Heart_disease_prediction.ipynb

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        "tags": []
      },
      "source": [
        "# Importing Libraries"
      ]
    },
    {
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        {
          "ename": "ModuleNotFoundError",
          "evalue": "No module named 'pandas'",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[1;32mc:\\Users\\Raj\\Desktop\\Apt's Workspace\\HeartSync-Pro\\Heart_disease_prediction.ipynb Cell 2\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Raj/Desktop/Apt%27s%20Workspace/HeartSync-Pro/Heart_disease_prediction.ipynb#W1sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpd\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Raj/Desktop/Apt%27s%20Workspace/HeartSync-Pro/Heart_disease_prediction.ipynb#W1sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Raj/Desktop/Apt%27s%20Workspace/HeartSync-Pro/Heart_disease_prediction.ipynb#W1sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n",
            "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pandas'"
          ]
        }
      ],
      "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 StandardScaler\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
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          "start_time": "2023-04-09T17:37:12.419112",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "# Data"
      ]
    },
    {
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          "status": "completed"
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        "tags": []
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      "source": [
        "df = pd.read_csv(\"Heart_Disease_Prediction.csv\")"
      ]
    },
    {
      "cell_type": "code",
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        "id": "-OeBEe3Nuvo2",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 270 entries, 0 to 269\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Age       270 non-null    int64  \n",
            " 1   Sex       270 non-null    int64  \n",
            " 2   CP        270 non-null    int64  \n",
            " 3   trestbps  270 non-null    int64  \n",
            " 4   Chol      270 non-null    int64  \n",
            " 5   FBS       270 non-null    int64  \n",
            " 6   restECG   270 non-null    int64  \n",
            " 7   thalach   270 non-null    int64  \n",
            " 8   exang     270 non-null    int64  \n",
            " 9   oldpeak   270 non-null    float64\n",
            " 10  slope     270 non-null    int64  \n",
            " 11  Ca        270 non-null    int64  \n",
            " 12  thal      270 non-null    int64  \n",
            " 13  num       270 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 29.7 KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
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              "   Age  Sex  CP  trestbps  Chol  FBS  restECG  thalach  exang  oldpeak  slope  \\\n",
              "0   70    1   4       130   322    0        2      109      0      2.4      2   \n",
              "1   67    0   3       115   564    0        2      160      0      1.6      2   \n",
              "2   57    1   2       124   261    0        0      141      0      0.3      1   \n",
              "3   64    1   4       128   263    0        0      105      1      0.2      2   \n",
              "4   74    0   2       120   269    0        2      121      1      0.2      1   \n",
              "\n",
              "   Ca  thal  num  \n",
              "0   3     3    1  \n",
              "1   0     7    0  \n",
              "2   0     7    1  \n",
              "3   1     7    0  \n",
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              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>CP</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>Chol</th>\n",
              "      <th>FBS</th>\n",
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              "      <th>count</th>\n",
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              "      <td>270.000000</td>\n",
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              "      <td>270.000000</td>\n",
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              "      <td>270.000000</td>\n",
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              "      <td>270.000000</td>\n",
              "      <td>270.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.433333</td>\n",
              "      <td>0.677778</td>\n",
              "      <td>3.174074</td>\n",
              "      <td>131.344444</td>\n",
              "      <td>249.659259</td>\n",
              "      <td>0.148148</td>\n",
              "      <td>1.022222</td>\n",
              "      <td>149.677778</td>\n",
              "      <td>0.329630</td>\n",
              "      <td>1.05000</td>\n",
              "      <td>1.585185</td>\n",
              "      <td>0.670370</td>\n",
              "      <td>4.696296</td>\n",
              "      <td>0.444444</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.109067</td>\n",
              "      <td>0.468195</td>\n",
              "      <td>0.950090</td>\n",
              "      <td>17.861608</td>\n",
              "      <td>51.686237</td>\n",
              "      <td>0.355906</td>\n",
