Assignment_8_REGex(Heart Attack Analysis & Prediction).ipynb

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      "name": "Heart Attack Analysis & Prediction .ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN+lt1N1nr+6rztloR6Mt4n",
      "include_colab_link": true
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      "name": "python3",
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      "name": "python"
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    {
      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Mano1720/DL-ML-/blob/main/Assignment_8_REGex(Heart%20Attack%20Analysis%20%26%20Prediction).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v9QUdfLc79ok"
      },
      "source": [
        "# **Task 8** \n",
        "\n",
        "***k Manoj***\n",
        "\n",
        "Referral Id:SIRSS2309"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0OVcqxii8VW8"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y5AlV7CR8_an"
      },
      "source": [
        "data=pd.read_csv('/content/heart.csv')"
      ],
      "execution_count": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tg88DV3V9O1a",
        "outputId": "9aff92e0-aab9-497c-effd-55b439c445f3"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalachh</th>\n",
              "      <th>exng</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slp</th>\n",
              "      <th>caa</th>\n",
              "      <th>thall</th>\n",
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              "      <td>1.4</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
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              "      <td>236</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex  cp  trtbps  chol  fbs  ...  exng  oldpeak  slp  caa  thall  output\n",
              "0   63    1   3     145   233    1  ...     0      2.3    0    0      1       1\n",
              "1   37    1   2     130   250    0  ...     0      3.5    0    0      2       1\n",
              "2   41    0   1     130   204    0  ...     0      1.4    2    0      2       1\n",
              "3   56    1   1     120   236    0  ...     0      0.8    2    0      2       1\n",
              "4   57    0   0     120   354    0  ...     1      0.6    2    0      2       1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
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      },
      "source": [
        "data.tail()"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>trtbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalachh</th>\n",
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              "      <th>301</th>\n",
              "      <td>57</td>\n",
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            ],
            "text/plain": [
              "     age  sex  cp  trtbps  chol  fbs  ...  exng  oldpeak  slp  caa  thall  output\n",
              "298   57    0   0     140   241    0  ...     1      0.2    1    0      3       0\n",
              "299   45    1   3     110   264    0  ...     0      1.2    1    0      3       0\n",
              "300   68    1   0     144   193    1  ...     0      3.4    1    2      3       0\n",
              "301   57    1   0     130   131    0  ...     1      1.2    1    1      3       0\n",
              "302   57    0   1     130   236    0  ...     0      0.0    1    1      2       0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "0ERCoBNc9SLL",
        "outputId": "eab184a2-0404-4982-f0fa-2b093ec7ce4d"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trtbps    303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalachh  303 non-null    int64  \n",
            " 8   exng      303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slp       303 non-null    int64  \n",
            " 11  caa       303 non-null    int64  \n",
            " 12  thall     303 non-null    int64  \n",
            " 13  output    303 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "ICrV7JLf9VSh",
        "outputId": "90e316da-3665-44a4-f8ee-1d18dfa9992f"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trtbps</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>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp  ...         caa       thall      output\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean    54.366337    0.683168    0.966997  ...    0.729373    2.313531    0.544554\n",
              "std      9.082101    0.466011    1.032052  ...    1.022606    0.612277    0.498835\n",
              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     47.500000    0.000000    0.000000  ...    0.000000    2.000000    0.000000\n",
              "50%     55.000000    1.000000    1.000000  ...    0.000000    2.000000    1.000000\n",
              "75%     61.000000    1.000000    2.000000  ...    1.000000    3.000000    1.000000\n",
              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000    1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DAFWqD999b4s"
      },
      "source": [
        "# **EDA**\n",
        "*Let's get some boxplots for each numeric variable and a correlation heatmap for the dataset.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "szbb4mO69XRa"
      },
      "source": [
        "numerics_features=['age', 'sex', 'trtbps', 'chol', 'thalachh', 'oldpeak', 'slp', 'caa']\n",
        "\n",
        "eda_df = data.loc[:, numerics_features].copy()\n"
      ],
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 915
        },
        "id": "AM1YN85m-DnV",
        "outputId": "b194bce8-b3e7-44f0-e530-2af61655cf80"
      },
      "source": [
        "plt.figure(figsize=(16,10))\n",
        "\n",
        "for i in range(len(eda_df.columns)):\n",
        "  plt.subplot(2,4,i+1)\n",
        "  sns.boxplot(eda_df[eda_df.columns[i]])"
      ],
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1152x720 with 8 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjEvRQQW-peq"
      },
      "source": [
        "corr=eda_df.corr()"
      ],
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "7A77g1Vi_TWl",
        "outputId": "9c840439-02d6-40fb-c7ec-46605fdfc06e"
      },
      "source": [
        "plt.figure(figsize=(12,10))\n",
        "sns.heatmap(corr,annot=True,fmt='.1f',cmap='inferno',vmin='-1')"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc9d71816d0>"
            ]
          },
          "metadata": {},
          "execution_count": 185
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "0JG62WNw_lyQ",
        "outputId": "38296211-91b1-403e-c10a-eb0cf92d03a8"
      },
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "plt.pie(data['output'].value_counts(), labels=[\"Heart Disease\", \"No Heart Disease\"], autopct='%.1f%%', colors=['#36a2ac', '#413f80'])\n",
        "plt.title(\"Class Distribution\")\n",
        "plt.show()"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nNP3_fpIAZyl"
      },
      "source": [
        "***Preprocessing***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSxB6gA0AJvz"
      },
      "source": [
        "def onehot_encode(df,column_dict):\n",
        "  df=df.copy()\n",
        "  for column, prefix in column_dict.items():\n",
        "    dummies=pd.get_dummies(df[column],prefix=prefix)\n",
