HeartDisease.ipynb

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  "metadata": {
    "colab": {
      "name": "HeartDisease.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "Jw3bh_Ss3DEN"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "TPU"
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  "cells": [
    {
      "metadata": {
        "id": "DAdOAWqk393f",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Heart Disease Prediction"
      ]
    },
    {
      "metadata": {
        "id": "3Tma3HHX7Pw2",
        "colab_type": "code",
        "outputId": "54d7cc28-d9b5-43fe-884b-3401edd3eb49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install seaborn==0.9.0"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: seaborn==0.9.0 in /usr/local/lib/python3.6/dist-packages (0.9.0)\n",
            "Requirement already satisfied: matplotlib>=1.4.3 in /usr/local/lib/python3.6/dist-packages (from seaborn==0.9.0) (3.0.3)\n",
            "Requirement already satisfied: numpy>=1.9.3 in /usr/local/lib/python3.6/dist-packages (from seaborn==0.9.0) (1.14.6)\n",
            "Requirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from seaborn==0.9.0) (1.1.0)\n",
            "Requirement already satisfied: pandas>=0.15.2 in /usr/local/lib/python3.6/dist-packages (from seaborn==0.9.0) (0.22.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn==0.9.0) (1.0.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn==0.9.0) (2.5.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn==0.9.0) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn==0.9.0) (2.3.1)\n",
            "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.15.2->seaborn==0.9.0) (2018.9)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.3->seaborn==0.9.0) (40.8.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib>=1.4.3->seaborn==0.9.0) (1.11.0)\n"
          ],
          "name": "stdout"
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    {
      "metadata": {
        "id": "N2pB57UN7XZk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from fancyimpute import KNN\n",
        "from scipy.stats import chi2_contingency\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from random import randrange,uniform\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import tree\n",
        "from sklearn.tree import export_graphviz               \n",
        "from sklearn.metrics import accuracy_score            \n",
        "from sklearn.metrics import confusion_matrix           \n",
        "from sklearn.ensemble import RandomForestClassifier    \n",
        "import statsmodels.api as sn                           \n",
        "from sklearn.neighbors import KNeighborsClassifier     \n",
        "from sklearn.naive_bayes import GaussianNB             \n",
        "from sklearn import model_selection                  \n",
        "from sklearn.metrics import classification_report,roc_auc_score,roc_curve \n",
        "from sklearn.metrics import classification_report      \n",
        "import pickle                                        \n",
        "import statsmodels.api as sm\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor  \n",
        "from statsmodels.tools.tools import add_constant\n",
        "np.random.seed(123) \n",
        "pd.options.mode.chained_assignment = None  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "P_cHSDi3Knhq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"heart.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "56LyBHAAK1O6",
        "colab_type": "code",
        "outputId": "5cfe0a55-57fb-4383-f48d-be92567546b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
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              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
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              "      <td>1</td>\n",
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              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
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              "      <th>4</th>\n",
              "      <td>57</td>\n",
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              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
              "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
              "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
              "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
              "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   0     1       1  \n",
              "1   0     2       1  \n",
              "2   0     2       1  \n",
              "3   0     2       1  \n",
              "4   0     2       1  "
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 29
        }
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "cell_type": "code",
      "source": [
        "data.sample(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <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",
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              "      <th>11</th>\n",
              "      <td>48</td>\n",
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              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>275</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>139</td>\n",
              "      <td>0</td>\n",
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              "      <th>292</th>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>170</td>\n",
              "      <td>225</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>146</td>\n",
              "      <td>1</td>\n",
              "      <td>2.8</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <th>269</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>103</td>\n",
              "      <td>1</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>268</th>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>122</td>\n",
              "      <td>286</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>116</td>\n",
              "      <td>1</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112</td>\n",
              "      <td>160</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>138</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "11    48    0   2       130   275    0        1      139      0      0.2   \n",
              "292   58    0   0       170   225    1        0      146      1      2.8   \n",
              "269   56    1   0       130   283    1        0      103      1      1.6   \n",
              "268   54    1   0       122   286    0        0      116      1      3.2   \n",
              "94    45    0   1       112   160    0        1      138      0      0.0   \n",
              "\n",
              "     slope  ca  thal  target  \n",
              "11       2   0     2       1  \n",
              "292      1   2     1       0  \n",
              "269      0   0     3       0  \n",
              "268      1   2     2       0  \n",
              "94       1   0     2       1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "id": "XMBZMcpa1_vs",
        "colab_type": "code",
        "outputId": "d5c7c74b-4bc6-4217-f8d5-325ead854cd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp    trestbps        chol         fbs  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
              "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
              "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
              "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
              "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
              "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
              "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
              "\n",
              "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
              "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
              "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
              "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
              "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
              "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
              "\n",
              "             thal      target  \n",
              "count  303.000000  303.000000  \n",
              "mean     2.313531    0.544554  \n",
              "std      0.612277    0.498835  \n",
              "min      0.000000    0.000000  \n",
              "25%      2.000000    0.000000  \n",
              "50%      2.000000    1.000000  \n",
              "75%      3.000000    1.000000  \n",
              "max      3.000000    1.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "FXjX2SrV2EiE",