              "      <td>0.997891</td>\n",
              "      <td>23.165717</td>\n",
              "      <td>0.470952</td>\n",
              "      <td>1.14521</td>\n",
              "      <td>0.614390</td>\n",
              "      <td>0.943896</td>\n",
              "      <td>1.940659</td>\n",
              "      <td>0.497827</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>213.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>245.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>153.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.80000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>280.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.60000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.20000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Age         Sex          CP    trestbps        Chol         FBS  \\\n",
              "count  270.000000  270.000000  270.000000  270.000000  270.000000  270.000000   \n",
              "mean    54.433333    0.677778    3.174074  131.344444  249.659259    0.148148   \n",
              "std      9.109067    0.468195    0.950090   17.861608   51.686237    0.355906   \n",
              "min     29.000000    0.000000    1.000000   94.000000  126.000000    0.000000   \n",
              "25%     48.000000    0.000000    3.000000  120.000000  213.000000    0.000000   \n",
              "50%     55.000000    1.000000    3.000000  130.000000  245.000000    0.000000   \n",
              "75%     61.000000    1.000000    4.000000  140.000000  280.000000    0.000000   \n",
              "max     77.000000    1.000000    4.000000  200.000000  564.000000    1.000000   \n",
              "\n",
              "          restECG     thalach       exang    oldpeak       slope          Ca  \\\n",
              "count  270.000000  270.000000  270.000000  270.00000  270.000000  270.000000   \n",
              "mean     1.022222  149.677778    0.329630    1.05000    1.585185    0.670370   \n",
              "std      0.997891   23.165717    0.470952    1.14521    0.614390    0.943896   \n",
              "min      0.000000   71.000000    0.000000    0.00000    1.000000    0.000000   \n",
              "25%      0.000000  133.000000    0.000000    0.00000    1.000000    0.000000   \n",
              "50%      2.000000  153.500000    0.000000    0.80000    2.000000    0.000000   \n",
              "75%      2.000000  166.000000    1.000000    1.60000    2.000000    1.000000   \n",
              "max      2.000000  202.000000    1.000000    6.20000    3.000000    3.000000   \n",
              "\n",
              "             thal         num  \n",
              "count  270.000000  270.000000  \n",
              "mean     4.696296    0.444444  \n",
              "std      1.940659    0.497827  \n",
              "min      3.000000    0.000000  \n",
              "25%      3.000000    0.000000  \n",
              "50%      3.000000    0.000000  \n",
              "75%      7.000000    1.000000  \n",
              "max      7.000000    1.000000  "
            ]
          },
          "execution_count": 168,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2023-04-09T17:37:12.792555Z",
          "iopub.status.busy": "2023-04-09T17:37:12.791543Z",
          "iopub.status.idle": "2023-04-09T17:37:12.796125Z",
          "shell.execute_reply": "2023-04-09T17:37:12.795463Z",
          "shell.execute_reply.started": "2023-02-28T18:23:11.298396Z"
        },
        "id": "O2LAslNUuvo5",
        "outputId": "5dc7cc94-57c3-4402-ce36-b712f74a20fc",
        "papermill": {
          "duration": 0.031692,
          "end_time": "2023-04-09T17:37:12.796251",
          "exception": false,
          "start_time": "2023-04-09T17:37:12.764559",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(270, 14)"
            ]
          },
          "execution_count": 278,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2023-04-09T17:37:12.848012Z",
          "iopub.status.busy": "2023-04-09T17:37:12.847095Z",
          "iopub.status.idle": "2023-04-09T17:37:12.850772Z",
          "shell.execute_reply": "2023-04-09T17:37:12.851314Z",
          "shell.execute_reply.started": "2023-02-28T18:23:11.311019Z"
        },
        "id": "OfuHKzM4uvo5",
        "outputId": "439582da-28eb-4d2b-832a-4b28afb628f9",
        "papermill": {
          "duration": 0.032776,
          "end_time": "2023-04-09T17:37:12.851482",
          "exception": false,
          "start_time": "2023-04-09T17:37:12.818706",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "execution_count": 279,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Checking for missing values.\n",
        "df.isnull().values.any()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2023-04-09T17:37:12.961324Z",
          "iopub.status.busy": "2023-04-09T17:37:12.960446Z",
          "iopub.status.idle": "2023-04-09T17:37:12.964851Z",
          "shell.execute_reply": "2023-04-09T17:37:12.964329Z",
          "shell.execute_reply.started": "2023-02-28T18:23:11.339031Z"
        },
        "id": "6A8neuz0uvo6",
        "outputId": "54c676fb-08f7-465a-8efb-e88818469782",
        "papermill": {
          "duration": 0.034272,
          "end_time": "2023-04-09T17:37:12.965008",
          "exception": false,
