",
            "Epoch 41/100\n",
            "237/237 [==============================] - 0s 131us/step - loss: 0.4437 - accuracy: 0.7890\n",
            "Epoch 42/100\n",
            "237/237 [==============================] - 0s 134us/step - loss: 0.4253 - accuracy: 0.8228\n",
            "Epoch 43/100\n",
            "237/237 [==============================] - 0s 146us/step - loss: 0.4447 - accuracy: 0.8143\n",
            "Epoch 44/100\n",
            "237/237 [==============================] - 0s 131us/step - loss: 0.4075 - accuracy: 0.8228\n",
            "Epoch 45/100\n",
            "237/237 [==============================] - 0s 141us/step - loss: 0.4204 - accuracy: 0.8186\n",
            "Epoch 46/100\n",
            "237/237 [==============================] - 0s 135us/step - loss: 0.4037 - accuracy: 0.8565\n",
            "Epoch 47/100\n",
            "237/237 [==============================] - 0s 134us/step - loss: 0.3911 - accuracy: 0.8523\n",
            "Epoch 48/100\n",
            "237/237 [==============================] - 0s 131us/step - loss: 0.3889 - accuracy: 0.8439\n",
            "Epoch 49/100\n",
            "237/237 [==============================] - 0s 132us/step - loss: 0.3900 - accuracy: 0.8565\n",
            "Epoch 50/100\n",
            "237/237 [==============================] - 0s 155us/step - loss: 0.3853 - accuracy: 0.8439\n",
            "Epoch 51/100\n",
            "237/237 [==============================] - 0s 164us/step - loss: 0.3908 - accuracy: 0.8481\n",
            "Epoch 52/100\n",
            "237/237 [==============================] - 0s 143us/step - loss: 0.3874 - accuracy: 0.8481\n",
            "Epoch 53/100\n",
            "237/237 [==============================] - 0s 133us/step - loss: 0.4073 - accuracy: 0.8481\n",
            "Epoch 54/100\n",
            "237/237 [==============================] - 0s 125us/step - loss: 0.4061 - accuracy: 0.8228\n",
            "Epoch 55/100\n",
            "237/237 [==============================] - 0s 133us/step - loss: 0.3971 - accuracy: 0.8523\n",
            "Epoch 56/100\n",
            "237/237 [==============================] - 0s 127us/step - loss: 0.3917 - accuracy: 0.8481\n",
            "Epoch 57/100\n",
            "237/237 [==============================] - 0s 127us/step - loss: 0.3838 - accuracy: 0.8481\n",
            "Epoch 58/100\n",
            "237/237 [==============================] - 0s 140us/step - loss: 0.3822 - accuracy: 0.8481\n",
            "Epoch 59/100\n",
            "237/237 [==============================] - 0s 131us/step - loss: 0.3904 - accuracy: 0.8481\n",
            "Epoch 60/100\n",
            "237/237 [==============================] - 0s 141us/step - loss: 0.3993 - accuracy: 0.8439\n",
            "Epoch 61/100\n",
            "237/237 [==============================] - 0s 159us/step - loss: 0.3926 - accuracy: 0.8565\n",
            "Epoch 62/100\n",
            "237/237 [==============================] - 0s 137us/step - loss: 0.4128 - accuracy: 0.8270\n",
            "Epoch 63/100\n",
            "237/237 [==============================] - 0s 150us/step - loss: 0.3894 - accuracy: 0.8481\n",
            "Epoch 64/100\n",
            "237/237 [==============================] - 0s 150us/step - loss: 0.3821 - accuracy: 0.8439\n",
            "Epoch 65/100\n",
            "237/237 [==============================] - 0s 149us/step - loss: 0.3741 - accuracy: 0.8523\n",
            "Epoch 66/100\n",
            "237/237 [==============================] - 0s 143us/step - loss: 0.4006 - accuracy: 0.8439\n",
            "Epoch 67/100\n",
            "237/237 [==============================] - 0s 136us/step - loss: 0.4123 - accuracy: 0.8143\n",
            "Epoch 68/100\n",
            "237/237 [==============================] - 0s 133us/step - loss: 0.3952 - accuracy: 0.8439\n",
            "Epoch 69/100\n",
            "237/237 [==============================] - 0s 129us/step - loss: 0.3875 - accuracy: 0.8481\n",
            "Epoch 70/100\n",
            "237/237 [==============================] - 0s 127us/step - loss: 0.3760 - accuracy: 0.8565\n",
            "Epoch 71/100\n",
            "237/237 [==============================] - 0s 151us/step - loss: 0.3799 - accuracy: 0.8354\n",
            "Epoch 72/100\n",
            "237/237 [==============================] - 0s 173us/step - loss: 0.3861 - accuracy: 0.8650\n",
            "Epoch 73/100\n",
            "237/237 [==============================] - 0s 157us/step - loss: 0.4027 - accuracy: 0.8397\n",
            "Epoch 74/100\n",
            "237/237 [==============================] - 0s 177us/step - loss: 0.3730 - accuracy: 0.8650\n",
            "Epoch 75/100\n",
            "237/237 [==============================] - 0s 172us/step - loss: 0.3714 - accuracy: 0.8565\n",
            "Epoch 76/100\n",
