hd2.ipynb

{
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  "metadata": {
    "colab": {
      "name": "hd_with_bad_dataset.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "W8iTXS6XLTuR",
        "colab_type": "code",
        "outputId": "17992111-d48a-48c1-b81e-dff4e5b6eaf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "import os\n",
        "print(os.listdir())\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['.config', 'processed_heart_data.csv', 'sample_data']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0zkWHuLILfg1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"processed_heart_data.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UnTLkWsfLrJ-",
        "colab_type": "code",
        "outputId": "3a2f5b78-93e4-40b5-a8f0-f410fdd9a7c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "type(data)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "-BsKZUGGLudq",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(297, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "1XomwEoZcd7h",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>age</th>\n",
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              "      <th>chol</th>\n",
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              "      <th>restecg</th>\n",
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              "      <th>oldpeak</th>\n",
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              "      <th>thal</th>\n",
              "      <th>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
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              "      <td>1</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
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              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
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              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>160</td>\n",
              "      <td>286</td>\n",
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              "      <td>2</td>\n",
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              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>67</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>120</td>\n",
              "      <td>229</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37</td>\n",
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              "      <td>3</td>\n",
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              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</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   1       145   233    1        2      150      0      2.3      3   \n",
              "1   67    1   4       160   286    0        2      108      1      1.5      2   \n",
              "2   67    1   4       120   229    0        2      129      1      2.6      2   \n",
              "3   37    1   3       130   250    0        0      187      0      3.5      3   \n",
              "4   41    0   2       130   204    0        2      172      0      1.4      1   \n",
              "\n",
              "   ca  thal  num  \n",
              "0   0     6    0  \n",
              "1   3     3    2  \n",
              "2   2     7    1  \n",
              "3   0     3    0  \n",
              "4   0     3    0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "nGuJY-jlchCZ",
        "colab_type": "code",
        "outputId": "7264276a-abbe-4aef-cede-6638ce9b5258",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "cell_type": "code",
      "source": [
        "data.sample(5)"
      ],
      "execution_count": 6,
      "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",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>43</td>\n",
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              "      <td>3</td>\n",
              "      <td>122</td>\n",
              "      <td>213</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>128</td>\n",
              "      <td>205</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>135</td>\n",
              "      <td>234</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>161</td>\n",
              "      <td>0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>71</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>110</td>\n",
              "      <td>265</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "133   43    0   3       122   213    0        0      165      0      0.2   \n",
              "94    52    1   4       128   255    0        0      161      1      0.0   \n",
              "141   52    1   2       128   205    1        0      184      0      0.0   \n",
              "33    59    1   4       135   234    0        0      161      0      0.5   \n",
              "102   71    0   3       110   265    1        2      130      0      0.0   \n",
              "\n",
              "     slope  ca  thal  num  \n",
              "133      2   0     3    0  \n",
              "94       1   1     7    1  \n",
              "141      1   0     3    0  \n",
              "33       2   0     7    0  \n",
              "102      1   1     3    0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "3uk-b1pycmly",
        "colab_type": "code",
        "outputId": "f2e64344-1f56-4edb-ad5e-a66d05edaca7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "execution_count": 7,
      "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",
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              "      <th>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "      <td>297.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.542088</td>\n",