        "colab_type": "code",
        "outputId": "c8be2344-1434-4188-dea6-51255b6419a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            "age         303 non-null int64\n",
            "sex         303 non-null int64\n",
            "cp          303 non-null int64\n",
            "trestbps    303 non-null int64\n",
            "chol        303 non-null int64\n",
            "fbs         303 non-null int64\n",
            "restecg     303 non-null int64\n",
            "thalach     303 non-null int64\n",
            "exang       303 non-null int64\n",
            "oldpeak     303 non-null float64\n",
            "slope       303 non-null int64\n",
            "ca          303 non-null int64\n",
            "thal        303 non-null int64\n",
            "target      303 non-null int64\n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.2 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "tMEhTXXo8Puw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# About the dataset"
      ]
    },
    {
      "metadata": {
        "id": "e0EudXgP2OAo",
        "colab_type": "code",
        "outputId": "2e7816e3-3531-4e25-fad7-518e12c55a2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        }
      },
      "cell_type": "code",
      "source": [
        "info = [\"age\",\"1: male, 0: female\",\"chest pain type, 1: typical angina, 2: atypical angina, 3: non-anginal pain, 4: asymptomatic\",\"resting blood pressure\",\" serum cholestoral in mg/dl\",\"fasting blood sugar > 120 mg/dl\",\"resting electrocardiographic results (values 0,1,2)\",\" maximum heart rate achieved\",\"exercise induced angina\",\"oldpeak = ST depression induced by exercise relative to rest\",\"the slope of the peak exercise ST segment\",\"number of major vessels (0-3) colored by flourosopy\",\"thal: 3 = normal; 6 = fixed defect; 7 = reversable defect\"]\n",
        "\n",
        "\n",
        "\n",
        "for i in range(len(info)):\n",
        "    print(data.columns[i]+\":\\t\\t\\t\"+info[i])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "age:\t\t\tage\n",
            "sex:\t\t\t1: male, 0: female\n",
            "cp:\t\t\tchest pain type, 1: typical angina, 2: atypical angina, 3: non-anginal pain, 4: asymptomatic\n",
            "trestbps:\t\t\tresting blood pressure\n",
            "chol:\t\t\t serum cholestoral in mg/dl\n",
            "fbs:\t\t\tfasting blood sugar > 120 mg/dl\n",
            "restecg:\t\t\tresting electrocardiographic results (values 0,1,2)\n",
            "thalach:\t\t\t maximum heart rate achieved\n",
            "exang:\t\t\texercise induced angina\n",
            "oldpeak:\t\t\toldpeak = ST depression induced by exercise relative to rest\n",
            "slope:\t\t\tthe slope of the peak exercise ST segment\n",
            "ca:\t\t\tnumber of major vessels (0-3) colored by flourosopy\n",
            "thal:\t\t\tthal: 3 = normal; 6 = fixed defect; 7 = reversable defect\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "37YScZygM99i",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        " -----------------------------------------------------------------------------------"
      ]
    },
    {
      "metadata": {
        "id": "W3xo6VriT9Ys",
        "colab_type": "code",
        "outputId": "d39171ce-5c2c-4cdd-d22d-f5b6f9c46e29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "type(data)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "FVaK7xbNTvhl",
        "colab_type": "code",
        "outputId": "f2dcb969-2391-409c-b825-63a503893339",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(303, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "id": "qBD6QlHjM_Ui",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        " -----------------------------------------------------------------------------------"
      ]
    },
    {
      "metadata": {
        "id": "H7en83n-MiBq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        " -----------------------------------------------------------------------------------"
      ]
    },
    {
      "metadata": {
        "id": "UO1zHahz2pC1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "metadata": {
        "id": "SYJpWuw4UIAj",
        "colab_type": "code",
        "outputId": "7b6e8468-251a-4745-c12c-eb3c2f325d23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "cell_type": "code",
      "source": [
        "y = data[\"target\"]\n",
        "\n",
        "sns.countplot(y)\n",
        "\n",
        "\n",
        "target_temp = data.target.value_counts()\n",
        "\n",
        "print(target_temp)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1    165\n",
            "0    138\n",
            "Name: target, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktQ05wIiyRFJbkyy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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "kmape3H824zt",
        "colab_type": "code",
        "outputId": "20aa3a53-8096-4223-c114-899abe6201b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "cell_type": "code",
      "source": [
        "sns.barplot(data[\"sex\"],data[\"target\"])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6835e8ccf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k3U+dlUJVnQC2AvuB64G9VXUwyY4kGwGSPC3JJPAbwPuSHOwqjyRpfl1++oiq2gfsmzG2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\nz5tnxOP9SZUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Jw3bh_Ss3DEN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### We notice, that females are more likely to have heart problems than males"
      ]
    },
    {
      "metadata": {
        "id": "9JZUEzV06kAp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Making the data more simple"
      ]
    },
    {
      "metadata": {
        "id": "hgjl9v0CK-Hz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data.columns = ['age', 'sex', 'chest_pain_type', 'resting_blood_pressure', 'cholesterol', 'fasting_blood_sugar', 'rest_ecg', 'max_heart_rate_achieved',\n",
        "       'exercise_induced_angina', 'st_depression', 'st_slope', 'num_major_vessels', 'thalassemia', 'target']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UCzP4R2wLkep",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data['sex'][data['sex'] == 0] = 'female'\n",
        "data['sex'][data['sex'] == 1] = 'male'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jo8QCvBqL5VK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data['chest_pain_type'][data['chest_pain_type'] == 1] = 'typical angina'\n",
        "data['chest_pain_type'][data['chest_pain_type'] == 2] = 'atypical angina'\n",
        "data['chest_pain_type'][data['chest_pain_type'] == 3] = 'non-anginal pain'\n",
        "data['chest_pain_type'][data['chest_pain_type'] == 4] = 'asymptomatic'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eVmmvI9RL-MC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data['fasting_blood_sugar'][data['fasting_blood_sugar'] == 0] = 'lower than 120mg/ml'\n",
        "data['fasting_blood_sugar'][data['fasting_blood_sugar'] == 1] = 'greater than 120mg/ml'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "44y-ppHQME_T",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data['rest_ecg'][data['rest_ecg'] == 0] = 'normal'\n",
        "data['rest_ecg'][data['rest_ecg'] == 1] = 'ST-T wave abnormality'\n",
        "data['rest_ecg'][data['rest_ecg'] == 2] = 'left ventricular hypertrophy'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BQLdXMovMNZS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data['exercise_induced_angina'][data['exercise_induced_angina'] == 0] = 'no'\n",
        "data['exercise_induced_angina'][data['exercise_induced_angina'] == 1] = 'yes'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EmQmMyvJMS3i",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data['st_slope'][data['st_slope'] == 1] = 'upsloping'\n",
        "data['st_slope'][data['st_slope'] == 2] = 'flat'\n",
        "data['st_slope'][data['st_slope'] == 3] = 'downsloping'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mk1DE7sIMYaL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data['thalassemia'][data['thalassemia'] == 1] = 'normal'\n",
        "data['thalassemia'][data['thalassemia'] == 2] = 'fixed defect'\n",
        "data['thalassemia'][data['thalassemia'] == 3] = 'reversable defect'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cfOVgd60McFy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data['target'][data['target'] == 0] = 'no'\n",
        "data['target'][data['target'] == 1] = 'yes'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gP7AxKAA4Os0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Percentage of patients with or without heart problems"
      ]
