Approach 4 - Vaishnavi Pingat.ipynb

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      "name": "HeartFailurePrediction_MLSC.ipynb",
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import math\n",
        "from matplotlib.figure import Figure\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.pipeline import make_pipeline\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/heartMLSC/heart.csv\")\n",
        "df.shape"
      ],
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        "colab": {
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        "id": "Mhu_8XsWFa0p",
        "outputId": "c6bee544-1540-41a1-8c7e-56e1d62e561d"
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        {
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            "text/plain": [
              "(918, 12)"
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          "execution_count": 79
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    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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        "id": "v_SaVoRpFhgD",
        "outputId": "272d82b1-ef9b-4019-b727-2295fa2fd45e"
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      "execution_count": null,
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>ChestPainType</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>RestingECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExerciseAngina</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>ST_Slope</th>\n",
              "      <th>HeartDisease</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
              "      <td>140</td>\n",
              "      <td>289</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
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              "      <td>Up</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>F</td>\n",
              "      <td>NAP</td>\n",
              "      <td>160</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>156</td>\n",
              "      <td>N</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>ST</td>\n",
              "      <td>98</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>F</td>\n",
              "      <td>ASY</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>108</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>M</td>\n",
              "      <td>NAP</td>\n",
              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>122</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            "text/plain": [
              "   Age Sex ChestPainType  ...  Oldpeak  ST_Slope  HeartDisease\n",
              "0   40   M           ATA  ...      0.0        Up             0\n",
              "1   49   F           NAP  ...      1.0      Flat             1\n",
              "2   37   M           ATA  ...      0.0        Up             0\n",
              "3   48   F           ASY  ...      1.5      Flat             1\n",
              "4   54   M           NAP  ...      0.0        Up             0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g42jUp2GFnNz",
        "outputId": "294eedaa-09bf-41f8-a3d7-36da552a7df9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Age', 'Sex', 'ChestPainType', 'RestingBP', 'Cholesterol', 'FastingBS',\n",
              "       'RestingECG', 'MaxHR', 'ExerciseAngina', 'Oldpeak', 'ST_Slope',\n",
              "       'HeartDisease'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0aoxD4y3Fu8f",
        "outputId": "b695a554-6b89-4e5d-cb76-f27382172933"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 918 entries, 0 to 917\n",
            "Data columns (total 12 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Age             918 non-null    int64  \n",
            " 1   Sex             918 non-null    object \n",
            " 2   ChestPainType   918 non-null    object \n",
            " 3   RestingBP       918 non-null    int64  \n",
            " 4   Cholesterol     918 non-null    int64  \n",
            " 5   FastingBS       918 non-null    int64  \n",
            " 6   RestingECG      918 non-null    object \n",
            " 7   MaxHR           918 non-null    int64  \n",
            " 8   ExerciseAngina  918 non-null    object \n",
            " 9   Oldpeak         918 non-null    float64\n",
            " 10  ST_Slope        918 non-null    object \n",
            " 11  HeartDisease    918 non-null    int64  \n",
            "dtypes: float64(1), int64(6), object(5)\n",
            "memory usage: 86.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are total 11 columns out of which 5 have object datatype, one is of float datatype and the rest are integer datatype."