              "      <td>0.676768</td>\n",
              "      <td>3.158249</td>\n",
              "      <td>131.693603</td>\n",
              "      <td>247.350168</td>\n",
              "      <td>0.144781</td>\n",
              "      <td>0.996633</td>\n",
              "      <td>149.599327</td>\n",
              "      <td>0.326599</td>\n",
              "      <td>1.055556</td>\n",
              "      <td>1.602694</td>\n",
              "      <td>0.676768</td>\n",
              "      <td>4.730640</td>\n",
              "      <td>0.946128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.049736</td>\n",
              "      <td>0.468500</td>\n",
              "      <td>0.964859</td>\n",
              "      <td>17.762806</td>\n",
              "      <td>51.997583</td>\n",
              "      <td>0.352474</td>\n",
              "      <td>0.994914</td>\n",
              "      <td>22.941562</td>\n",
              "      <td>0.469761</td>\n",
              "      <td>1.166123</td>\n",
              "      <td>0.618187</td>\n",
              "      <td>0.938965</td>\n",
              "      <td>1.938629</td>\n",
              "      <td>1.234551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</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>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>56.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>243.000000</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>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>276.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</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>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</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>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp    trestbps        chol         fbs  \\\n",
              "count  297.000000  297.000000  297.000000  297.000000  297.000000  297.000000   \n",
              "mean    54.542088    0.676768    3.158249  131.693603  247.350168    0.144781   \n",
              "std      9.049736    0.468500    0.964859   17.762806   51.997583    0.352474   \n",
              "min     29.000000    0.000000    1.000000   94.000000  126.000000    0.000000   \n",
              "25%     48.000000    0.000000    3.000000  120.000000  211.000000    0.000000   \n",
              "50%     56.000000    1.000000    3.000000  130.000000  243.000000    0.000000   \n",
              "75%     61.000000    1.000000    4.000000  140.000000  276.000000    0.000000   \n",
              "max     77.000000    1.000000    4.000000  200.000000  564.000000    1.000000   \n",
              "\n",
              "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
              "count  297.000000  297.000000  297.000000  297.000000  297.000000  297.000000   \n",
              "mean     0.996633  149.599327    0.326599    1.055556    1.602694    0.676768   \n",
              "std      0.994914   22.941562    0.469761    1.166123    0.618187    0.938965   \n",
              "min      0.000000   71.000000    0.000000    0.000000    1.000000    0.000000   \n",
              "25%      0.000000  133.000000    0.000000    0.000000    1.000000    0.000000   \n",
              "50%      1.000000  153.000000    0.000000    0.800000    2.000000    0.000000   \n",
              "75%      2.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
              "max      2.000000  202.000000    1.000000    6.200000    3.000000    3.000000   \n",
              "\n",
              "             thal         num  \n",
              "count  297.000000  297.000000  \n",
              "mean     4.730640    0.946128  \n",
              "std      1.938629    1.234551  \n",
              "min      3.000000    0.000000  \n",
              "25%      3.000000    0.000000  \n",
              "50%      3.000000    0.000000  \n",
              "75%      7.000000    2.000000  \n",
              "max      7.000000    4.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "edurcSVbcq-M",
        "colab_type": "code",
        "outputId": "4deab1cc-37be-4a37-c84e-6d33d7fd0556",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 297 entries, 0 to 296\n",
            "Data columns (total 14 columns):\n",
            "age         297 non-null int64\n",
            "sex         297 non-null int64\n",
            "cp          297 non-null int64\n",
            "trestbps    297 non-null int64\n",
            "chol        297 non-null int64\n",
            "fbs         297 non-null int64\n",
            "restecg     297 non-null int64\n",
            "thalach     297 non-null int64\n",
            "exang       297 non-null int64\n",
            "oldpeak     297 non-null float64\n",
            "slope       297 non-null int64\n",
            "ca          297 non-null int64\n",
            "thal        297 non-null int64\n",
            "num         297 non-null int64\n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 32.6 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Fuz6Hb6FcwSb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "###we have no missing values"
      ]
    },
    {
      "metadata": {
        "id": "xWPS2GN_MWSf",
        "colab_type": "code",
        "outputId": "bbc13f03-90c5-45d5-9b56-5090229a8ee2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "data[\"sex\"].unique()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "9r4vNghMatAa",
        "colab_type": "code",
        "outputId": "6868c72d-0790-4abf-c5db-e630e0c51aee",