    },
    {
      "metadata": {
        "id": "m2WU6_0uW8U2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "assigning levels to categories"
      ]
    },
    {
      "metadata": {
        "id": "0ZTQitOVMhtZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "list = []\n",
        "for i in range(0,data.shape[1]):\n",
        "  if(data.iloc[:,i].dtypes == 'object'):\n",
        "    data.iloc[:,i] = pd.Categorical(data.iloc[:,i])\n",
        "    data.iloc[:,i] = data.iloc[:,i].cat.codes\n",
        "    data.iloc[:,i] = data.iloc[:,i].astype('object')\n",
        "    list.append(data.columns[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gH6VHHBfXfGt",
        "colab_type": "code",
        "outputId": "a23c3868-90d5-4d41-c9c8-3b626beba5f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "cell_type": "code",
      "source": [
        "sns.countplot(x='target',data=data,palette=\"bwr\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmuZdQCQ5IsnNSdYl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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5poLMj9BYZhd",
        "colab_type": "code",
        "outputId": "a80f5be6-ceb0-4e21-8a04-cefc27051c68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "countFemale = len(data[data.sex == 0])\n",
        "countMale = len(data[data.sex == 1])\n",
        "print(\"Percentage of Female Patients:{:.2f}%\".format((countFemale)/(len(data.sex))*100))\n",
        "print(\"Percentage of Male Patients:{:.2f}%\".format((countMale)/(len(data.sex))*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Percentage of Female Patients:31.68%\n",
            "Percentage of Male Patients:68.32%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "gCIjbgG48th5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "----------------------------------------------------------------------------------------------------------------------------\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "WOUyPl4caBV-",
        "colab_type": "code",
        "outputId": "a7a7aeae-2d18-43b9-b9da-6fb2ab062319",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "countNoDisease = len(data[data.target == 0])\n",
        "countHaveDisease = len(data[data.target == 1])\n",
        "print(\"Percentage of Patients Haven't Heart Disease: {:.2f}%\".format((countNoDisease / (len(data.target))*100)))\n",
        "print(\"Percentage of Patients Have Heart Disease: {:.2f}%\".format((countHaveDisease / (len(data.target))*100)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Percentage of Patients Haven't Heart Disease: 45.54%\n",
            "Percentage of Patients Have Heart Disease: 54.46%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qCdKTb5Fa5HW",
        "colab_type": "code",
        "outputId": "73eaac8a-0f4a-469a-adbe-a8b1796f5160",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        }
      },
      "cell_type": "code",
      "source": [
        "data.groupby('target').mean()"
      ],
      "execution_count": 0,
      "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>resting_blood_pressure</th>\n",
              "      <th>cholesterol</th>\n",
              "      <th>max_heart_rate_achieved</th>\n",
              "      <th>st_depression</th>\n",
              "      <th>num_major_vessels</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56.601449</td>\n",
              "      <td>134.398551</td>\n",
              "      <td>251.086957</td>\n",
              "      <td>139.101449</td>\n",
              "      <td>1.585507</td>\n",
              "      <td>1.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>52.496970</td>\n",
              "      <td>129.303030</td>\n",
              "      <td>242.230303</td>\n",
              "      <td>158.466667</td>\n",
              "      <td>0.583030</td>\n",
              "      <td>0.363636</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age  resting_blood_pressure  cholesterol  \\\n",
              "target                                                   \n",
              "0       56.601449              134.398551   251.086957   \n",
              "1       52.496970              129.303030   242.230303   \n",
              "\n",
              "        max_heart_rate_achieved  st_depression  num_major_vessels  \n",
              "target                                                             \n",
              "0                    139.101449       1.585507           1.166667  \n",
              "1                    158.466667       0.583030           0.363636  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "metadata": {
        "id": "nOqH-bko46F1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "------------------------------------------------------------------------------------"
      ]
    },
    {
      "metadata": {
        "id": "T9b3omGK5PK9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Heart disease frequency for ages"
      ]
    },
    {
      "metadata": {
        "id": "_On_mtDubRc2",
        "colab_type": "code",
        "outputId": "e8c0df1d-2c1f-491f-af4b-d8631ce5c2ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        }
      },
      "cell_type": "code",
      "source": [
        "pd.crosstab(data.age,data.target).plot(kind=\"bar\",figsize=(20,6))\n",
        "plt.title('Heart Disease Frequency for Ages')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.savefig('heartDiseaseAndAges.png')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAAAwSPEIAAAAgEGKRwAAAAAMUjwCAAAAYJDiEQAAAACDFI8AAAAAGKR4BAAA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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "WMorBBJI5Fdx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Heart Disease Frequency for male and female"
      ]
    },
    {
      "metadata": {
        "id": "lt_bZZgybaqm",
        "colab_type": "code",
        "outputId": "02c169fc-4f37-4560-cad1-441db7f64672",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "cell_type": "code",
      "source": [
        "pd.crosstab(data.sex,data.target).plot(kind=\"bar\",figsize=(15,6),color=['blue','#AA1111' ])\n",
        "plt.title('Heart Disease Frequency for Sex')\n",
        "plt.xlabel('Sex (0 = Female, 1 = Male)')\n",
        "plt.xticks(rotation=0)\n",
        "plt.legend([\"Haven't Disease\", \"Have Disease\"])\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XI2JSRAyIiI0j4p6IeLn4d6MV7V+SJEmS9He1Br8PM3PewoWIaEfle/xW1EXA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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "jJPjVHPQ5h0F",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Thalassemia vs cholesterol"
      ]
    },
    {
      "metadata": {
        "id": "YbTLzjBkbrZm",
        "colab_type": "code",
        "outputId": "9a53024c-fd88-4639-afb3-2150ccb47a92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        }
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "sns.scatterplot(x='cholesterol',y='thalassemia',data=data,hue='target')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xFVw8bwynHzai5yQr6Pfws7OnU9rPPfKg38sXjx3PYbmTFL/X8PWPHtrvCcCBrMDn4bKj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\nFeGkbmZmVhH/AxnDP+ZdfaXeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Ik6D7o555tpN",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Thalassemia vs resting blood pressure"
      ]
    },
    {
      "metadata": {
        "id": "Kypuhyz65z0_",
        "colab_type": "code",
        "outputId": "a04c6314-4ef7-476a-dfa6-1f14c075eed7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        }
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "sns.scatterplot(x='thalassemia',y='resting_blood_pressure',data=data,hue='target')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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myZYvhIz85OUkIilv9oR8yvL2zaRPT3NxzUmTSXd4T/mBzgCYAxwf/9oS24lO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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "z6DdBELS50rl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Age vs Maximum heart disease rate"
      ]
    },
    {
      "metadata": {
        "id": "8nbmQBuJdSSm",
        "colab_type": "code",
        "outputId": "89a1f5ee-f7ca-47fe-d97d-6d8be0a75307",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "cell_type": "code",
      "source": [
        "plt.scatter(x=data.age[data.target==1], y=data.thalassemia[(data.target==1)], c=\"green\")\n",
        "plt.scatter(x=data.age[data.target==0], y=data.thalassemia[(data.target==0)])\n",
        "plt.legend([\"Disease\", \"Not Disease\"])\n",
        "plt.xlabel(\"Age\")\n",