            "237/237 [==============================] - 0s 159us/step - loss: 0.3722 - accuracy: 0.8650\n",
            "Epoch 77/100\n",
            "237/237 [==============================] - 0s 157us/step - loss: 0.3716 - accuracy: 0.8650\n",
            "Epoch 78/100\n",
            "237/237 [==============================] - 0s 184us/step - loss: 0.3776 - accuracy: 0.8650\n",
            "Epoch 79/100\n",
            "237/237 [==============================] - 0s 176us/step - loss: 0.3684 - accuracy: 0.8523\n",
            "Epoch 80/100\n",
            "237/237 [==============================] - 0s 210us/step - loss: 0.3669 - accuracy: 0.8734\n",
            "Epoch 81/100\n",
            "237/237 [==============================] - 0s 154us/step - loss: 0.3864 - accuracy: 0.8608\n",
            "Epoch 82/100\n",
            "237/237 [==============================] - 0s 155us/step - loss: 0.3906 - accuracy: 0.8354\n",
            "Epoch 83/100\n",
            "237/237 [==============================] - 0s 159us/step - loss: 0.3727 - accuracy: 0.8481\n",
            "Epoch 84/100\n",
            "237/237 [==============================] - 0s 158us/step - loss: 0.3788 - accuracy: 0.8439\n",
            "Epoch 85/100\n",
            "237/237 [==============================] - 0s 153us/step - loss: 0.4022 - accuracy: 0.8312\n",
            "Epoch 86/100\n",
            "237/237 [==============================] - 0s 154us/step - loss: 0.3983 - accuracy: 0.8397\n",
            "Epoch 87/100\n",
            "237/237 [==============================] - 0s 142us/step - loss: 0.4229 - accuracy: 0.8101\n",
            "Epoch 88/100\n",
            "237/237 [==============================] - 0s 129us/step - loss: 0.3914 - accuracy: 0.8523\n",
            "Epoch 89/100\n",
            "237/237 [==============================] - 0s 128us/step - loss: 0.3777 - accuracy: 0.8565\n",
            "Epoch 90/100\n",
            "237/237 [==============================] - 0s 141us/step - loss: 0.3750 - accuracy: 0.8397\n",
            "Epoch 91/100\n",
            "237/237 [==============================] - 0s 134us/step - loss: 0.3849 - accuracy: 0.8397\n",
            "Epoch 92/100\n",
            "237/237 [==============================] - 0s 127us/step - loss: 0.3908 - accuracy: 0.8397\n",
            "Epoch 93/100\n",
            "237/237 [==============================] - 0s 137us/step - loss: 0.3739 - accuracy: 0.8608\n",
            "Epoch 94/100\n",
            "237/237 [==============================] - 0s 132us/step - loss: 0.3651 - accuracy: 0.8523\n",
            "Epoch 95/100\n",
            "237/237 [==============================] - 0s 128us/step - loss: 0.3673 - accuracy: 0.8523\n",
            "Epoch 96/100\n",
            "237/237 [==============================] - 0s 136us/step - loss: 0.3916 - accuracy: 0.8523\n",
            "Epoch 97/100\n",
            "237/237 [==============================] - 0s 130us/step - loss: 0.3722 - accuracy: 0.8608\n",
            "Epoch 98/100\n",
            "237/237 [==============================] - 0s 148us/step - loss: 0.3658 - accuracy: 0.8565\n",
            "Epoch 99/100\n",
            "237/237 [==============================] - 0s 153us/step - loss: 0.3624 - accuracy: 0.8650\n",
            "Epoch 100/100\n",
            "237/237 [==============================] - 0s 125us/step - loss: 0.3880 - accuracy: 0.8565\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7fe578905630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 684
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYIrWgyRpL1-",
        "colab_type": "code",
        "outputId": "132fe2b1-deec-42ab-a1f9-388355523957",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        }
      },
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "\n",
        "categorical_pred = np.argmax(model.predict(X_test), axis=1)\n",
        "\n",
        "print('Results for Categorical Model')\n",
        "print(accuracy_score(y_test, categorical_pred))\n",
        "print(classification_report(y_test, categorical_pred))"
      ],
      "execution_count": 685,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Results for Categorical Model\n",
            "0.55\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.66      1.00      0.79        27\n",
            "           1       1.00      0.13      0.24        15\n",
            "           2       0.00      0.00      0.00         8\n",
            "           3       0.24      0.80      0.36         5\n",
            "           4       0.00      0.00      0.00         5\n",
            "\n",
            "    accuracy                           0.55        60\n",
            "   macro avg       0.38      0.39      0.28        60\n",
            "weighted avg       0.57      0.55      0.45        60\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wl-IBKlmC7NB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XcKvW-jypiLe",