      ],
      "metadata": {
        "id": "C9mYnnP8GCEK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# no null values \n",
        "\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ftCIck1GF3AU",
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age               0\n",
              "Sex               0\n",
              "ChestPainType     0\n",
              "RestingBP         0\n",
              "Cholesterol       0\n",
              "FastingBS         0\n",
              "RestingECG        0\n",
              "MaxHR             0\n",
              "ExerciseAngina    0\n",
              "Oldpeak           0\n",
              "ST_Slope          0\n",
              "HeartDisease      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "M1mX4q_w0pLP",
        "outputId": "1a09cec8-a9d8-40f8-f156-a24d2a314d09"
      },
      "execution_count": null,
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          "data": {
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>HeartDisease</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>53.510893</td>\n",
              "      <td>132.396514</td>\n",
              "      <td>198.799564</td>\n",
              "      <td>0.233115</td>\n",
              "      <td>136.809368</td>\n",
              "      <td>0.887364</td>\n",
              "      <td>0.553377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.432617</td>\n",
              "      <td>18.514154</td>\n",
              "      <td>109.384145</td>\n",
              "      <td>0.423046</td>\n",
              "      <td>25.460334</td>\n",
              "      <td>1.066570</td>\n",
              "      <td>0.497414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>-2.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>173.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>267.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>1.000000</td>\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d1beb73d-7483-4a82-99d1-587ebe70d14f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              Age   RestingBP  ...     Oldpeak  HeartDisease\n",
              "count  918.000000  918.000000  ...  918.000000    918.000000\n",
              "mean    53.510893  132.396514  ...    0.887364      0.553377\n",
              "std      9.432617   18.514154  ...    1.066570      0.497414\n",
              "min     28.000000    0.000000  ...   -2.600000      0.000000\n",
              "25%     47.000000  120.000000  ...    0.000000      0.000000\n",
              "50%     54.000000  130.000000  ...    0.600000      1.000000\n",
              "75%     60.000000  140.000000  ...    1.500000      1.000000\n",
              "max     77.000000  200.000000  ...    6.200000      1.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "djxTF3N20S_L",
        "outputId": "141b009b-c1b3-4948-9bce-6745e288f565"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Age</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
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              "      <td>-0.095282</td>\n",
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              "      <td>0.164803</td>\n",
              "      <td>0.107589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cholesterol</th>\n",
              "      <td>-0.095282</td>\n",
              "      <td>0.100893</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>FastingBS</th>\n",
              "      <td>0.198039</td>\n",
              "      <td>0.070193</td>\n",
              "      <td>-0.260974</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.131438</td>\n",
              "      <td>0.052698</td>\n",
              "      <td>0.267291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MaxHR</th>\n",
              "      <td>-0.382045</td>\n",
              "      <td>-0.112135</td>\n",
              "      <td>0.235792</td>\n",
              "      <td>-0.131438</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.160691</td>\n",
              "      <td>-0.400421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oldpeak</th>\n",
              "      <td>0.258612</td>\n",
              "      <td>0.164803</td>\n",
              "      <td>0.050148</td>\n",
              "      <td>0.052698</td>\n",
              "      <td>-0.160691</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.403951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HeartDisease</th>\n",
              "      <td>0.282039</td>\n",
              "      <td>0.107589</td>\n",
              "      <td>-0.232741</td>\n",
              "      <td>0.267291</td>\n",
              "      <td>-0.400421</td>\n",
              "      <td>0.403951</td>\n",
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              "          const element = document.querySelector('#df-c997d086-15d4-4c5e-b1f4-062a64c21816');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "                   Age  RestingBP  ...   Oldpeak  HeartDisease\n",
              "Age           1.000000   0.254399  ...  0.258612      0.282039\n",
              "RestingBP     0.254399   1.000000  ...  0.164803      0.107589\n",
              "Cholesterol  -0.095282   0.100893  ...  0.050148     -0.232741\n",
              "FastingBS     0.198039   0.070193  ...  0.052698      0.267291\n",