          "start_time": "2023-04-09T17:37:12.930736",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    150\n",
              "1    120\n",
              "Name: num, dtype: int64"
            ]
          },
          "execution_count": 172,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Checking for imbalanced data based on outcome.\n",
        "df['num'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "execution": {
          "iopub.execute_input": "2023-04-09T17:37:13.250668Z",
          "iopub.status.busy": "2023-04-09T17:37:13.240582Z",
          "iopub.status.idle": "2023-04-09T17:37:13.255479Z",
          "shell.execute_reply": "2023-04-09T17:37:13.254809Z",
          "shell.execute_reply.started": "2023-02-28T18:23:11.536131Z"
        },
        "id": "TtyNySvuuvo7",
        "outputId": "80468bff-d724-4441-d9cd-9b90cd818059",
        "papermill": {
          "duration": 0.051352,
          "end_time": "2023-04-09T17:37:13.255603",
          "exception": false,
          "start_time": "2023-04-09T17:37:13.204251",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>CP</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>Chol</th>\n",
              "      <th>FBS</th>\n",
              "      <th>restECG</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>Ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.094401</td>\n",
              "      <td>0.096920</td>\n",
              "      <td>0.273053</td>\n",
              "      <td>0.220056</td>\n",
              "      <td>0.123458</td>\n",
              "      <td>0.128171</td>\n",
              "      <td>-0.402215</td>\n",
              "      <td>0.098297</td>\n",
              "      <td>0.194234</td>\n",
              "      <td>0.159774</td>\n",
              "      <td>0.356081</td>\n",
              "      <td>0.106100</td>\n",
              "      <td>0.212322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>-0.094401</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.034636</td>\n",
              "      <td>-0.062693</td>\n",
              "      <td>-0.201647</td>\n",
              "      <td>0.042140</td>\n",
              "      <td>0.039253</td>\n",
              "      <td>-0.076101</td>\n",
              "      <td>0.180022</td>\n",
              "      <td>0.097412</td>\n",
              "      <td>0.050545</td>\n",
              "      <td>0.086830</td>\n",
              "      <td>0.391046</td>\n",
              "      <td>0.297721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CP</th>\n",
              "      <td>0.096920</td>\n",
              "      <td>0.034636</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.043196</td>\n",
              "      <td>0.090465</td>\n",
              "      <td>-0.098537</td>\n",
              "      <td>0.074325</td>\n",
              "      <td>-0.317682</td>\n",
              "      <td>0.353160</td>\n",
              "      <td>0.167244</td>\n",
              "      <td>0.136900</td>\n",
              "      <td>0.225890</td>\n",
              "      <td>0.262659</td>\n",
              "      <td>0.417436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>0.273053</td>\n",
              "      <td>-0.062693</td>\n",
              "      <td>-0.043196</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.173019</td>\n",
              "      <td>0.155681</td>\n",
              "      <td>0.116157</td>\n",
              "      <td>-0.039136</td>\n",
              "      <td>0.082793</td>\n",
              "      <td>0.222800</td>\n",
              "      <td>0.142472</td>\n",
              "      <td>0.085697</td>\n",
              "      <td>0.132045</td>\n",
              "      <td>0.155383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chol</th>\n",
              "      <td>0.220056</td>\n",
              "      <td>-0.201647</td>\n",
              "      <td>0.090465</td>\n",
              "      <td>0.173019</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.025186</td>\n",
              "      <td>0.167652</td>\n",
              "      <td>-0.018739</td>\n",
              "      <td>0.078243</td>\n",
              "      <td>0.027709</td>\n",
              "      <td>-0.005755</td>\n",
              "      <td>0.126541</td>\n",
              "      <td>0.028836</td>\n",
              "      <td>0.118021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FBS</th>\n",
              "      <td>0.123458</td>\n",
              "      <td>0.042140</td>\n",
              "      <td>-0.098537</td>\n",
              "      <td>0.155681</td>\n",
              "      <td>0.025186</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.053499</td>\n",
              "      <td>0.022494</td>\n",
              "      <td>-0.004107</td>\n",
              "      <td>-0.025538</td>\n",
              "      <td>0.044076</td>\n",
              "      <td>0.123774</td>\n",
              "      <td>0.049237</td>\n",
              "      <td>-0.016319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>restECG</th>\n",
              "      <td>0.128171</td>\n",
              "      <td>0.039253</td>\n",
              "      <td>0.074325</td>\n",
              "      <td>0.116157</td>\n",
              "      <td>0.167652</td>\n",
              "      <td>0.053499</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.074628</td>\n",