        "colab_type": "code",
        "outputId": "09ce8a32-d0cb-4d7c-8de8-44dd7f1a0689",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "source": [
        "\n",
        "# generate classification report using predictions for binary model \n",
        "binary_pred = np.round(binary_model.predict(X_test)).astype(int)\n",
        "\n",
        "print('Results for Binary Model')\n",
        "print(accuracy_score(Y_test_binary, binary_pred))\n",
        "print(classification_report(Y_test_binary, binary_pred))"
      ],
      "execution_count": 686,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Results for Binary Model\n",
            "0.8166666666666667\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.72      0.96      0.83        27\n",
            "           1       0.96      0.70      0.81        33\n",
            "\n",
            "    accuracy                           0.82        60\n",
            "   macro avg       0.84      0.83      0.82        60\n",
            "weighted avg       0.85      0.82      0.82        60\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSQCeDgeD3sV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tS3yjtGDZpP7",
        "colab_type": "text"
      },
      "source": [
        "## KNN Algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYqu36rknitk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "url = \"http://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data\"\n",
        "\n",
        "# the names will be the names of each column in our pandas DataFrame\n",
        "names = ['age',\n",
        "        'sex',\n",
        "        'cp',\n",
        "        'trestbps',\n",
        "        'chol',\n",
        "        'fbs',\n",
        "        'restecg',\n",
        "        'thalach',\n",
        "        'exang',\n",
        "        'oldpeak',\n",
        "        'slope',\n",
        "        'ca',\n",
        "        'thal',\n",
        "        'class']\n",
        "\n",
        "# read the csv\n",
        "datset = pd.read_csv(url, names=names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7IvpdopXsPsH",
        "colab_type": "code",
        "outputId": "20df1839-cfd8-497f-dfac-9fcbdefc1647",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "datset"
      ],
      "execution_count": 688,
      "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",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>233.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>286.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>229.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>45.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>264.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>68.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>57.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>38.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>173.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>?</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>303 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      age  sex   cp  trestbps   chol  ...  oldpeak  slope   ca  thal  class\n",
              "0    63.0  1.0  1.0     145.0  233.0  ...      2.3    3.0  0.0   6.0      0\n",
              "1    67.0  1.0  4.0     160.0  286.0  ...      1.5    2.0  3.0   3.0      2\n",
              "2    67.0  1.0  4.0     120.0  229.0  ...      2.6    2.0  2.0   7.0      1\n",
              "3    37.0  1.0  3.0     130.0  250.0  ...      3.5    3.0  0.0   3.0      0\n",
              "4    41.0  0.0  2.0     130.0  204.0  ...      1.4    1.0  0.0   3.0      0\n",
              "..    ...  ...  ...       ...    ...  ...      ...    ...  ...   ...    ...\n",
              "298  45.0  1.0  1.0     110.0  264.0  ...      1.2    2.0  0.0   7.0      1\n",
              "299  68.0  1.0  4.0     144.0  193.0  ...      3.4    2.0  2.0   7.0      2\n",
              "300  57.0  1.0  4.0     130.0  131.0  ...      1.2    2.0  1.0   7.0      3\n",
              "301  57.0  0.0  2.0     130.0  236.0  ...      0.0    2.0  1.0   3.0      1\n",
              "302  38.0  1.0  3.0     138.0  175.0  ...      0.0    1.0    ?   3.0      0\n",
              "\n",
              "[303 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 688
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60dz2BxXsZiP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# remove null values as less values\n",
        "data = cleveland[~cleveland.isin(['?'])]    \n",
        "#dropping row\n",
        "data = data.dropna(axis=0) \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nqlGhYlhskRN",
        "colab_type": "code",
        "outputId": "38e62bf8-f7f4-4bff-fbc5-ac79295b89cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "print (data.shape)\n",
        "print (data.dtypes)"
      ],
      "execution_count": 690,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(297, 14)\n",