              "MaxHR        -0.382045  -0.112135  ... -0.160691     -0.400421\n",
              "Oldpeak       0.258612   0.164803  ...  1.000000      0.403951\n",
              "HeartDisease  0.282039   0.107589  ...  0.403951      1.000000\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.heatmap(df.corr(), annot=True, cmap='RdYlBu')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "PnkFTewP1Whc",
        "outputId": "0153bed4-f247-4ec5-bd34-13d1ff5f6a30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4dd4f3d390>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Sex:\",df['Sex'].unique())\n",
        "print(\"RestingECG:\",df['RestingECG'].unique())\n",
        "print(\"ChestPainType:\",df['ChestPainType'].unique())\n",
        "print(\"ExerciseAngina:\",df['ExerciseAngina'].unique())\n",
        "print(\"ST_Slope:\",df['ST_Slope'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CuNznzSY2Njj",
        "outputId": "e2099699-d533-47ff-c6fc-806be57f8e12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sex: ['M' 'F']\n",
            "RestingECG: ['Normal' 'ST' 'LVH']\n",
            "ChestPainType: ['ATA' 'NAP' 'ASY' 'TA']\n",
            "ExerciseAngina: ['N' 'Y']\n",
            "ST_Slope: ['Up' 'Flat' 'Down']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include=[object])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "WMuUB0TR2Hoy",
        "outputId": "04a01efe-1277-4f9f-82a2-dbba523e099c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </thead>\n",
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              "      <th>count</th>\n",
              "      <td>918</td>\n",
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              "      <td>3</td>\n",
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              "      <td>Flat</td>\n",
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              "      <td>725</td>\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        Sex ChestPainType RestingECG ExerciseAngina ST_Slope\n",
              "count   918           918        918            918      918\n",
              "unique    2             4          3              2        3\n",
              "top       M           ASY     Normal              N     Flat\n",
              "freq    725           496        552            547      460"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f, axes = plt.subplots(2, 3, figsize=(15, 8))\n",
        "\n",
        "sns.countplot(x = df['HeartDisease'], data = df, palette='rocket', ax=axes[0,0])\n",
        "sns.countplot(x = df['RestingECG'], data = df, palette='rocket', ax=axes[0,1])\n",
        "sns.countplot(x = df['ChestPainType'], data = df, palette='rocket', ax=axes[0,2])\n",
        "\n",
        "sns.countplot(x = df['ExerciseAngina'], data = df, palette='rocket', ax=axes[1,0])\n",
        "sns.countplot(x = df['RestingECG'], data = df, palette='rocket', ax=axes[1,1])\n",
        "sns.countplot(x = df['Sex'], data = df, palette='rocket', ax=axes[1,2])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "DH_sKu6h2_WL",
        "outputId": "50722fc4-9060-4e29-fe8b-c3841e5da683"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10, 8))\n",
        "sns.displot(df['Cholesterol'])\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# found many 0 values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "wpurDyyr2dOn",
        "outputId": "8f42078f-0fe0-4cc0-8d1c-d93e5f088409"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"median of Cholesterol : \" ,df['Cholesterol'].median())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jazexztSG_oL",
        "outputId": "04a02a3e-0fb9-4712-8d96-ae0ce593a8e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "median of Cholesterol :  223.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 0 values replacing them with median\n",
        "df['Cholesterol'] = df['Cholesterol'].replace(0, df['Cholesterol'].median())"
      ],
      "metadata": {
        "id": "R50ayL42GTMu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install category_encoders"
      ],
      "metadata": {
        "id": "OGYXl3zfIRvm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "534866b9-a0da-49a4-b563-d386ca165c03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: category_encoders in /usr/local/lib/python3.7/dist-packages (2.3.0)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (0.10.2)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.4.1)\n",
            "Requirement already satisfied: pandas>=0.21.1 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.3.5)\n",
            "Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (0.5.2)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.19.5)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.0.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21.1->category_encoders) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21.1->category_encoders) (2.8.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.5.1->category_encoders) (1.15.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.0->category_encoders) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.0->category_encoders) (3.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import category_encoders as ce"
      ],
      "metadata": {