              "      <td>0.095098</td>\n",
              "      <td>0.120034</td>\n",
              "      <td>0.160614</td>\n",
              "      <td>0.114368</td>\n",
              "      <td>0.007337</td>\n",
              "      <td>0.182091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>-0.402215</td>\n",
              "      <td>-0.076101</td>\n",
              "      <td>-0.317682</td>\n",
              "      <td>-0.039136</td>\n",
              "      <td>-0.018739</td>\n",
              "      <td>0.022494</td>\n",
              "      <td>-0.074628</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.380719</td>\n",
              "      <td>-0.349045</td>\n",
              "      <td>-0.386847</td>\n",
              "      <td>-0.265333</td>\n",
              "      <td>-0.253397</td>\n",
              "      <td>-0.418514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exang</th>\n",
              "      <td>0.098297</td>\n",
              "      <td>0.180022</td>\n",
              "      <td>0.353160</td>\n",
              "      <td>0.082793</td>\n",
              "      <td>0.078243</td>\n",
              "      <td>-0.004107</td>\n",
              "      <td>0.095098</td>\n",
              "      <td>-0.380719</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.274672</td>\n",
              "      <td>0.255908</td>\n",
              "      <td>0.153347</td>\n",
              "      <td>0.321449</td>\n",
              "      <td>0.419303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>0.194234</td>\n",
              "      <td>0.097412</td>\n",
              "      <td>0.167244</td>\n",
              "      <td>0.222800</td>\n",
              "      <td>0.027709</td>\n",
              "      <td>-0.025538</td>\n",
              "      <td>0.120034</td>\n",
              "      <td>-0.349045</td>\n",
              "      <td>0.274672</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.609712</td>\n",
              "      <td>0.255005</td>\n",
              "      <td>0.324333</td>\n",
              "      <td>0.417967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <td>0.159774</td>\n",
              "      <td>0.050545</td>\n",
              "      <td>0.136900</td>\n",
              "      <td>0.142472</td>\n",
              "      <td>-0.005755</td>\n",
              "      <td>0.044076</td>\n",
              "      <td>0.160614</td>\n",
              "      <td>-0.386847</td>\n",
              "      <td>0.255908</td>\n",
              "      <td>0.609712</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.109498</td>\n",
              "      <td>0.283678</td>\n",
              "      <td>0.337616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ca</th>\n",
              "      <td>0.356081</td>\n",
              "      <td>0.086830</td>\n",
              "      <td>0.225890</td>\n",
              "      <td>0.085697</td>\n",
              "      <td>0.126541</td>\n",
              "      <td>0.123774</td>\n",
              "      <td>0.114368</td>\n",
              "      <td>-0.265333</td>\n",
              "      <td>0.153347</td>\n",
              "      <td>0.255005</td>\n",
              "      <td>0.109498</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.255648</td>\n",
              "      <td>0.455336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thal</th>\n",
              "      <td>0.106100</td>\n",
              "      <td>0.391046</td>\n",
              "      <td>0.262659</td>\n",
              "      <td>0.132045</td>\n",
              "      <td>0.028836</td>\n",
              "      <td>0.049237</td>\n",
              "      <td>0.007337</td>\n",
              "      <td>-0.253397</td>\n",
              "      <td>0.321449</td>\n",
              "      <td>0.324333</td>\n",
              "      <td>0.283678</td>\n",
              "      <td>0.255648</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.525020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num</th>\n",
              "      <td>0.212322</td>\n",
              "      <td>0.297721</td>\n",
              "      <td>0.417436</td>\n",
              "      <td>0.155383</td>\n",
              "      <td>0.118021</td>\n",
              "      <td>-0.016319</td>\n",
              "      <td>0.182091</td>\n",
              "      <td>-0.418514</td>\n",
              "      <td>0.419303</td>\n",
              "      <td>0.417967</td>\n",
              "      <td>0.337616</td>\n",
              "      <td>0.455336</td>\n",
              "      <td>0.525020</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Age       Sex        CP  trestbps      Chol       FBS  \\\n",
              "Age       1.000000 -0.094401  0.096920  0.273053  0.220056  0.123458   \n",
              "Sex      -0.094401  1.000000  0.034636 -0.062693 -0.201647  0.042140   \n",
              "CP        0.096920  0.034636  1.000000 -0.043196  0.090465 -0.098537   \n",
              "trestbps  0.273053 -0.062693 -0.043196  1.000000  0.173019  0.155681   \n",
              "Chol      0.220056 -0.201647  0.090465  0.173019  1.000000  0.025186   \n",
              "FBS       0.123458  0.042140 -0.098537  0.155681  0.025186  1.000000   \n",
              "restECG   0.128171  0.039253  0.074325  0.116157  0.167652  0.053499   \n",