        "    df=pd.concat([df,dummies],axis=1)\n",
        "    df.drop(column,axis=1)\n",
        "  return df  \n",
        "\n",
        "  "
      ],
      "execution_count": 187,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2HBbkVoPBii-"
      },
      "source": [
        "def preprocessing(data,scaler):\n",
        "  df=data.copy()\n",
        "\n",
        "  # One-hot encode the nominal features\n",
        "  nominal_features=['cp','slp','thall']\n",
        "  df=onehot_encode(df,dict(zip(nominal_features,['CP','SP','TH'])))\n",
        "\n",
        "  # Split df into X and y\n",
        "  X=df.drop('output',axis=1).copy()\n",
        "  y=df['output'].copy()\n",
        "\n",
        "  # Scale X\n",
        "  X=pd.DataFrame(scaler.fit_transform(X),columns=X.columns)\n",
        "\n",
        "  return X,y\n"
      ],
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xGZjN-l9CaBn"
      },
      "source": [
        "X,y=preprocessing(data,RobustScaler())"
      ],
      "execution_count": 189,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "sW4LMhDSDXXM",
        "outputId": "3dd23c75-7ced-4167-bb91-2f0995648f57"
      },
      "source": [
        "X"
      ],
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trtbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalachh</th>\n",
              "      <th>exng</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slp</th>\n",
              "      <th>caa</th>\n",
              "      <th>thall</th>\n",
              "      <th>CP_0</th>\n",
              "      <th>CP_1</th>\n",
              "      <th>CP_2</th>\n",
              "      <th>CP_3</th>\n",
              "      <th>SP_0</th>\n",
              "      <th>SP_1</th>\n",
              "      <th>SP_2</th>\n",
              "      <th>TH_0</th>\n",
              "      <th>TH_1</th>\n",
              "      <th>TH_2</th>\n",
              "      <th>TH_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.592593</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>-0.110236</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.092308</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9375</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.333333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.157480</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.046154</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.6875</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.037037</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.566929</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.584615</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3750</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.074074</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>-0.062992</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.769231</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.148148</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>1.795276</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.307692</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.1250</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>0.148148</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.015748</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.923077</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.3750</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>-0.740741</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.00</td>\n",
              "      <td>0.377953</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.646154</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>0.70</td>\n",
              "      <td>-0.740157</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.369231</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.6250</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>0.148148</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-1.716535</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.169231</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>0.148148</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.062992</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.646154</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.5000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>303 rows × 24 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          age  sex   cp  trtbps      chol  ...  SP_2  TH_0  TH_1  TH_2  TH_3\n",
              "0    0.592593  0.0  1.0    0.75 -0.110236  ...   0.0   0.0   1.0  -1.0   0.0\n",
              "1   -1.333333  0.0  0.5    0.00  0.157480  ...   0.0   0.0   0.0   0.0   0.0\n",
              "2   -1.037037 -1.0  0.0    0.00 -0.566929  ...   1.0   0.0   0.0   0.0   0.0\n",
              "3    0.074074  0.0  0.0   -0.50 -0.062992  ...   1.0   0.0   0.0   0.0   0.0\n",
              "4    0.148148 -1.0 -0.5   -0.50  1.795276  ...   1.0   0.0   0.0   0.0   0.0\n",
              "..        ...  ...  ...     ...       ...  ...   ...   ...   ...   ...   ...\n",
              "298  0.148148 -1.0 -0.5    0.50  0.015748  ...   0.0   0.0   0.0  -1.0   1.0\n",
              "299 -0.740741  0.0  1.0   -1.00  0.377953  ...   0.0   0.0   0.0  -1.0   1.0\n",
              "300  0.962963  0.0 -0.5    0.70 -0.740157  ...   0.0   0.0   0.0  -1.0   1.0\n",
              "301  0.148148  0.0 -0.5    0.00 -1.716535  ...   0.0   0.0   0.0  -1.0   1.0\n",
              "302  0.148148 -1.0  0.0    0.00 -0.062992  ...   0.0   0.0   0.0   0.0   0.0\n",
              "\n",
              "[303 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78TluRQjFx4I",
        "outputId": "f3ad7f17-c101-4b71-d1af-da52c66787d6"
      },
      "source": [
        "y"
      ],
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      1\n",
              "2      1\n",
              "3      1\n",
              "4      1\n",
              "      ..\n",
              "298    0\n",
              "299    0\n",
              "300    0\n",
              "301    0\n",
              "302    0\n",
              "Name: output, Length: 303, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X9jPIZjuF4Qt"
      },
      "source": [
        "***Training***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pfgbv3HdF2B_"
      },
      "source": [
        "X_train,X_test,y_train,y_test= train_test_split(X,y,test_size=0.2)"
      ],
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x1CWaT0IGUWA",
        "outputId": "0fb39f76-de07-4dc9-b0c1-d8b72ce3b131"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(242, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NJBfzAiYGXV_",
        "outputId": "0430fccf-ec57-42be-b272-a31fc232b1d5"
      },
      "source": [
        "svm_model=SVC()\n",
        "svm_model.fit(X_train,y_train)"
      ],
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {},
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AlBa53v2Gs2a",
        "outputId": "e7872ddf-e2a6-41f6-d17a-11951c6f4ef1"
      },
      "source": [
        "score=svm_model.score(X_test,y_test)\n",
        "print(\"Support Vector Machine Accuracy: {:.2f}%\".format(score*100))"
      ],
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Support Vector Machine Accuracy: 85.25%\n"
          ]
        }
      ]
    }
  ]
}