        "id": "krs0vSJ7HJEH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# one hot encoding to convert categorical cols into numerical cols\n",
        "encoder = ce.OneHotEncoder(use_cat_names=True)"
      ],
      "metadata": {
        "id": "JyaDj2coH6mG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['HeartDisease']"
      ],
      "metadata": {
        "id": "VQWrOXoZI3Rg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# drop tagert col heart disease\n",
        "\n",
        "X = df.drop('HeartDisease', axis='columns')"
      ],
      "metadata": {
        "id": "wM2j1yZvI9LP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#dividing the dataset into train and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=1)"
      ],
      "metadata": {
        "id": "X9BJHKxnJVc4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Train :', X_train.shape, y_train.shape)\n",
        "print('Test :', X_test.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0_1E-qgCMP4z",
        "outputId": "c66eaa8b-e54e-478a-a7ce-b8cf35e16fcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train : (734, 11) (734,)\n",
            "Test : (184, 11) (184,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoder.fit(X_train)\n",
        "X_train_enc = encoder.transform(X_train)\n",
        "X_test_enc = encoder.transform(X_test)"
      ],
      "metadata": {
        "id": "Ffekse6CMrc3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_enc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "GEUtcD1yNnRC",
        "outputId": "fedd6243-37fd-41b1-f996-23603b40cf13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </tbody>\n",
              "</table>\n",
              "<p>734 rows × 20 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c1f2fbb-ed07-4dee-8459-dc925b5bdcf2')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-7c1f2fbb-ed07-4dee-8459-dc925b5bdcf2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7c1f2fbb-ed07-4dee-8459-dc925b5bdcf2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     Age  Sex_M  Sex_F  ...  ST_Slope_Flat  ST_Slope_Up  ST_Slope_Down\n",
              "852   43      1      0  ...              1            0              0\n",
              "121   52      0      1  ...              0            1              0\n",
              "664   65      0      1  ...              1            0              0\n",
              "187   41      1      0  ...              1            0              0\n",
              "108   50      1      0  ...              0            1              0\n",
              "..   ...    ...    ...  ...            ...          ...            ...\n",
              "767   54      0      1  ...              0            1              0\n",
              "72    52      1      0  ...              1            0              0\n",
              "908   63      1      0  ...              0            1              0\n",
              "235   39      1      0  ...              1            0              0\n",
              "37    41      0      1  ...              0            1              0\n",
              "\n",
              "[734 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Mqj-CpWKN0FC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# model building\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier \n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "\n",
        "#libraries for model evaluation\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "agzFDW7GO7_T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. logistic regression\n",
        "lr = LogisticRegression(max_iter=9000, solver='lbfgs')\n",
        "model1 = lr.fit(X_train_enc, y_train)\n",
        "print(\"train accuracy:\", model1.score(X_train_enc, y_train))\n",
        "print(\"test accuracy:\", model1.score(X_test_enc,y_test))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8bEkQO0AQLm9",
        "outputId": "18989fed-7076-478f-edb9-0af33d403b39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train accuracy: 0.8555858310626703\n",
            "test accuracy: 0.907608695652174\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model1.predict(X_test_enc)\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G-HP2HlXSZkt",
        "outputId": "8d2ed878-52af-4f6b-a799-cca9c0f5fd5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0,\n",
              "       1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0,\n",
              "       0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1,\n",
              "       1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
              "       0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0,\n",
              "       0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0,\n",
              "       0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
              "       0, 0, 1, 1, 0, 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"classification report for logistic regression \\n \")\n",
        "print(classification_report(y_test,predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GvyyPr97U3UP",
        "outputId": "736eafb3-a45f-4c49-d6af-ccc242eccbc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classification report for logistic regression \n",