            "age         float64\n",
            "sex         float64\n",
            "cp          float64\n",
            "trestbps    float64\n",
            "chol        float64\n",
            "fbs         float64\n",
            "restecg     float64\n",
            "thalach     float64\n",
            "exang       float64\n",
            "oldpeak     float64\n",
            "slope       float64\n",
            "ca           object\n",
            "thal         object\n",
            "class         int64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyGTisUrvdgr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = data.apply(pd.to_numeric)\n",
        "data.dtypes\n",
        "# print (data.shape)\n",
        "# print (data.dtypes)\n",
        "# print(data)\n",
        "colnames_numeric = data.columns[0:14]\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "data[colnames_numeric] = scaler.fit_transform(data[colnames_numeric])\n",
        "X = np.array(data)\n",
        "y = np.array(data['class'])\n",
        "\n",
        "#print(X)\n",
        "# y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2WdEqFQvoDs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y[y>0]=1\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "da412gAg3jV_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['class']=y\n",
        "#data1 = data\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqhErAko4Fmo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "df = data.values.tolist()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWqK6RcrDvkR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "#Breaking the data into training and test set\n",
        "import random\n",
        "def train_test_split(data, split, trainingSet = [], testSet = []):\n",
        "    for x in range(len(data)):\n",
        "        if random.random() < split:\n",
        "            trainingSet.append(data[x])\n",
        "        else:\n",
        "            testSet.append(data[x])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T12J6EimD10I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "trainingSet = []\n",
        "testSet = []\n",
        "split = 0.8\n",
        "train_test_split(df, split, trainingSet, testSet)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDCAg6lPD6Sb",
        "colab_type": "code",
        "outputId": "6905b108-68c0-42ac-f46a-91c4a0d9f35f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(trainingSet)\n"
      ],
      "execution_count": 697,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "229"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 697
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-e5m9d7DD86m",
        "colab_type": "code",
        "outputId": "74b8593e-d738-44e6-b335-bf8428f852eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(testSet)\n",
        "\n"
      ],
      "execution_count": 698,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "68"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 698
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJujdikbECA7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Define Euclidean distances\n",
        "import math\n",
        "def Euclideandist(x,xi, length):\n",
        "    d = 0.0\n",
        "    for i in range(length):\n",
        "        d += pow(float(x[i])- float(xi[i]),2)\n",
        "    return math.sqrt(d)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0hmlGvbxEJiT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "#Getting the K neighbours having the closest Euclidean distance to the test instance\n",
        "import operator\n",
        "def getNeighbors(trainingSet, testInstance, k):\n",
        "    distances = []\n",
        "    length = len(testInstance)-1\n",
        "    for x in range(len(trainingSet)):\n",
        "        dist = Euclideandist(testInstance, trainingSet[x], length)\n",
        "        distances.append((trainingSet[x], dist))\n",
        "    distances.sort(key=operator.itemgetter(1))\n",
        "    neighbors = []\n",
        "    for x in range(k):\n",
        "        neighbors.append(distances[x][0])\n",
        "    return neighbors"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k432BYQgEMBO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "#After sorting the neighbours based on their respective classes, max voting to give the final class of the test instance\n",
        "import operator\n",
        "def getResponse(neighbors):\n",
        "\tclassVotes = {}\n",
        "\tfor x in range(len(neighbors)):\n",
        "\t\tresponse = neighbors[x][-1]\n",
        "\t\tif response in classVotes:\n",
        "\t\t\tclassVotes[response] += 1\n",
        "\t\telse:\n",
        "\t\t\tclassVotes[response] = 1\n",
        "\tsortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)#Sorting it based on votes\n",