        "plt.ylabel(\"Maximum Heart Rate\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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C8+v95QML4rcT+qpuZjnEEkKpuz8TskoFcFC9+33jZQ24+0x3L3H3kp49eyaya0lRYdfw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\nREQCSgoiIhL4/1SurXn/X9YsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "78WhF_mXAh_n",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Fasting Blood sugar Data"
      ]
    },
    {
      "metadata": {
        "id": "pVgCUbYpAmVv",
        "colab_type": "code",
        "outputId": "1ababa2e-ef00-4f42-b57a-78da40c4ee5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        }
      },
      "cell_type": "code",
      "source": [
        "pd.crosstab(data.fasting_blood_sugar,data.target).plot(kind=\"bar\",figsize=(20,10),color=['#4286f4','#f49242'])\n",
        "plt.title(\"Heart disease according to FBS\")\n",
        "plt.xlabel('FBS- (Fasting Blood Sugar > 120 mg/dl) (1 = true; 0 = false)')\n",
        "plt.xticks(rotation=90)\n",
        "plt.legend([\"Haven't Disease\", \"Have Disease\"])\n",
        "plt.ylabel('Disease or not')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AdCYqAQAAANCZqAQAAABAZ6ISAAAAAJ2JSgAAAAB0JioBAAAA0Jmo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\n4Uj0YwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "y5dk7RriDFIY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Missing Value Analysis"
      ]
    },
    {
      "metadata": {
        "id": "CmMIJysnDIlQ",
        "colab_type": "code",
        "outputId": "c7464558-45bb-464f-916f-2c6a4e3efa39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                        0\n",
              "sex                        0\n",
              "chest_pain_type            0\n",
              "resting_blood_pressure     0\n",
              "cholesterol                0\n",
              "fasting_blood_sugar        0\n",
              "rest_ecg                   0\n",
              "max_heart_rate_achieved    0\n",
              "exercise_induced_angina    0\n",
              "st_depression              0\n",
              "st_slope                   0\n",
              "num_major_vessels          0\n",
              "thalassemia                0\n",
              "target                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "metadata": {
        "id": "D8hBV-iVEi0p",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Feature Selection"
      ]
    },
    {
      "metadata": {
        "id": "3HOFJlnbER0g",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "names=['age','resting_blood_pressure','cholesterol','max_heart_rate_achieved','st_depression','num_major_vessels']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ae11mBw8D5xI",
        "colab_type": "code",
        "outputId": "42c80979-1ae1-4aa3-b13e-28dbf99d54b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        }
      },
      "cell_type": "code",
      "source": [
        "#Set the width and height of the plot\n",
        "f, ax = plt.subplots(figsize=(7, 5))\n",
        "\n",
        "#Correlation plot\n",
        "df_corr = data.loc[:,names]\n",
        "#Generate correlation matrix\n",
        "corr = df_corr.corr()\n",
        "\n",
        "#Plot using seaborn library\n",
        "sns.heatmap(corr, annot = True, cmap='coolwarm',linewidths=.1)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NUEdEaoiIH/bE4eeL9i1SDwgCVjrMCxKREtb/ocANwLac614pbalQSiml3MwdZ38YYzJF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            "text/plain": [
              "<Figure size 504x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "oVxF3aFlEcDI",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Correlation analysis"
      ]
    },
    {
      "metadata": {
        "id": "Peozrck8EXtI",
        "colab_type": "code",
        "outputId": "a982b594-64e6-4233-a53c-d0539a7df423",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1927
        }
      },
      "cell_type": "code",
      "source": [
        "df_corr"
      ],
      "execution_count": 0,
      "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>resting_blood_pressure</th>\n",
              "      <th>cholesterol</th>\n",
              "      <th>max_heart_rate_achieved</th>\n",
              "      <th>st_depression</th>\n",
              "      <th>num_major_vessels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>150</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>187</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>172</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>178</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>163</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>57</td>\n",
              "      <td>140</td>\n",
              "      <td>192</td>\n",
              "      <td>148</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>56</td>\n",
              "      <td>140</td>\n",
              "      <td>294</td>\n",
              "      <td>153</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>44</td>\n",
              "      <td>120</td>\n",
              "      <td>263</td>\n",
              "      <td>173</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>52</td>\n",
              "      <td>172</td>\n",
              "      <td>199</td>\n",
              "      <td>162</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>57</td>\n",
              "      <td>150</td>\n",
              "      <td>168</td>\n",
              "      <td>174</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>54</td>\n",
              "      <td>140</td>\n",
              "      <td>239</td>\n",
              "      <td>160</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>48</td>\n",
              "      <td>130</td>\n",
              "      <td>275</td>\n",
              "      <td>139</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>49</td>\n",
              "      <td>130</td>\n",
              "      <td>266</td>\n",
              "      <td>171</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>64</td>\n",
              "      <td>110</td>\n",
              "      <td>211</td>\n",
              "      <td>144</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>58</td>\n",
              "      <td>150</td>\n",
              "      <td>283</td>\n",
              "      <td>162</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>50</td>\n",
              "      <td>120</td>\n",
              "      <td>219</td>\n",
              "      <td>158</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>58</td>\n",
              "      <td>120</td>\n",
              "      <td>340</td>\n",
              "      <td>172</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>66</td>\n",
              "      <td>150</td>\n",
              "      <td>226</td>\n",
              "      <td>114</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>43</td>\n",
              "      <td>150</td>\n",
              "      <td>247</td>\n",
              "      <td>171</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>69</td>\n",
              "      <td>140</td>\n",
              "      <td>239</td>\n",
              "      <td>151</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>59</td>\n",
              "      <td>135</td>\n",
              "      <td>234</td>\n",
              "      <td>161</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>44</td>\n",
              "      <td>130</td>\n",
              "      <td>233</td>\n",
              "      <td>179</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>42</td>\n",
              "      <td>140</td>\n",
              "      <td>226</td>\n",
              "      <td>178</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>61</td>\n",
              "      <td>150</td>\n",
              "      <td>243</td>\n",
              "      <td>137</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>40</td>\n",
              "      <td>140</td>\n",
              "      <td>199</td>\n",
              "      <td>178</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>71</td>\n",
              "      <td>160</td>\n",
              "      <td>302</td>\n",
              "      <td>162</td>\n",
              "      <td>0.4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>59</td>\n",
              "      <td>150</td>\n",
              "      <td>212</td>\n",
              "      <td>157</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>51</td>\n",
              "      <td>110</td>\n",
              "      <td>175</td>\n",
              "      <td>123</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>65</td>\n",
              "      <td>140</td>\n",
              "      <td>417</td>\n",