            " \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.88      0.88        74\n",
            "           1       0.92      0.93      0.92       110\n",
            "\n",
            "    accuracy                           0.91       184\n",
            "   macro avg       0.90      0.90      0.90       184\n",
            "weighted avg       0.91      0.91      0.91       184\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DoBniVmtStMf",
        "outputId": "99d8ef88-b80e-444e-d9f4-eb30706894f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 65,   9],\n",
              "       [  8, 102]])"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot \n",
        "print(\"confusion matrix for logistic regression\")\n",
        "plot_confusion_matrix(model1, X_test_enc, y_test,cmap=plt.cm.Blues, values_format='d')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "j5BcGA0kT4Ft",
        "outputId": "697dd54a-5a18-44bc-c1d9-a3db56893d38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "confusion matrix for logistic regression\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f4dc788b390>"
            ]
          },
          "metadata": {},
          "execution_count": 110
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#2. decision tree classifier\n",
        "dt = DecisionTreeClassifier()\n",
        "model2 = dt.fit(X_train_enc, y_train)\n",
        "print(\"train accuracy:\", model2.score(X_train_enc, y_train))\n",
        "print(\"test accuracy:\",model2.score(X_test_enc,y_test))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VspwUvqPX-BI",
        "outputId": "219a2a65-e600-4619-f711-653d1be6be07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train accuracy: 1.0\n",
            "test accuracy: 0.7934782608695652\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model2.predict(X_test_enc)\n",
        "print(\"classification report for decision tree classifier \\n \")\n",
        "print(classification_report(y_test,predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dSew4L7UZG0g",
        "outputId": "681c44dc-7146-4fd9-c7d1-7a9a03906dff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classification report for decision tree classifier \n",
            " \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.89      0.78        74\n",
            "           1       0.91      0.73      0.81       110\n",
            "\n",
            "    accuracy                           0.79       184\n",
            "   macro avg       0.80      0.81      0.79       184\n",
            "weighted avg       0.82      0.79      0.80       184\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6Z-PBlEab-J",
        "outputId": "8d1ef589-14ca-441e-b925-10dd884fef29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[66,  8],\n",
              "       [30, 80]])"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#3. random forest classifier\n",
        "rf = RandomForestClassifier()\n",
        "model3 = rf.fit(X_train_enc, y_train)\n",
        "print(\"train accuracy:\",model3.score(X_train_enc, y_train))\n",
        "print(\"test accuracy:\",model3.score(X_test_enc,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sybtOYv5aicK",
        "outputId": "ec6f123e-6140-41b2-c524-cca588b3c9e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train accuracy: 1.0\n",
            "test accuracy: 0.8913043478260869\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model3.predict(X_test_enc)\n",
        "print(\"classification report for random forest classifier \\n \")\n",
        "print(classification_report(y_test, predictions))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ivbj7DZlb-r2",
        "outputId": "0c47e1a7-2924-443f-d34b-a326300c6d3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classification report for random forest classifier \n",
            " \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.88      0.87        74\n",
            "           1       0.92      0.90      0.91       110\n",
            "\n",
            "    accuracy                           0.89       184\n",
            "   macro avg       0.89      0.89      0.89       184\n",
            "weighted avg       0.89      0.89      0.89       184\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kkoY6GI6bhWa",
        "outputId": "26254c40-b2a6-4ca8-f31f-7397020a8962"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[65,  9],\n",
              "       [11, 99]])"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. bagging classifier\n",
        "bg = BaggingClassifier()\n",
        "model4 = bg.fit(X_train_enc, y_train)\n",
        "print(\"train accuracy:\",model4.score(X_train_enc, y_train))\n",
        "print(\"test accuracy:\",model4.score(X_test_enc,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tEiPR3xFb40b",
        "outputId": "81d55ef5-a1a1-418e-c3bc-6d005e3ccdd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train accuracy: 0.989100817438692\n",
            "test accuracy: 0.8695652173913043\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model4.predict(X_test_enc)\n",
        "print(\"classification report for bagging classifier \\n\")\n",
        "print(classification_report(y_test, predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRdX0yDJc3G4",