              "thalach  -0.402215 -0.076101 -0.317682 -0.039136 -0.018739  0.022494   \n",
              "exang     0.098297  0.180022  0.353160  0.082793  0.078243 -0.004107   \n",
              "oldpeak   0.194234  0.097412  0.167244  0.222800  0.027709 -0.025538   \n",
              "slope     0.159774  0.050545  0.136900  0.142472 -0.005755  0.044076   \n",
              "Ca        0.356081  0.086830  0.225890  0.085697  0.126541  0.123774   \n",
              "thal      0.106100  0.391046  0.262659  0.132045  0.028836  0.049237   \n",
              "num       0.212322  0.297721  0.417436  0.155383  0.118021 -0.016319   \n",
              "\n",
              "           restECG   thalach     exang   oldpeak     slope        Ca  \\\n",
              "Age       0.128171 -0.402215  0.098297  0.194234  0.159774  0.356081   \n",
              "Sex       0.039253 -0.076101  0.180022  0.097412  0.050545  0.086830   \n",
              "CP        0.074325 -0.317682  0.353160  0.167244  0.136900  0.225890   \n",
              "trestbps  0.116157 -0.039136  0.082793  0.222800  0.142472  0.085697   \n",
              "Chol      0.167652 -0.018739  0.078243  0.027709 -0.005755  0.126541   \n",
              "FBS       0.053499  0.022494 -0.004107 -0.025538  0.044076  0.123774   \n",
              "restECG   1.000000 -0.074628  0.095098  0.120034  0.160614  0.114368   \n",
              "thalach  -0.074628  1.000000 -0.380719 -0.349045 -0.386847 -0.265333   \n",
              "exang     0.095098 -0.380719  1.000000  0.274672  0.255908  0.153347   \n",
              "oldpeak   0.120034 -0.349045  0.274672  1.000000  0.609712  0.255005   \n",
              "slope     0.160614 -0.386847  0.255908  0.609712  1.000000  0.109498   \n",
              "Ca        0.114368 -0.265333  0.153347  0.255005  0.109498  1.000000   \n",
              "thal      0.007337 -0.253397  0.321449  0.324333  0.283678  0.255648   \n",
              "num       0.182091 -0.418514  0.419303  0.417967  0.337616  0.455336   \n",
              "\n",
              "              thal       num  \n",
              "Age       0.106100  0.212322  \n",
              "Sex       0.391046  0.297721  \n",
              "CP        0.262659  0.417436  \n",
              "trestbps  0.132045  0.155383  \n",
              "Chol      0.028836  0.118021  \n",
              "FBS       0.049237 -0.016319  \n",
              "restECG   0.007337  0.182091  \n",
              "thalach  -0.253397 -0.418514  \n",
              "exang     0.321449  0.419303  \n",
              "oldpeak   0.324333  0.417967  \n",
              "slope     0.283678  0.337616  \n",
              "Ca        0.255648  0.455336  \n",
              "thal      1.000000  0.525020  \n",
              "num       0.525020  1.000000  "
            ]
          },
          "execution_count": 280,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Checking for any correlations.\n",
        "df.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-04-09T17:37:13.362674Z",
          "iopub.status.busy": "2023-04-09T17:37:13.361998Z",
          "iopub.status.idle": "2023-04-09T17:37:13.365570Z",
          "shell.execute_reply": "2023-04-09T17:37:13.365036Z",
          "shell.execute_reply.started": "2023-02-28T18:23:11.569052Z"
        },
        "id": "SpBve2Azuvo7",
        "papermill": {
          "duration": 0.036278,
          "end_time": "2023-04-09T17:37:13.365699",
          "exception": false,
          "start_time": "2023-04-09T17:37:13.329421",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(175, 13)\n",
            "(95, 13)\n",
            "     Age  Sex  CP  trestbps  Chol  FBS  restECG  thalach  exang  oldpeak  \\\n",
            "64    63    1   1       145   233    1        2      150      0      2.3   \n",
            "135   46    0   3       142   177    0        2      160      1      1.4   \n",
            "153   64    0   4       130   303    0        0      122      0      2.0   \n",
            "189   70    1   3       160   269    0        0      112      1      2.9   \n",
            "253   51    1   3       110   175    0        0      123      0      0.6   \n",
            "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
            "75    45    1   4       142   309    0        2      147      1      0.0   \n",
            "194   48    1   3       124   255    1        0      175      0      0.0   \n",
            "210   59    1   1       170   288    0        2      159      0      0.2   \n",
            "145   53    1   4       123   282    0        0       95      1      2.0   \n",
            "109   45    0   2       112   160    0        0      138      0      0.0   \n",
            "\n",
            "     slope  Ca  thal  \n",
            "64       3   0     6  \n",
            "135      3   0     3  \n",
            "153      2   2     3  \n",
            "189      2   1     7  \n",
            "253      1   0     3  \n",
            "..     ...  ..   ...  \n",
            "75       2   3     7  \n",
            "194      1   2     3  \n",
            "210      2   0     7  \n",
            "145      2   2     7  \n",
            "109      2   0     3  \n",
            "\n",
            "[95 rows x 13 columns]\n",
            "(175,)\n",
            "(95,)\n"
          ]
        }