              "      <td>157</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>53</td>\n",
              "      <td>130</td>\n",
              "      <td>197</td>\n",
              "      <td>152</td>\n",
              "      <td>1.2</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273</th>\n",
              "      <td>58</td>\n",
              "      <td>100</td>\n",
              "      <td>234</td>\n",
              "      <td>156</td>\n",
              "      <td>0.1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>274</th>\n",
              "      <td>47</td>\n",
              "      <td>110</td>\n",
              "      <td>275</td>\n",
              "      <td>118</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>275</th>\n",
              "      <td>52</td>\n",
              "      <td>125</td>\n",
              "      <td>212</td>\n",
              "      <td>168</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>276</th>\n",
              "      <td>58</td>\n",
              "      <td>146</td>\n",
              "      <td>218</td>\n",
              "      <td>105</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>277</th>\n",
              "      <td>57</td>\n",
              "      <td>124</td>\n",
              "      <td>261</td>\n",
              "      <td>141</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>278</th>\n",
              "      <td>58</td>\n",
              "      <td>136</td>\n",
              "      <td>319</td>\n",
              "      <td>152</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>61</td>\n",
              "      <td>138</td>\n",
              "      <td>166</td>\n",
              "      <td>125</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>42</td>\n",
              "      <td>136</td>\n",
              "      <td>315</td>\n",
              "      <td>125</td>\n",
              "      <td>1.8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281</th>\n",
              "      <td>52</td>\n",
              "      <td>128</td>\n",
              "      <td>204</td>\n",
              "      <td>156</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>282</th>\n",
              "      <td>59</td>\n",
              "      <td>126</td>\n",
              "      <td>218</td>\n",
              "      <td>134</td>\n",
              "      <td>2.2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>283</th>\n",
              "      <td>40</td>\n",
              "      <td>152</td>\n",
              "      <td>223</td>\n",
              "      <td>181</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284</th>\n",
              "      <td>61</td>\n",
              "      <td>140</td>\n",
              "      <td>207</td>\n",
              "      <td>138</td>\n",
              "      <td>1.9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285</th>\n",
              "      <td>46</td>\n",
              "      <td>140</td>\n",
              "      <td>311</td>\n",
              "      <td>120</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>286</th>\n",
              "      <td>59</td>\n",
              "      <td>134</td>\n",
              "      <td>204</td>\n",
              "      <td>162</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>57</td>\n",
              "      <td>154</td>\n",
              "      <td>232</td>\n",
              "      <td>164</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>288</th>\n",
              "      <td>57</td>\n",
              "      <td>110</td>\n",
              "      <td>335</td>\n",
              "      <td>143</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289</th>\n",
              "      <td>55</td>\n",
              "      <td>128</td>\n",
              "      <td>205</td>\n",
              "      <td>130</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>290</th>\n",
              "      <td>61</td>\n",
              "      <td>148</td>\n",
              "      <td>203</td>\n",
              "      <td>161</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>291</th>\n",
              "      <td>58</td>\n",
              "      <td>114</td>\n",
              "      <td>318</td>\n",
              "      <td>140</td>\n",
              "      <td>4.4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>58</td>\n",
              "      <td>170</td>\n",
              "      <td>225</td>\n",
              "      <td>146</td>\n",
              "      <td>2.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>67</td>\n",
              "      <td>152</td>\n",
              "      <td>212</td>\n",
              "      <td>150</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>44</td>\n",
              "      <td>120</td>\n",
              "      <td>169</td>\n",
              "      <td>144</td>\n",
              "      <td>2.8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>63</td>\n",
              "      <td>140</td>\n",
              "      <td>187</td>\n",
              "      <td>144</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>63</td>\n",
              "      <td>124</td>\n",
              "      <td>197</td>\n",
              "      <td>136</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>59</td>\n",
              "      <td>164</td>\n",
              "      <td>176</td>\n",
              "      <td>90</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>57</td>\n",
              "      <td>140</td>\n",
              "      <td>241</td>\n",
              "      <td>123</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>45</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>132</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>68</td>\n",
              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>141</td>\n",
              "      <td>3.4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>115</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>57</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>174</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>303 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  resting_blood_pressure  cholesterol  max_heart_rate_achieved  \\\n",
              "0     63                     145          233                      150   \n",
              "1     37                     130          250                      187   \n",
              "2     41                     130          204                      172   \n",
              "3     56                     120          236                      178   \n",
              "4     57                     120          354                      163   \n",
              "5     57                     140          192                      148   \n",
              "6     56                     140          294                      153   \n",
              "7     44                     120          263                      173   \n",
              "8     52                     172          199                      162   \n",
              "9     57                     150          168                      174   \n",
              "10    54                     140          239                      160   \n",
              "11    48                     130          275                      139   \n",
              "12    49                     130          266                      171   \n",
              "13    64                     110          211                      144   \n",
              "14    58                     150          283                      162   \n",
              "15    50                     120          219                      158   \n",
              "16    58                     120          340                      172   \n",
              "17    66                     150          226                      114   \n",
              "18    43                     150          247                      171   \n",
              "19    69                     140          239                      151   \n",
              "20    59                     135          234                      161   \n",
              "21    44                     130          233                      179   \n",
              "22    42                     140          226                      178   \n",
              "23    61                     150          243                      137   \n",
              "24    40                     140          199                      178   \n",
              "25    71                     160          302                      162   \n",
              "26    59                     150          212                      157   \n",