        "outputId": "9a6c97d2-53d8-4b6c-9778-572e6748b00f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classification report for bagging classifier \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.89      0.85        74\n",
            "           1       0.92      0.85      0.89       110\n",
            "\n",
            "    accuracy                           0.87       184\n",
            "   macro avg       0.86      0.87      0.87       184\n",
            "weighted avg       0.87      0.87      0.87       184\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J9LL3uUfdKdk",
        "outputId": "740e4579-7f62-415f-9630-32544286a0aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[66,  8],\n",
              "       [16, 94]])"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. gradient boost classifier \n",
        "gbm = GradientBoostingClassifier()\n",
        "model5 = gbm.fit(X_train_enc, y_train)\n",
        "print(\"train accuracy:\",model5.score(X_train_enc, y_train))\n",
        "print(\"test accuracy:\",model5.score(X_test_enc,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KuNeCHVxdOE7",
        "outputId": "b9366e3c-0819-45d4-cc95-2fd4d83228ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train accuracy: 0.9495912806539509\n",
            "test accuracy: 0.8858695652173914\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model5.predict(X_test_enc)\n",
        "print(\"classification report for gradient boost classifier \\n\")\n",
        "print(classification_report(y_test, predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "baI4Ijatdb0d",
        "outputId": "f88ca170-0303-4dc3-adec-b126f38a0f58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classification report for gradient boost classifier \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.88      0.86        74\n",
            "           1       0.92      0.89      0.90       110\n",
            "\n",
            "    accuracy                           0.89       184\n",
            "   macro avg       0.88      0.88      0.88       184\n",
            "weighted avg       0.89      0.89      0.89       184\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cgyfois9du7j",
        "outputId": "6e7e30fe-a909-45e0-c540-f9766c7285cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[65,  9],\n",
              "       [12, 98]])"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 6. adaboost classifier \n",
        "ada = AdaBoostClassifier()\n",
        "model6 = ada.fit(X_train_enc, y_train)\n",
        "print(\"train accuracy:\",model6.score(X_train_enc, y_train))\n",
        "print(\"test accuracy:\",model6.score(X_test_enc,y_test))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tr9dHTWBdzQj",
        "outputId": "8d387266-aa1a-42b3-f48c-00b73c4deb63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train accuracy: 0.8705722070844687\n",
            "test accuracy: 0.8913043478260869\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model6.predict(X_test_enc)\n",
        "print(\"classification report for adaboost classifier \\n\")\n",
        "print(classification_report(y_test, predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p2plN3A0eGIj",
        "outputId": "d69f7c6a-621d-4117-9e3a-b443af68d3b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classification report for adaboost classifier \n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.91      0.87        74\n",
            "           1       0.93      0.88      0.91       110\n",
            "\n",
            "    accuracy                           0.89       184\n",
            "   macro avg       0.89      0.89      0.89       184\n",
            "weighted avg       0.89      0.89      0.89       184\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8eh0xJf5edlq",
        "outputId": "9d4889cc-eec5-4451-f7f3-4eacb50a5193"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[67,  7],\n",
              "       [13, 97]])"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 7. voting classifer = GB + RF\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "clf1 = GradientBoostingClassifier()\n",
        "clf2 = RandomForestClassifier()\n",
        "\n",
        "vc = VotingClassifier(estimators=[('ext', clf1),('gbm', clf2)], voting='soft')\n",
        "model7 = vc.fit(X_train_enc, y_train)\n",
        "print(\"train accuracy:\",model7.score(X_train_enc, y_train))\n",
        "print(\"test accuracy:\",model7.score(X_test_enc,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3KgwryQbekJ7",
        "outputId": "1b14023e-1361-4945-c1c6-ac5acfdb30cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train accuracy: 0.9782016348773842\n",
            "test accuracy: 0.8967391304347826\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vcpred = vc.predict(X_test_enc)\n",
        "print(\"classification report for voting classifier\")\n",
        "print(classification_report(vcpred,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0qACr9-e7Du",
        "outputId": "4462c56d-03fc-40b7-bee7-ced939a1abe9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "classification report for voting classifier\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.88      0.87        73\n",
            "           1       0.92      0.91      0.91       111\n",