      ],
      "source": [
        "# Splitting the dataset into training and testing sets.\n",
        "x = df.iloc[:, :-1]  # Select all columns except the last one (features)\n",
        "y = df.iloc[:, -1]  # Select the last column (target variable)\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0, test_size=0.35)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-04-09T17:37:13.422778Z",
          "iopub.status.busy": "2023-04-09T17:37:13.421695Z",
          "iopub.status.idle": "2023-04-09T17:37:13.430596Z",
          "shell.execute_reply": "2023-04-09T17:37:13.430037Z",
          "shell.execute_reply.started": "2023-02-28T18:23:11.582241Z"
        },
        "id": "UfIps4BTuvo8",
        "papermill": {
          "duration": 0.040377,
          "end_time": "2023-04-09T17:37:13.430737",
          "exception": false,
          "start_time": "2023-04-09T17:37:13.390360",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "# Using standard scaler as a standardization technique.\n",
        "sc_x = StandardScaler()\n",
        "x_train = sc_x.fit_transform(x_train)\n",
        "x_test = sc_x.transform(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(175, 13)\n"
          ]
        }
      ],
      "source": [
        "print (x_train.shape) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(175, 2)\n",
            "(95, 2)\n",
            "4      0\n",
            "150    0\n",
            "61     1\n",
            "67     1\n",
            "52     0\n",
            "66     0\n",
            "26     0\n",
            "205    0\n",
            "212    0\n",
            "122    1\n",
            "Name: num, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# convert the data to categorical labels\n",
        "from keras.utils import to_categorical \n",
        "\n",
        "Y_train = to_categorical(y_train, num_classes=None) \n",
        "Y_test = to_categorical(y_test, num_classes=None) \n",
        "\n",
        "print (Y_train.shape)\n",
        "print(Y_test.shape) \n",
        "print (y_train[:10])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5FA-KiC_uvpC"
      },
      "source": [
        "# ANN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "11/11 [==============================] - 2s 51ms/step - loss: 15.8698 - accuracy: 0.5429 - val_loss: 6.7308 - val_accuracy: 0.5789\n",
            "Epoch 2/100\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 3.1588 - accuracy: 0.5029 - val_loss: 3.7609 - val_accuracy: 0.4421\n",
            "Epoch 3/100\n",
            "11/11 [==============================] - 0s 11ms/step - loss: 2.6388 - accuracy: 0.4343 - val_loss: 2.0338 - val_accuracy: 0.5789\n",
            "Epoch 4/100\n",
            "11/11 [==============================] - 0s 12ms/step - loss: 1.6213 - accuracy: 0.4343 - val_loss: 1.1864 - val_accuracy: 0.4737\n",
            "Epoch 5/100\n",
            "11/11 [==============================] - 0s 13ms/step - loss: 1.3715 - accuracy: 0.4343 - val_loss: 0.9125 - val_accuracy: 0.5263\n",
            "Epoch 6/100\n",
            "11/11 [==============================] - 0s 11ms/step - loss: 1.1266 - accuracy: 0.4743 - val_loss: 0.9515 - val_accuracy: 0.6105\n",
            "Epoch 7/100\n",
            "11/11 [==============================] - 0s 14ms/step - loss: 1.0989 - accuracy: 0.4971 - val_loss: 0.8133 - val_accuracy: 0.5789\n",
            "Epoch 8/100\n",
            "11/11 [==============================] - 0s 12ms/step - loss: 0.9825 - accuracy: 0.5371 - val_loss: 0.7568 - val_accuracy: 0.6211\n",
            "Epoch 9/100\n",
            "11/11 [==============================] - 0s 15ms/step - loss: 0.9748 - accuracy: 0.4629 - val_loss: 0.8070 - val_accuracy: 0.6947\n",
            "Epoch 10/100\n",
            "11/11 [==============================] - 0s 15ms/step - loss: 1.0394 - accuracy: 0.5943 - val_loss: 0.7983 - val_accuracy: 0.5789\n",
            "Epoch 11/100\n",
            "11/11 [==============================] - 0s 17ms/step - loss: 0.9408 - accuracy: 0.5029 - val_loss: 0.6605 - val_accuracy: 0.5895\n",
            "Epoch 12/100\n",
            "11/11 [==============================] - 0s 14ms/step - loss: 0.7579 - accuracy: 0.5657 - val_loss: 0.6283 - val_accuracy: 0.6947\n",
            "Epoch 13/100\n",
            "11/11 [==============================] - 0s 49ms/step - loss: 0.6805 - accuracy: 0.5943 - val_loss: 0.7467 - val_accuracy: 0.6000\n",
            "Epoch 14/100\n",
            "11/11 [==============================] - 0s 27ms/step - loss: 0.7844 - accuracy: 0.5886 - val_loss: 0.6360 - val_accuracy: 0.6211\n",
            "Epoch 15/100\n",
            "11/11 [==============================] - 0s 28ms/step - loss: 0.9089 - accuracy: 0.6114 - val_loss: 0.8832 - val_accuracy: 0.6526\n",
            "Epoch 16/100\n",
            "11/11 [==============================] - 0s 12ms/step - loss: 0.7494 - accuracy: 0.6057 - val_loss: 0.5531 - val_accuracy: 0.7263\n",
            "Epoch 17/100\n",
            "11/11 [==============================] - 0s 11ms/step - loss: 0.6023 - accuracy: 0.6629 - val_loss: 0.5606 - val_accuracy: 0.7474\n",
            "Epoch 18/100\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 0.5311 - accuracy: 0.7371 - val_loss: 0.5760 - val_accuracy: 0.7158\n",