              "27    51                     110          175                      123   \n",
              "28    65                     140          417                      157   \n",
              "29    53                     130          197                      152   \n",
              "..   ...                     ...          ...                      ...   \n",
              "273   58                     100          234                      156   \n",
              "274   47                     110          275                      118   \n",
              "275   52                     125          212                      168   \n",
              "276   58                     146          218                      105   \n",
              "277   57                     124          261                      141   \n",
              "278   58                     136          319                      152   \n",
              "279   61                     138          166                      125   \n",
              "280   42                     136          315                      125   \n",
              "281   52                     128          204                      156   \n",
              "282   59                     126          218                      134   \n",
              "283   40                     152          223                      181   \n",
              "284   61                     140          207                      138   \n",
              "285   46                     140          311                      120   \n",
              "286   59                     134          204                      162   \n",
              "287   57                     154          232                      164   \n",
              "288   57                     110          335                      143   \n",
              "289   55                     128          205                      130   \n",
              "290   61                     148          203                      161   \n",
              "291   58                     114          318                      140   \n",
              "292   58                     170          225                      146   \n",
              "293   67                     152          212                      150   \n",
              "294   44                     120          169                      144   \n",
              "295   63                     140          187                      144   \n",
              "296   63                     124          197                      136   \n",
              "297   59                     164          176                       90   \n",
              "298   57                     140          241                      123   \n",
              "299   45                     110          264                      132   \n",
              "300   68                     144          193                      141   \n",
              "301   57                     130          131                      115   \n",
              "302   57                     130          236                      174   \n",
              "\n",
              "     st_depression  num_major_vessels  \n",
              "0              2.3                  0  \n",
              "1              3.5                  0  \n",
              "2              1.4                  0  \n",
              "3              0.8                  0  \n",
              "4              0.6                  0  \n",
              "5              0.4                  0  \n",
              "6              1.3                  0  \n",
              "7              0.0                  0  \n",
              "8              0.5                  0  \n",
              "9              1.6                  0  \n",
              "10             1.2                  0  \n",
              "11             0.2                  0  \n",
              "12             0.6                  0  \n",
              "13             1.8                  0  \n",
              "14             1.0                  0  \n",
              "15             1.6                  0  \n",
              "16             0.0                  0  \n",
              "17             2.6                  0  \n",
              "18             1.5                  0  \n",
              "19             1.8                  2  \n",
              "20             0.5                  0  \n",
              "21             0.4                  0  \n",
              "22             0.0                  0  \n",
              "23             1.0                  0  \n",
              "24             1.4                  0  \n",
              "25             0.4                  2  \n",
              "26             1.6                  0  \n",
              "27             0.6                  0  \n",
              "28             0.8                  1  \n",
              "29             1.2                  0  \n",
              "..             ...                ...  \n",
              "273            0.1                  1  \n",
              "274            1.0                  1  \n",
              "275            1.0                  2  \n",
              "276            2.0                  1  \n",
              "277            0.3                  0  \n",
              "278            0.0                  2  \n",
              "279            3.6                  1  \n",
              "280            1.8                  0  \n",
              "281            1.0                  0  \n",
              "282            2.2                  1  \n",
              "283            0.0                  0  \n",
              "284            1.9                  1  \n",
              "285            1.8                  2  \n",
              "286            0.8                  2  \n",
              "287            0.0                  1  \n",
              "288            3.0                  1  \n",
              "289            2.0                  1  \n",
              "290            0.0                  1  \n",
              "291            4.4                  3  \n",
              "292            2.8                  2  \n",
              "293            0.8                  0  \n",
              "294            2.8                  0  \n",
              "295            4.0                  2  \n",
              "296            0.0                  0  \n",
              "297            1.0                  2  \n",
              "298            0.2                  0  \n",
              "299            1.2                  0  \n",
              "300            3.4                  2  \n",
              "301            1.2                  1  \n",
              "302            0.0                  1  \n",
              "\n",
              "[303 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "metadata": {
        "id": "xEl1Ed6tFyki",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Train Test Split"
      ]
    },
    {
      "metadata": {
        "id": "80H60CYCF2RA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "predictors = data.drop(\"target\",axis=1)\n",
        "target = data[\"target\"]\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(predictors,target,test_size=0.20,random_state=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HJYnBPGLL19K",
        "colab_type": "code",
        "outputId": "d0f1d8fd-2b33-45ed-94c6-95031ea0db11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(242, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 212
        }
      ]
    },
    {
      "metadata": {
        "id": "IRmPSvtyL6US",
        "colab_type": "code",
        "outputId": "6fe5a867-53fc-4929-ef52-54aca334e7ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 213
        }
      ]
    },
    {
      "metadata": {
        "id": "uwFlqgX8L9-3",
        "colab_type": "code",
        "outputId": "6aefb8d2-66cc-48b5-ab30-aa0707da2be6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "Y_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(242,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 214
        }
      ]
    },
    {
      "metadata": {
        "id": "BjNlo21NMBg3",
        "colab_type": "code",
        "outputId": "d58ce03c-0e5c-46e3-82df-68475eb6499d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "Y_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 215
        }
      ]
    },
    {
      "metadata": {
        "id": "4yWSip8BMEcL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Model Fitting"
      ]
    },
    {
      "metadata": {
        "id": "sxLYI6KfMqaK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Naive Bayes\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "ltcjatbiOtsj",