        "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": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Percentage of Female Patients:32.32%\n",
            "Percentage of Male Patients:67.68%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "txsfOS4abYGc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "run this line after prediction"
      ]
    },
    {
      "metadata": {
        "id": "u9c5cXJnbXMc",
        "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": "8cP_g8Qka848",
        "colab_type": "code",
        "outputId": "34a22042-f52f-4156-98c8-0e74ca0141cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        }
      },
      "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": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Nk9wxxDMBAAAA0DCkAanW+qrlX5dS3pXkN0l2SPLKJP/e/f1bQzkT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\nkiSp6f8BQOc46oVSpLMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "eOa5rLT2Mh99",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# TRAIN TEST SPLIT"
      ]
    },
    {
      "metadata": {
        "id": "V7iKKDPiMk0N",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "predictors = data.drop(\"target\",axis=1)\n",
        "target = data[\"target\"]\n",
        "\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": "7YwomHgBMtZt",
        "colab_type": "code",
        "outputId": "ceffdc1d-5fa4-434b-e09a-540ed0586cdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(237, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "cR7VVOQUMyck",
        "colab_type": "code",
        "outputId": "63807bb9-a7f8-4938-a76a-f2225d02face",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "jkMdNviWM1Mx",
        "colab_type": "code",
        "outputId": "6fb54537-c62d-41d8-a801-4700fc481546",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "Y_train.shape"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(237,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "w4AmfZuKM6ZL",
        "colab_type": "code",
        "outputId": "469e8f9f-ceaa-40e2-8060-3a7b8fcb3735",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "Y_test.shape"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "i7W7vtpQM-c-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## importing Accuracy score"
      ]
    },
    {
      "metadata": {
        "id": "KosuqmCiNBmI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KsVRQL0zNEJg",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Logistic regression"
      ]
    },
    {
      "metadata": {
        "id": "6vsnDoxqNInB",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "metadata": {
        "id": "WOncqcRVNM0Q",
        "colab_type": "code",
        "outputId": "f3659ca2-c83b-4c56-f246-c5b265c7a00f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "Y_pred_lr.shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "sJFyMzLTNPET",
        "colab_type": "code",
        "outputId": "45934470-f679-47b1-fe8b-73300234b2d4",
        "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": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy score achieved using Logistic Regression is: 56.67 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SBnYvM4BNS95",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Random Forest"
      ]
    },
    {
      "metadata": {
        "id": "Ro3iaSbiNVJe",
        "colab_type": "code",
        "outputId": "27a7e038-a2ba-41fb-ab43-2f58cd1f4e70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "max_accuracy = 0\n",
        "\n",
        "\n",
        "for x in range(2000):\n",
        "    rf = RandomForestClassifier(random_state=x)\n",
        "    rf.fit(X_train,Y_train)\n",
        "    Y_pred_rf = rf.predict(X_test)\n",
        "    current_accuracy = round(accuracy_score(Y_pred_rf,Y_test)*100,2)\n",
        "    if(current_accuracy>max_accuracy):\n",
        "        max_accuracy = current_accuracy\n",
        "        best_x = x\n",
        "        \n",
        "print(max_accuracy)\n",
        "print(best_x)\n",
        "\n",
        "rf = RandomForestClassifier(random_state=best_x)\n",
        "rf.fit(X_train,Y_train)\n",
        "Y_pred_rf = rf.predict(X_test)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "63.33\n",
            "1101\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "AgF33Bo0Ncn3",
        "colab_type": "code",
        "outputId": "ce9c48c8-ee34-416a-a5f1-f5b5e129cc10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "Y_pred_rf.shape"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "iRVqY3o0NeCB",
        "colab_type": "code",
        "outputId": "39f36491-878f-4a3a-8a39-de3ca8917009",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "score_rf = round(accuracy_score(Y_pred_rf,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Decision Tree is: \"+str(score_rf)+\" %\")"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy score achieved using Decision Tree is: 63.33 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "itvCy6g1OIKT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Naive Bayes"
      ]
    },
    {
      "metadata": {
        "id": "niuIRgKIOKia",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "nb = GaussianNB()\n",
        "\n",
        "nb.fit(X_train,Y_train)\n",
        "\n",
        "Y_pred_nb = nb.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zd5qmJiKOXfB",
        "colab_type": "code",
        "outputId": "53008184-0c1f-4438-b8cc-67f4bd7d021c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "Y_pred_nb.shape"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "id": "edGDQiM8OYzW",
        "colab_type": "code",