        "\treturn sortedVotes[0][0] #Please note we need the class for the top voted class, hence [0][0]#"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rgs6bLvtEOvI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "#Getting the accuracy\n",
        "def getAccuracy(testSet, predictions):\n",
        "\tcorrect = 0\n",
        "\tfor x in range(len(testSet)):\n",
        "\t\tif testSet[x][-1] == predictions[x]:\n",
        "\t\t\tcorrect += 1\n",
        "\treturn (correct/(len(testSet))) * 100.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z62pRUw0EQ-i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# generate predictions\n",
        "from sklearn.metrics import confusion_matrix \n",
        "from sklearn.metrics import accuracy_score \n",
        "from sklearn.metrics import classification_report\n",
        "predictions=[]\n",
        "actual = []\n",
        "k = 3\n",
        "for x in range(len(testSet)):\n",
        "    neighbors = getNeighbors(trainingSet, testSet[x], k)\n",
        "    result = getResponse(neighbors)\n",
        "    predictions.append(result)\n",
        "    actual.append(testSet[x][-1])\n",
        "    #print('> predicted=' + repr(result) + ', actual=' + repr(testSet[x][-1]))\n",
        "#print (predictions)\n",
        "#print(actual)\n",
        "results = confusion_matrix(actual, predictions) \n",
        "  \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEV7C0K08axX",
        "colab_type": "code",
        "outputId": "328a3c75-1099-4392-ec73-638701df864e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        }
      },
      "source": [
        "print ('Confusion Matrix :')\n",
        "print(results) \n",
        "print ('Accuracy Score :',accuracy_score(actual, predictions)) \n",
        "print ('Report : ')\n",
        "print (classification_report(actual, predictions)) \n",
        "accuracy = getAccuracy(testSet, predictions)\n",
        "print('Accuracy: ' + repr(accuracy) + '%')"
      ],
      "execution_count": 704,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix :\n",
            "[[28  8]\n",
            " [ 7 25]]\n",
            "Accuracy Score : 0.7794117647058824\n",
            "Report : \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.80      0.78      0.79        36\n",
            "         1.0       0.76      0.78      0.77        32\n",
            "\n",
            "    accuracy                           0.78        68\n",
            "   macro avg       0.78      0.78      0.78        68\n",
            "weighted avg       0.78      0.78      0.78        68\n",
            "\n",
            "Accuracy: 77.94117647058823%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uuz-uatyEUV7",
        "colab_type": "code",
        "outputId": "dcc3c8b9-7a75-4c1e-8f9c-d9bc807e17a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import make_scorer, confusion_matrix, classification_report\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "\n",
        "nnbc_cm = confusion_matrix(actual,predictions)\n",
        "\n",
        "ax = sns.heatmap(nnbc_cm,annot=True,cmap='coolwarm',fmt='.0f')\n",
        "ax.set_title('KNN Confusion Matrix')\n",
        "ax.set_ylabel('True Label')\n",
        "ax.set_xlabel('Predicted Label')"
      ],
      "execution_count": 705,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Predicted Label')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 705
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wUxhZkLYRnPj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eNZphue0NT9D",
        "colab_type": "text"
      },
      "source": [
        "# LOGISTIC REGRESSION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1z0Ho4URSjgn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class LogisticRegression:  \n",
        "    def __init__(self, lr=0.01, num_iter=100000, fit_intercept=True, verbose=False):  \n",
        "        self.lr = lr  \n",
        "        self.num_iter = num_iter  \n",
        "        self.fit_intercept = fit_intercept  \n",
        "        self.verbose = verbose  \n",
        "     \n",
        "    def __add_intercept(self, X):  \n",
        "        intercept = np.ones((X.shape[0], 1))  \n",
        "        return np.concatenate((intercept, X), axis=1)  \n",
        "     \n",
        "    def __sigmoid(self, z):  \n",
        "        return 1 / (1 + np.exp(-z))  \n",
        "    def __loss(self, h, y):  \n",
        "        return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()  \n",
        "     \n",
        "    def fit(self, X, y):  \n",
        "        if self.fit_intercept:  \n",
        "            X = self.__add_intercept(X)  \n",
        "         \n",
        "        # weights initialization  \n",
        "        self.theta = np.zeros(X.shape[1])  \n",
        "         \n",
        "        for i in range(self.num_iter):  \n",
        "            z = np.dot(X, self.theta)  \n",