        "colab_type": "code",
        "outputId": "52580453-23b2-4697-a209-5f6cef8d0289",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1227
        }
      },
      "cell_type": "code",
      "source": [
        "nb = GaussianNB()\n",
        "\n",
        "Y_train=Y_train.astype('int')\n",
        "\n",
        "nb.fit(X_train,Y_train)\n",
        "\n",
        "\n",
        "Y_pred_nb = nb.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-243-6970599ecb33>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGaussianNB\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mY_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mY_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mnb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_deprecate_kwarg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, **kwargs)\u001b[0m\n\u001b[1;32m   4002\u001b[0m         \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4003\u001b[0m         new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors,\n\u001b[0;32m-> 4004\u001b[0;31m                                      **kwargs)\n\u001b[0m\u001b[1;32m   4005\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[1;32m   3460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3461\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3462\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'astype'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3463\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3464\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[1;32m   3327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3328\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mgr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3329\u001b[0;31m             \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3330\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors, values, **kwargs)\u001b[0m\n\u001b[1;32m    542\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'raise'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m         return self._astype(dtype, copy=copy, errors=errors, values=values,\n\u001b[0;32m--> 544\u001b[0;31m                             **kwargs)\n\u001b[0m\u001b[1;32m    545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    546\u001b[0m     def _astype(self, dtype, copy=False, errors='raise', values=None,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36m_astype\u001b[0;34m(self, dtype, copy, errors, values, klass, mgr, **kwargs)\u001b[0m\n\u001b[1;32m    623\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m                 \u001b[0;31m# _astype_nansafe works fine with 1-d only\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy)\u001b[0m\n\u001b[1;32m    690\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    691\u001b[0m         \u001b[0;31m# work around NumPy brokenness, #1987\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 692\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype_intsafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"datetime64\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"timedelta64\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.astype_intsafe\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/src/util.pxd\u001b[0m in \u001b[0;36mutil.set_value_at_unsafe\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'Yes'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "1RKwk7tYPwM7",
        "colab_type": "code",
        "outputId": "bb8402af-b61f-4feb-f4b0-c1b1064c3d3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "Y_pred_nb.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 244
        }
      ]
    },
    {
      "metadata": {
        "id": "VWrQek6RRxEs",
        "colab_type": "code",
        "outputId": "802b3e6c-070f-4861-b8fa-32ecef462404",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "cell_type": "code",
      "source": [
        "# build confusion metrics\n",
        "CM=pd.crosstab(Y_test,Y_pred_nb)\n",
        "CM"
      ],
      "execution_count": 0,
      "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>col_0</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>No</th>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>7</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "col_0    0   1\n",
              "target        \n",
              "No      26   1\n",
              "Yes      7  27"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 245
        }
      ]
    },
    {
      "metadata": {
        "id": "PwXpFaQ7R8OM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#let us save TP, TN, FP, FN\n",
        "TN=CM.iloc[0,0]\n",
        "FP=CM.iloc[0,1]\n",
        "FN=CM.iloc[1,0]\n",
        "TP=CM.iloc[1,1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fX1b4LIsR_K2",
        "colab_type": "code",
        "outputId": "a37918f8-27ce-4582-efdb-be0fd7342b56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "#check accuracy of model\n",
        "score_nb=((TP+TN)*100)/(TP+TN+FP+FN)\n",
        "score_nb"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "86.88524590163935"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 247
        }
      ]
    },
    {
      "metadata": {
        "id": "13QCsi7-SH6k",
        "colab_type": "code",
        "outputId": "953ba6d0-c14b-4db6-857c-f301700183a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# check false negative rate of the model\n",
        "fnr=FN*100/(FN+TP)\n",
        "fnr"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20.58823529411765"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "metadata": {
        "id": "R87IntJocc0y",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "_kyw77jHTJM8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Decision Tree"
      ]
    },
    {
      "metadata": {
        "id": "AhNs4gDoTOkP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# replace target variable  with yes or no\n",
        "#data['target'] = data['target'].replace(0, 'No')\n",
        "#data['target'] = data['target'].replace(1, 'Yes')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AjrFw_DEhdQQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# to handle data imbalance issue we are dividing our dataset on basis of stratified sampling\n",
        "# divide data into train and test\n",
        "#X=data.values[:,0:13]\n",
        "#Y=data.values[:,13]\n",
        "#X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "J_EUk52whhUy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Decision tree - we will build the model on train data and test it on test data\n",
        "C50_model = tree.DecisionTreeClassifier(criterion='entropy').fit(X_train, Y_train)\n",
        "# predict new test cases\n",
        "C50_Predictions = C50_model.predict(X_test) # applying decision tree model on test data set"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8nqte4W3hsXW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Create dot file to visualise tree  #http://webgraphviz.com/\n",
        "dotfile = open(\"pt.dot\", 'w')\n",
        "df = tree.export_graphviz(C50_model, out_file=dotfile,feature_names=data1.columns)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pFK07Ipzhwag",
        "colab_type": "code",
        "outputId": "388f6b8d-9c9d-48ca-d87f-1156fabbb072",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "cell_type": "code",
      "source": [
        "# Confusion matrix of decision tree\n",
        "CM = pd.crosstab(Y_test, C50_Predictions)\n",
        "CM"
      ],
      "execution_count": 0,
      "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>col_0</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>No</th>\n",
              "      <td>25</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>7</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "col_0   No  Yes\n",
              "target         \n",
              "No      25    2\n",
              "Yes      7   27"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 218
        }
      ]
    },
    {
      "metadata": {
        "id": "Jj3rKPYDh1kQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#let us save TP, TN, FP, FN\n",
        "TN=CM.iloc[0,0]\n",
        "FP=CM.iloc[0,1]\n",
        "FN=CM.iloc[1,0]\n",
        "TP=CM.iloc[1,1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cjpFPXi_h3A4",
        "colab_type": "code",
        "outputId": "ae416ea8-bc8f-49cc-c652-773d9758d19b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "#check accuracy of model\n",
        "score_dt=((TP+TN)*100)/(TP+TN+FP+FN)\n",
        "score_dt"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "85.24590163934427"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 241