            "\n",
            "    accuracy                           0.90       184\n",
            "   macro avg       0.89      0.89      0.89       184\n",
            "weighted avg       0.90      0.90      0.90       184\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,vcpred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWw-lNvNfEwX",
        "outputId": "0e7f50c4-0c72-4a98-9d18-3b98fca8ee09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 64,  10],\n",
              "       [  9, 101]])"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# models"
      ],
      "metadata": {
        "id": "FigZ370tfgXR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.   Logistic Regression : 91%\n",
        "\n",
        "1.  Decision Tree : 79%\n",
        "\n",
        "1.   Random Frorest : 89 %\n",
        "\n",
        "1.   Bagging Classifier : 87 %\n",
        "\n",
        "1.   Gradient Boost : 89 % \n",
        "\n",
        "1.   Adaboost : 89 %\n",
        "1.   Voting Classifier : 90%\n",
        "\n",
        "From the above fitted models Gradient Boost & Voting classifier has the highest classification accuracy of 90 % \n",
        " \n",
        "\n"
      ],
      "metadata": {
        "id": "kHnoJferfbgX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# taking inputs from user & making predictions"
      ],
      "metadata": {
        "id": "N19_5TKXfSr7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d = {}\n",
        "d['MaxHR'] = float(input('What is Max Heart rate of the patient ?'))\n",
        "d['Age'] = float(input('What is age of the patient ?'))\n",
        "d['Sex'] = input('What is Sex of the patient (M or F) ?')\n",
        "d['RestingBP'] = float(input('What is Resting blood pressure of the patient ?'))\n",
        "d['Cholesterol'] = float(input('What is Cholesterol of the patient ?'))\n",
        "d['ExcerciseAngina'] = input('Does the patient have excercise induced angina (Y or N) ?')\n",
        "d['FastingBS'] = float(input('What is fasting blood sugar of the patient ? [1 : FastingBS > 120 mg/dl, 0 : otherwise]'))\n",
        "d['ChestPainType'] = input('What is ChestPainType of the patient ?(use capital letters)[TA: Typical Angina , ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]')\n",
        "d['ST_Slope'] = input('What is Resting ST slope of the patient (Up, flat, down) ?')\n",
        "d['RestingECG'] = input('What is Resting ECG of the patient (LHV, Normal, ST)?')\n",
        "d['Oldpeak'] = float(input('What is Oldpeak value of the patient ?'))\n",
        "print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')\n",
        "print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QgbHap8dNZwJ",
        "outputId": "ac943a62-3899-4c1d-ddee-6b37f7cbace1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "What is Max Heart rate of the patient ? 150\n",
            "What is age of the patient ? 65\n",
            "What is Sex of the patient (M or F) ? F\n",
            "What is Resting blood pressure of the patient ? 150\n",
            "What is Cholesterol of the patient ? 220\n",
            "Does the patient have excercise induced angina (Y or N) ? N\n",
            "What is fasting blood sugar of the patient ? [1 : FastingBS > 120 mg/dl, 0 : otherwise] 1\n",
            "What is ChestPainType of the patient ?(use capital letters)[TA: Typical Angina , ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]NAP\n",
            "What is Resting ST slope of the patient (Up, flat, down) ? Up\n",
            "What is Resting ECG of the patient (LHV, Normal, ST)? Normal\n",
            "What is Oldpeak value of the patient ? 1\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
            "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fe55jpSnNZ0e",
        "outputId": "3c7db375-7a41-499d-e600-a88472ef5d54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'MaxHR': 150.0, 'Age': 65.0, 'Sex': ' F', 'RestingBP': 150.0, 'Cholesterol': 220.0, 'ExcerciseAngina': ' N', 'FastingBS': 1.0, 'ChestPainType': 'NAP', 'ST_Slope': ' Up', 'RestingECG': ' Normal', 'Oldpeak': 1.0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unseen = pd.DataFrame(d.items(), index = d).T\n",
        "unseen = unseen.drop([0])\n",
        "unseen"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "8lPXMJ0hNZ3a",
        "outputId": "b2ae8045-a5ea-4736-c616-0aa1c76513e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-20eb9abb-2ec8-494e-a30c-b59dc5489a51\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Age</th>\n",
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              "      <th>RestingBP</th>\n",
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              "      <td>150.0</td>\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-20eb9abb-2ec8-494e-a30c-b59dc5489a51 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-20eb9abb-2ec8-494e-a30c-b59dc5489a51');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   MaxHR   Age Sex RestingBP  ... ChestPainType ST_Slope RestingECG Oldpeak\n",
              "1  150.0  65.0   F     150.0  ...           NAP       Up     Normal     1.0\n",
              "\n",
              "[1 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " unseen1 = unseen.drop(columns=['Sex','ExcerciseAngina','ChestPainType','ST_Slope','RestingECG'])\n",