            "Epoch 19/100\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 0.5401 - accuracy: 0.7314 - val_loss: 0.5861 - val_accuracy: 0.6947\n",
            "Epoch 20/100\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 0.5345 - accuracy: 0.7371 - val_loss: 0.5359 - val_accuracy: 0.7579\n",
            "Epoch 21/100\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 0.4785 - accuracy: 0.7543 - val_loss: 0.5944 - val_accuracy: 0.7053\n",
            "Epoch 22/100\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 0.5421 - accuracy: 0.7257 - val_loss: 0.5319 - val_accuracy: 0.7789\n",
            "Epoch 23/100\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 0.4621 - accuracy: 0.7886 - val_loss: 0.5606 - val_accuracy: 0.6737\n",
            "Epoch 24/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.4769 - accuracy: 0.7886 - val_loss: 0.5612 - val_accuracy: 0.6737\n",
            "Epoch 25/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4710 - accuracy: 0.7829 - val_loss: 0.5272 - val_accuracy: 0.7368\n",
            "Epoch 26/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4747 - accuracy: 0.7371 - val_loss: 0.5730 - val_accuracy: 0.6947\n",
            "Epoch 27/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4351 - accuracy: 0.7943 - val_loss: 0.7033 - val_accuracy: 0.5895\n",
            "Epoch 28/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4568 - accuracy: 0.7943 - val_loss: 0.5538 - val_accuracy: 0.7158\n",
            "Epoch 29/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.4325 - accuracy: 0.8457 - val_loss: 0.5350 - val_accuracy: 0.7158\n",
            "Epoch 30/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4189 - accuracy: 0.8229 - val_loss: 0.5198 - val_accuracy: 0.7263\n",
            "Epoch 31/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4445 - accuracy: 0.8057 - val_loss: 0.5794 - val_accuracy: 0.6842\n",
            "Epoch 32/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4109 - accuracy: 0.8457 - val_loss: 0.5306 - val_accuracy: 0.7158\n",
            "Epoch 33/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4460 - accuracy: 0.7886 - val_loss: 0.5509 - val_accuracy: 0.7053\n",
            "Epoch 34/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.4735 - accuracy: 0.7714 - val_loss: 0.5143 - val_accuracy: 0.7368\n",
            "Epoch 35/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.4433 - accuracy: 0.8114 - val_loss: 0.5559 - val_accuracy: 0.7053\n",
            "Epoch 36/100\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 0.3946 - accuracy: 0.8457 - val_loss: 0.4995 - val_accuracy: 0.7684\n",
            "Epoch 37/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4017 - accuracy: 0.8343 - val_loss: 0.5051 - val_accuracy: 0.7684\n",
            "Epoch 38/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.5046 - accuracy: 0.7314 - val_loss: 0.5339 - val_accuracy: 0.7158\n",
            "Epoch 39/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.5390 - accuracy: 0.7429 - val_loss: 0.5206 - val_accuracy: 0.7474\n",
            "Epoch 40/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.5149 - accuracy: 0.7600 - val_loss: 0.5559 - val_accuracy: 0.6947\n",
            "Epoch 41/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3911 - accuracy: 0.8514 - val_loss: 0.4980 - val_accuracy: 0.7579\n",
            "Epoch 42/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3799 - accuracy: 0.8571 - val_loss: 0.4965 - val_accuracy: 0.7684\n",
            "Epoch 43/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3928 - accuracy: 0.8229 - val_loss: 0.5219 - val_accuracy: 0.7684\n",
            "Epoch 44/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.4047 - accuracy: 0.8057 - val_loss: 0.4934 - val_accuracy: 0.7368\n",
            "Epoch 45/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3843 - accuracy: 0.8457 - val_loss: 0.4952 - val_accuracy: 0.7263\n",
            "Epoch 46/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.3838 - accuracy: 0.8686 - val_loss: 0.4882 - val_accuracy: 0.7684\n",
            "Epoch 47/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3678 - accuracy: 0.8686 - val_loss: 0.5422 - val_accuracy: 0.6947\n",
            "Epoch 48/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3600 - accuracy: 0.8571 - val_loss: 0.4937 - val_accuracy: 0.7789\n",
            "Epoch 49/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3774 - accuracy: 0.8343 - val_loss: 0.4918 - val_accuracy: 0.7158\n",
            "Epoch 50/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3710 - accuracy: 0.8571 - val_loss: 0.4954 - val_accuracy: 0.7368\n",
            "Epoch 51/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3588 - accuracy: 0.8629 - val_loss: 0.5130 - val_accuracy: 0.7895\n",
            "Epoch 52/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3789 - accuracy: 0.8286 - val_loss: 0.4910 - val_accuracy: 0.7684\n",
            "Epoch 53/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3824 - accuracy: 0.8286 - val_loss: 0.4853 - val_accuracy: 0.7789\n",
            "Epoch 54/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3610 - accuracy: 0.8686 - val_loss: 0.5169 - val_accuracy: 0.7158\n",
            "Epoch 55/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3700 - accuracy: 0.8743 - val_loss: 0.6531 - val_accuracy: 0.6421\n",