        "outputId": "cd2b8d4d-7970-4762-9098-f205ac8e1d2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "score_nb = round(accuracy_score(Y_pred_nb,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Naive Bayes is: \"+str(score_nb)+\" %\")"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy score achieved using Naive Bayes is: 51.67 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ovHec9BDOeWS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# KNN(K Nearest Neighbors)"
      ]
    },
    {
      "metadata": {
        "id": "cgD5RQVfOf02",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "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": "0mNYgeVhOl0A",
        "colab_type": "code",
        "outputId": "9691e8d3-8119-4e34-cf7d-844a6eeff2a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "Y_pred_knn.shape"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "WN7EX1UbOouC",
        "colab_type": "code",
        "outputId": "a315f4f7-7a23-4b14-8e7a-5dac15e568ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "score_knn = 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": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy score achieved using KNN is: 51.67 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Q2uvggitO1iZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## for neighbors = 4"
      ]
    },
    {
      "metadata": {
        "id": "-dZT5oiqOsBx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=4)\n",
        "knn.fit(X_train,Y_train)\n",
        "Y_pred_knn=knn.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9W4nPHtwOvjH",
        "colab_type": "code",
        "outputId": "293d78d8-537a-4692-9adf-e8f279d97efe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "Y_pred_knn.shape"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "id": "BicdIDjvOxhH",
        "colab_type": "code",
        "outputId": "ce69b692-7ee6-424b-baab-8ccd75c82973",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "score_knn_4 = round(accuracy_score(Y_pred_knn,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using KNN is: \"+str(score_knn_4)+\" %\")"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy score achieved using KNN is: 41.67 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "KyhawFIRPUch",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Decision Tree"
      ]
    },
    {
      "metadata": {
        "id": "yM2faYCKPYlP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "max_accuracy = 0\n",
        "\n",
        "\n",
        "for x in range(200):\n",
        "    dt = DecisionTreeClassifier(random_state=x)\n",
        "    dt.fit(X_train,Y_train)\n",
        "    Y_pred_dt = dt.predict(X_test)\n",
        "    current_accuracy = round(accuracy_score(Y_pred_dt,Y_test)*100,2)\n",
        "    if(current_accuracy>max_accuracy):\n",
        "        max_accuracy = current_accuracy\n",
        "        best_x = x\n",
        "        \n",
        "#print(max_accuracy)\n",
        "#print(best_x)\n",
        "\n",
        "\n",
        "dt = DecisionTreeClassifier(random_state=best_x)\n",
        "dt.fit(X_train,Y_train)\n",
        "Y_pred_dt = dt.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jgEqnhioPbN7",
        "colab_type": "code",
        "outputId": "fc6b9426-c7f0-4ff6-b94b-04807236bbeb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "print(Y_pred_dt.shape)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3V4aPp_LPdnj",
        "colab_type": "code",
        "outputId": "b37e7a28-4041-4b8b-b757-d4ff2df33552",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "score_dt = round(accuracy_score(Y_pred_dt,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Decision Tree is: \"+str(score_dt)+\" %\")"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy score achieved using Decision Tree is: 56.67 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BeTYQ0FZPh0I",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# FINAL SCORE"
      ]
    },
    {
      "metadata": {
        "id": "akYXJZ5MPj8z",
        "colab_type": "code",
        "outputId": "582997c4-50b4-4502-a39f-43e0bb6bc765",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "cell_type": "code",
      "source": [
        "scores = [score_lr,score_nb,score_knn,score_dt,score_rf]\n",
        "algorithms = [\"Logistic Regression\",\"Naive Bayes\",\"K-Nearest Neighbors\",\"Decision Tree\",\"Random Forest\"]    \n",
        "\n",
        "for i in range(len(algorithms)):\n",
        "    print(\"The accuracy score achieved using \"+algorithms[i]+\" is: \"+str(scores[i])+\" %\")"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy score achieved using Logistic Regression is: 56.67 %\n",
            "The accuracy score achieved using Naive Bayes is: 51.67 %\n",
            "The accuracy score achieved using K-Nearest Neighbors is: 51.67 %\n",
            "The accuracy score achieved using Decision Tree is: 56.67 %\n",
            "The accuracy score achieved using Random Forest is: 63.33 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fpf1tEKCP1Hp",
        "colab_type": "code",
        "outputId": "7e197956-2631-4fef-8277-de1adfb8900a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        }
      },
      "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": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b2d8caeb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WWzjVEgAAYAWp8TTLBx98MO3atasKuTvuuCNffPFFkmTIkCGlnQ4AAIClqjHmTjnllHz0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\nAAAU0P8HlzsyP+4RJcYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}