        " unseen1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "cC4XC7oaeTat",
        "outputId": "a2f0c903-fd37-42eb-992b-e0d30714c453"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-2bd71628-1e99-4da9-8094-acee929b69ad\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Age</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>Oldpeak</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>150.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2bd71628-1e99-4da9-8094-acee929b69ad')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2bd71628-1e99-4da9-8094-acee929b69ad button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2bd71628-1e99-4da9-8094-acee929b69ad');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   MaxHR   Age RestingBP Cholesterol FastingBS Oldpeak\n",
              "1  150.0  65.0     150.0       220.0       1.0     1.0"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to convert categorical col into numerical \n",
        "encoder2 = ce.OneHotEncoder(use_cat_names=True)\n",
        "encoder2.fit(unseen[['Sex','ExcerciseAngina','ChestPainType','ST_Slope','RestingECG']])\n",
        "unseen_enc = encoder2.transform(unseen[['Sex','ExcerciseAngina','ChestPainType','ST_Slope','RestingECG']])\n",
        "unseen_enc\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "M0-PzZKXe3AL",
        "outputId": "027a32a9-b778-4729-b0d2-c2dfc8b84813"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-86f740b2-4953-4bed-b50b-fc5030437632\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sex_ F</th>\n",
              "      <th>ExcerciseAngina_ N</th>\n",
              "      <th>ChestPainType_NAP</th>\n",
              "      <th>ST_Slope_ Up</th>\n",
              "      <th>RestingECG_ Normal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86f740b2-4953-4bed-b50b-fc5030437632')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-86f740b2-4953-4bed-b50b-fc5030437632 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-86f740b2-4953-4bed-b50b-fc5030437632');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Sex_ F  ExcerciseAngina_ N  ...  ST_Slope_ Up  RestingECG_ Normal\n",
              "1       1                   1  ...             1                   1\n",
              "\n",
              "[1 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to concat\n",
        "unseen_with_encoding = pd.concat([unseen_enc, unseen1], axis=1, join=\"inner\")\n",
        "unseen_with_encoding"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "yMhCxjQRNaF4",
        "outputId": "3128e6c6-895e-4f53-c3aa-0224c23111eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-042d8402-9436-4962-9ea3-de31dfa76c74\">\n",
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              "<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",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Sex_ F</th>\n",
              "      <th>ExcerciseAngina_ N</th>\n",
              "      <th>ChestPainType_NAP</th>\n",
              "      <th>ST_Slope_ Up</th>\n",
              "      <th>RestingECG_ Normal</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Age</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>Oldpeak</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>150.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-042d8402-9436-4962-9ea3-de31dfa76c74')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-042d8402-9436-4962-9ea3-de31dfa76c74 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-042d8402-9436-4962-9ea3-de31dfa76c74');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Sex_ F  ExcerciseAngina_ N  ChestPainType_NAP  ...  Cholesterol  FastingBS Oldpeak\n",
              "1       1                   1                  1  ...        220.0        1.0     1.0\n",
              "\n",
              "[1 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to make list\n",
        "list1=list(unseen_with_encoding.columns.values)\n",
        "list1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8VYjtDvVe3E9",
        "outputId": "e2249d9a-1d61-42b3-eb90-205b9dab9bd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Sex_ F',\n",
              " 'ExcerciseAngina_ N',\n",
              " 'ChestPainType_NAP',\n",
              " 'ST_Slope_ Up',\n",
              " 'RestingECG_ Normal',\n",
              " 'MaxHR',\n",
              " 'Age',\n",
              " 'RestingBP',\n",
              " 'Cholesterol',\n",
              " 'FastingBS',\n",
              " 'Oldpeak']"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "603UQ_V1kGoA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "8QUtUL0HkG7L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "zqOA8bJ0kHOr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "JJeDAkwJPChc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "n7cAd5C8PClI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "MNxieWhaPCoO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "o2y7FSshPCrp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "FlZcaahePCv1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "3lZKe9w2PCyt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "UeSr8pLLPC1s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "2wUn3Vb-Os9V"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}