        "            h = self.__sigmoid(z)  \n",
        "            gradient = np.dot(X.T, (h - y)) / y.size  \n",
        "            self.theta -= self.lr * gradient  \n",
        "             \n",
        "            z = np.dot(X, self.theta)  \n",
        "            h = self.__sigmoid(z)  \n",
        "            loss = self.__loss(h, y)  \n",
        "                 \n",
        "            if(self.verbose ==True and i % 10000 == 0):  \n",
        "                print(f'loss: {loss} \\t')  \n",
        "     \n",
        "    def predict_prob(self, X):  \n",
        "        if self.fit_intercept:  \n",
        "            X = self.__add_intercept(X)  \n",
        "     \n",
        "        return self.__sigmoid(np.dot(X, self.theta))  \n",
        "     \n",
        "    def predict(self, X):  \n",
        "        return self.predict_prob(X).round()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZspd6bzTMDa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df= pd.read_csv(url, names=names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CAClskrmTOyW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# print(df)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NI-APvrzVJDM",
        "colab": {}
      },
      "source": [
        "# remove null values as less values\n",
        "df = df[~df.isin(['?'])]    \n",
        "#dropping row\n",
        "df = df.dropna(axis=0) \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T_qOj3AANbNH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = pd.get_dummies(df['cp'], prefix = \"cp\")\n",
        "b = pd.get_dummies(df['thal'], prefix = \"thal\")\n",
        "c = pd.get_dummies(df['slope'], prefix = \"slope\")\n",
        "d= pd.get_dummies(df['ca'], prefix = \"ca\")\n",
        "frames = [df, a, b, c,d]\n",
        "df = pd.concat(frames, axis = 1)\n",
        "df = df.drop(columns = ['cp', 'thal', 'slope', 'ca'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QDRTz3OZQSTX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#15613"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-7k2_9tTElI",
        "colab_type": "code",
        "outputId": "a5f47176-25b3-4cbb-bc48-3b9a5e63a373",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 712,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(297, 24)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 712
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emHC5Aibrc6E",
        "colab_type": "code",
        "outputId": "c389ee27-73c0-43f1-f6d6-323fca26c1d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "df['class'].value_counts()"
      ],
      "execution_count": 713,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    160\n",
              "1     54\n",
              "3     35\n",
              "2     35\n",
              "4     13\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 713
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X41szlfTS5w_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['class'] = df['class'].apply(lambda x : 1 if x >= 1 else 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AikHrdVMTFpQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.apply(pd.to_numeric)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "id7kriiSTl76",
        "colab_type": "code",
        "outputId": "09e625d8-e73b-419b-997b-daf9f35c3d8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "df['class'].value_counts()"
      ],
      "execution_count": 716,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    160\n",
              "1    137\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 716
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kQSmJ87RsVS7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y = df['class']\n",
        "X_original = df.drop(['class','oldpeak'], axis = 1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dpE_tqZ1rOzc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tw6SyPKPsJXO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X= (X_original - np.min(X_original)) / (np.max(X_original) - np.min(X_original))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q6sfvYvNQb5v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create X and Y datasets for training\n",
        "from sklearn import model_selection\n",
        "\n",
        "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, Y, test_size = 0.3)\n",
        "#reset index"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XBvsJewQ1Us6",
        "colab_type": "code",
        "outputId": "be125775-ad24-40b0-c67e-b166f988ce08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(X_train)"
      ],
      "execution_count": 720,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 720
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJqWnF8e77eF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train.reset_index(inplace=True, drop=True)\n",