        }
      ]
    },
    {
      "metadata": {
        "id": "3G2KhFZNja6p",
        "colab_type": "code",
        "outputId": "f230b241-7d27-4416-8bf2-f79a895ea289",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# check false negative rate of the model\n",
        "fnr=FN*100/(FN+TP)\n",
        "fnr"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20.58823529411765"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 221
        }
      ]
    },
    {
      "metadata": {
        "id": "TbMOnZM5lGA7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# KNN(K Nearest Neighbors)"
      ]
    },
    {
      "metadata": {
        "id": "xUXNBpW9l13H",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "for neighbors = 7"
      ]
    },
    {
      "metadata": {
        "id": "zYfXxKcFlek0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=7)\n",
        "knn.fit(X_train,Y_train)\n",
        "Y_pred_knn=knn.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yWmkjOLklgaH",
        "colab_type": "code",
        "outputId": "dd2490a3-cc52-466c-8787-f348ebf3c1e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "Y_pred_knn.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 238
        }
      ]
    },
    {
      "metadata": {
        "id": "24rNZxPnlkRa",
        "colab_type": "code",
        "outputId": "77c88fef-eef9-4b68-8b24-f22b34b22a26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "score_knn_7 = round(accuracy_score(Y_pred_knn,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using KNN is: \"+str(score_knn)+\" %\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy score achieved using KNN is: 67.21 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Lx1QR5Edl5An",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "for neighbors = 4"
      ]
    },
    {
      "metadata": {
        "id": "yHi3ekuml5ih",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "knn_model=KNeighborsClassifier(n_neighbors=4).fit(X_train,Y_train)\n",
        "knn_predictions=knn_model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fjOB-hsHmAMM",
        "colab_type": "code",
        "outputId": "bb46c0a7-eb9a-4120-fcaa-32813035ef3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "cell_type": "code",
      "source": [
        "# build confusion metrics\n",
        "CM=pd.crosstab(y_test,knn_predictions)\n",
        "CM"
      ],
      "execution_count": 0,
      "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>col_0</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>row_0</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>No</th>\n",
              "      <td>14</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "col_0  No  Yes\n",
              "row_0         \n",
              "No     14   13\n",
              "Yes    19   15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        }
      ]
    },
    {
      "metadata": {
        "id": "oPCet3cAmDov",
        "colab_type": "code",
        "outputId": "655413a6-838f-4082-dca0-d8fa29cce686",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "# try K=1 through K=25 and record testing accuracy\n",
        "k_range = range(1, 26)\n",
        "\n",
        "# We can create Python dictionary using [] or dict()\n",
        "scores = []\n",
        "from sklearn import metrics\n",
        "# We use a loop through the range 1 to 26\n",
        "# We append the scores in the dictionary\n",
        "for k in k_range:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    knn.fit(X_train, y_train)\n",
        "    y_pred = knn.predict(X_test)\n",
        "    scores.append(metrics.accuracy_score(y_test, y_pred))\n",
        "\n",
        "print(scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.5409836065573771, 0.5409836065573771, 0.4918032786885246, 0.45901639344262296, 0.5573770491803278, 0.5409836065573771, 0.5245901639344263, 0.5409836065573771, 0.4918032786885246, 0.5245901639344263, 0.5737704918032787, 0.5081967213114754, 0.5737704918032787, 0.5409836065573771, 0.5081967213114754, 0.5573770491803278, 0.5409836065573771, 0.5737704918032787, 0.5737704918032787, 0.5737704918032787, 0.5245901639344263, 0.5737704918032787, 0.5573770491803278, 0.5573770491803278, 0.5573770491803278]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4LtPqb6omGzp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#let us save TP, TN, FP, FN\n",
        "TN=CM.iloc[0,0]\n",
        "FP=CM.iloc[0,1]\n",
        "FN=CM.iloc[1,0]\n",
        "TP=CM.iloc[1,1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9P7x44MBmHit",
        "colab_type": "code",
        "outputId": "705c3dbf-abcf-4da0-de38-b476a5362af1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "#check accuracy of model\n",
        "score_knn_4=((TP+TN)*100)/(TP+TN+FP+FN)\n",
        "score_knn_4"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "85.24590163934427"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 240
        }
      ]
    },
    {
      "metadata": {
        "id": "89icEEbPmNFB",
        "colab_type": "code",
        "outputId": "24f2a6b2-8254-47cf-b99f-b40d9af04ff6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# check false negative rate of the model\n",
        "fnr=FN*100/(FN+TP)\n",
        "fnr"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20.58823529411765"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 230
        }
      ]
    },
    {
      "metadata": {
        "id": "2ic5cludmfaT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Logistic Regression"
      ]
    },
    {
      "metadata": {
        "id": "3ZhmUUZbmgCS",
        "colab_type": "code",
        "outputId": "f25626e1-33b5-4330-814d-57c23bc4c7b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr = LogisticRegression()\n",
        "\n",
        "lr.fit(X_train,Y_train)\n",
        "\n",
        "Y_pred_lr = lr.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "yJaF4wAjmyTY",
        "colab_type": "code",
        "outputId": "52680500-a3d2-454a-ae74-ea9fea4aa1a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "Y_pred_lr.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 234
        }
      ]
    },
    {
      "metadata": {
        "id": "qcCzQpivm0gJ",
        "colab_type": "code",
        "outputId": "a3d7f533-cd1f-40c7-8915-402a73c99408",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "score_lr = round(accuracy_score(Y_pred_lr,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Logistic Regression is: \"+str(score_lr)+\" %\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy score achieved using Logistic Regression is: 86.89 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "81_qGMDDnAGe",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Final Score"
      ]
    },
    {
      "metadata": {
        "id": "ewbSSbg_nAuC",
        "colab_type": "code",
        "outputId": "873dfa64-e9e7-4bf3-c9cc-8ca9b34959c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "cell_type": "code",
      "source": [
        "scores = [score_lr,score_nb,score_knn,score_dt]\n",
        "algorithms = [\"Logistic Regression\",\"Naive Bayes\",\"K-Nearest Neighbors\",\"Decision Tree\"]    \n",
        "\n",
        "for i in range(len(algorithms)):\n",
        "    print(\"The accuracy score achieved using \"+algorithms[i]+\" is: \"+str(scores[i])+\" %\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy score achieved using Logistic Regression is: 86.89 %\n",
            "The accuracy score achieved using Naive Bayes is: 86.88524590163935 %\n",
            "The accuracy score achieved using K-Nearest Neighbors is: 67.21 %\n",
            "The accuracy score achieved using Decision Tree is: 85.24590163934427 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vNiCmN2koZhg",
        "colab_type": "code",
        "outputId": "9277e4e8-80f7-4658-fc61-b36d90c493e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        }
      },
      "cell_type": "code",
      "source": [
        "sns.set(rc={'figure.figsize':(15,8)})\n",
        "plt.xlabel(\"Algorithms\")\n",
        "plt.ylabel(\"Accuracy score\")\n",
        "\n",
        "sns.barplot(algorithms,scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f68358d9f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 249
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BAAAKpE4ReM0112TOnDkNPQsAAAANrE4R+Nhjj2WXXXbJEUcckTvvvDOVlZUNPRcAAAAN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\nEAAAoEBEIAAAQIGIQAAAgAL5/wCi87UwlMkPXQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "RDnaJLFLonLV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# We observe that, we can achieve the best accuracy of 86.89% using Logistic Regression"
      ]
    }
  ]
}