        "y_test.reset_index(inplace=True, drop=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2089bDRxS4gS",
        "colab_type": "code",
        "outputId": "893e6f2f-2e16-4a2b-88c1-34de2c7590d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "model = LogisticRegression(lr=0.001, num_iter=10000)  \n",
        "get_ipython().magic(u'time model.fit(X_train, y_train)')\n",
        "\n",
        "preds_test = model.predict(X_test)\n",
        "preds_train = model.predict(X_train)\n",
        "\n",
        "\n"
      ],
      "execution_count": 722,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 12.2 s, sys: 19.3 ms, total: 12.3 s\n",
            "Wall time: 12.3 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7FjG6NxWTMk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "q = pd.DataFrame(np.around(preds_test)) #prediciton\n",
        "#print(q)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TIp7mhiytEoh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "q['train']=pd.DataFrame(np.around(preds_train))\n",
        "q['test class']=y_test\n",
        "#print(q)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zz3V9ptjy_aI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLzdnXRXtL4N",
        "colab_type": "code",
        "outputId": "eac15ee4-d7e2-4998-86e8-a5c1d4764176",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "q['class train']=y_train\n",
        "#print(q)\n",
        "print('Test Accuracy:',q.loc[q[0]==q['test class']].shape[0] / q.shape[0] * 100)\n",
        "\n",
        "from sklearn import metrics\n",
        "print('Test Precision Score: ',metrics.precision_score(y_test, preds_test))\n",
        "print('Test Recall Score: ',metrics.recall_score(y_test, preds_test))\n",
        "print('Test f1 Score: ',metrics.f1_score(y_test, preds_test))"
      ],
      "execution_count": 725,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Accuracy: 86.66666666666667\n",
            "Test Precision Score:  0.9166666666666666\n",
            "Test Recall Score:  0.7857142857142857\n",
            "Test f1 Score:  0.8461538461538461\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2u6nef2EoXj",
        "colab_type": "code",
        "outputId": "c85a02d2-aade-446c-b14b-20a4ea15f84d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        }
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import make_scorer, confusion_matrix, classification_report\n",
        "import seaborn as sns\n",
        "print(classification_report(y_test, preds_test))\n",
        "\n",
        "\n",
        "lr_cm = confusion_matrix(y_test, preds_test)\n",
        "\n",
        "ax = sns.heatmap(lr_cm,annot=True,cmap='coolwarm',fmt='.0f')\n",
        "ax.set_title('Logistic Regression Confusion Matrix')\n",
        "ax.set_ylabel('True Label')\n",
        "ax.set_xlabel('Predicted Label')\n"
      ],
      "execution_count": 726,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.94      0.88        48\n",
            "           1       0.92      0.79      0.85        42\n",
            "\n",
            "    accuracy                           0.87        90\n",
            "   macro avg       0.88      0.86      0.86        90\n",
            "weighted avg       0.87      0.87      0.87        90\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Predicted Label')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 726
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFZceKS5y6x_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4U0CsDCn5O5o",
        "colab_type": "text"
      },
      "source": [
        "# SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wrj6hSj05OHu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import math\n",
        "\n",
        "'---------------------------------------------------------'\n",
        "\n",
        "\n",
        "# Functions\n",
        "\n",
        "def threshold(array):\n",
        "    for i in range(len(array)):\n",
        "        if array[i] > 0:\n",
        "            array[i] = 1\n",
        "    return array\n",
        "\n",
        "\n",
        "'---------------------------------------------------------'\n",
        "\n",
        "\n",
        "# class\n",
        "\n",
        "class SVM:\n",
        "    def __init__(self, learning_rate=0.001, lambda_param=0.01, iterations=1000):\n",
        "        self.lr = learning_rate\n",
        "        self.lambda_param = lambda_param\n",
        "        self.iterations = iterations\n",
        "        self.w = None\n",
        "        self.b = None\n",
        "\n",
        "    def fit(self, x_train, y_train):\n",
        "        y_ = np.where(y_train <= 0, -1, 1)\n",
        "        numberOfSamples, numberOfFeatures = x_train.shape\n",
        "        # print(x_train)\n",
        "\n",
        "        self.w = np.zeros(numberOfFeatures)\n",
        "        # print(self.w)\n",
        "        self.b = 0\n",
        "\n",
        "        for _ in range(self.iterations):\n",
        "            for index, x_i in enumerate(x_train):\n",
        "                # print('yindex--',y_[index])\n",
        "                # print('x_i--', x_i)\n",
        "                # print()\n",
        "                # print('dot--', np.dot(x_i,self.w))\n",
        "                condition = y_[index] * (np.dot(x_i, self.w)) - self.b\n",
        "                # print('condition--',condition)\n",
        "                # print('x_i.shape',x_i.shape)\n",
        "                # print('y_index.shape',y_[index].shape)\n",
        "                # print(y_[index])\n",
        "\n",
        "                if condition >= 1:\n",
        "                    self.w = self.w - self.lr * (2 * self.lambda_param * self.w)\n",
        "                else:\n",
        "                    self.w = self.w - self.lr * (2 * self.lambda_param * self.w - (x_i * y_[index]))\n",
        "                    self.b = self.b - self.lr * y_[index]\n",
        "\n",
        "    def predict(self, x_test):\n",
        "        linear_output = np.dot(self.w, x_test) - self.b\n",
        "        return np.sign(linear_output)\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OFnaku5K5WYG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = np.genfromtxt(url, delimiter=',')\n",
        "\n",
        "t = 100  # <-------------------------------------no. of training samples\n",
        "f = [2,3,5,7,11,13] # <-----------------feature selection\n",
        "\n",
        "x_train = data[0:t, f]\n",
        "\n",
        "y1_train = data[0:t, 13:14]\n",
        "y_train = threshold(y1_train)\n",
        "#print(y1_train)\n",
        "\n",
        "x_test = data[t + 1:301, f]\n",
        "y1_test = data[t + 1:301, 13:14]\n",
        "y_test = threshold(y1_test)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wkDvdIOr5aDp",
        "colab_type": "code",
        "outputId": "a9287354-a19a-4121-d9a7-0693b1d26da6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "svm = SVM()\n",
        "svm.fit(x_train, y_train)\n",
        "\n",
        "print('weight--', svm.w)\n",
        "\n",
        "w1 = np.reshape(svm.w, (len(f), 1))\n",
        "\n",
        "y_pre = []\n",
        "sum = 0\n",
        "count = 0\n",
        "for i in range(len(y1_test)):\n",
        "    sum = sum + (np.where((np.dot(x_test[i], w1)) <= 0, 0, 1))\n",
        "\n",
        "    if (np.where((np.dot(x_test[i], w1)) <= 0, 0, 1)) == y_test[i]:\n",
        "        count = count + 1\n",
        "\n",
        "    #print(\"actual: \", y_test[i], \"predicted: \", (np.where((np.dot(x_test[i], w1)) <= 0, 0, 1)))\n",
        "    y_pre.append(np.where((np.dot(x_test[i], w1)) <= 0, 0, 1))\n",
        "\n",
        "print('count', count)\n",
        "\n"
      ],
      "execution_count": 729,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "weight-- [ 0.86152406  0.02704144 -0.01301449 -0.05197008  0.90939667  6.09215396]\n",
            "count 188\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0xK3Wvg5p1D",
        "colab_type": "code",
        "outputId": "ea833f9c-fc0b-4ed0-a09e-7363e77be792",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print('SVM TEST Accuracy using selected features=', 100 * count / len(y1_test))"
      ],
      "execution_count": 730,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SVM TEST Accuracy using selected features= 94.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qaOoAWgcFGb9",
        "colab_type": "code",
        "outputId": "b1db744b-6ec1-4fe8-bbab-ac878f446edb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        }
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import make_scorer, confusion_matrix, classification_report\n",
        "import seaborn as sns\n",
        "print(classification_report(y_test, y_pre))\n",
        "\n",
        "\n",
        "lr_cm = confusion_matrix(y_test, y_pre)\n",
        "\n",
        "ax = sns.heatmap(lr_cm,annot=True,cmap='coolwarm',fmt='.0f')\n",
        "ax.set_title('SVM Confusion Matrix')\n",
        "ax.set_ylabel('True Label')\n",
        "ax.set_xlabel('Predicted Label')\n"
      ],
      "execution_count": 731,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      0.89      0.94       105\n",
            "         1.0       0.89      1.00      0.94        95\n",
            "\n",
            "    accuracy                           0.94       200\n",
            "   macro avg       0.94      0.94      0.94       200\n",
            "weighted avg       0.95      0.94      0.94       200\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Predicted Label')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 731
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R35MnLiyRtI-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}