            "Epoch 56/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3730 - accuracy: 0.8400 - val_loss: 0.7776 - val_accuracy: 0.5684\n",
            "Epoch 57/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3957 - accuracy: 0.8286 - val_loss: 0.5455 - val_accuracy: 0.6947\n",
            "Epoch 58/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3476 - accuracy: 0.8571 - val_loss: 0.4909 - val_accuracy: 0.8105\n",
            "Epoch 59/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3566 - accuracy: 0.8629 - val_loss: 0.5385 - val_accuracy: 0.7053\n",
            "Epoch 60/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3468 - accuracy: 0.8743 - val_loss: 0.4875 - val_accuracy: 0.7579\n",
            "Epoch 61/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3448 - accuracy: 0.8629 - val_loss: 0.5334 - val_accuracy: 0.7789\n",
            "Epoch 62/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.3958 - accuracy: 0.8400 - val_loss: 0.5038 - val_accuracy: 0.7895\n",
            "Epoch 63/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.3840 - accuracy: 0.8171 - val_loss: 0.4948 - val_accuracy: 0.7474\n",
            "Epoch 64/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.4165 - accuracy: 0.8171 - val_loss: 0.5416 - val_accuracy: 0.7158\n",
            "Epoch 65/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3790 - accuracy: 0.8400 - val_loss: 0.4876 - val_accuracy: 0.7474\n",
            "Epoch 66/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4122 - accuracy: 0.8171 - val_loss: 0.6011 - val_accuracy: 0.6632\n",
            "Epoch 67/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3743 - accuracy: 0.8571 - val_loss: 0.6074 - val_accuracy: 0.6632\n",
            "Epoch 68/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3547 - accuracy: 0.8743 - val_loss: 0.5989 - val_accuracy: 0.6632\n",
            "Epoch 69/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3622 - accuracy: 0.8514 - val_loss: 0.5839 - val_accuracy: 0.6737\n",
            "Epoch 70/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3304 - accuracy: 0.8743 - val_loss: 0.4895 - val_accuracy: 0.7684\n",
            "Epoch 71/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3404 - accuracy: 0.8686 - val_loss: 0.5075 - val_accuracy: 0.7474\n",
            "Epoch 72/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3327 - accuracy: 0.8743 - val_loss: 0.5158 - val_accuracy: 0.7474\n",
            "Epoch 73/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3370 - accuracy: 0.8743 - val_loss: 0.5179 - val_accuracy: 0.7579\n",
            "Epoch 74/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.3448 - accuracy: 0.8800 - val_loss: 0.5967 - val_accuracy: 0.6842\n",
            "Epoch 75/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3592 - accuracy: 0.8400 - val_loss: 0.5696 - val_accuracy: 0.6947\n",
            "Epoch 76/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.4043 - accuracy: 0.8229 - val_loss: 0.4872 - val_accuracy: 0.7895\n",
            "Epoch 77/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4391 - accuracy: 0.8057 - val_loss: 0.5566 - val_accuracy: 0.6947\n",
            "Epoch 78/100\n",
            "11/11 [==============================] - 0s 10ms/step - loss: 0.3397 - accuracy: 0.8914 - val_loss: 0.5012 - val_accuracy: 0.7684\n",
            "Epoch 79/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3418 - accuracy: 0.8743 - val_loss: 0.4918 - val_accuracy: 0.7895\n",
            "Epoch 80/100\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 0.3479 - accuracy: 0.8686 - val_loss: 0.4979 - val_accuracy: 0.7474\n",
            "Epoch 81/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.3428 - accuracy: 0.8800 - val_loss: 0.5617 - val_accuracy: 0.7158\n",
            "Epoch 82/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3315 - accuracy: 0.8514 - val_loss: 0.5519 - val_accuracy: 0.7474\n",
            "Epoch 83/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3544 - accuracy: 0.8571 - val_loss: 0.4988 - val_accuracy: 0.8211\n",
            "Epoch 84/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3367 - accuracy: 0.8514 - val_loss: 0.5199 - val_accuracy: 0.7474\n",
            "Epoch 85/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.4308 - accuracy: 0.7943 - val_loss: 0.5556 - val_accuracy: 0.7158\n",
            "Epoch 86/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3548 - accuracy: 0.8343 - val_loss: 0.6168 - val_accuracy: 0.6526\n",
            "Epoch 87/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3768 - accuracy: 0.8514 - val_loss: 0.8575 - val_accuracy: 0.5789\n",
            "Epoch 88/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3501 - accuracy: 0.8571 - val_loss: 0.5600 - val_accuracy: 0.7158\n",
            "Epoch 89/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.3885 - accuracy: 0.8571 - val_loss: 0.4920 - val_accuracy: 0.7684\n",
            "Epoch 90/100\n",
            "11/11 [==============================] - 0s 9ms/step - loss: 0.3136 - accuracy: 0.8686 - val_loss: 0.5087 - val_accuracy: 0.7684\n",
            "Epoch 91/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.3167 - accuracy: 0.8857 - val_loss: 0.4959 - val_accuracy: 0.7789\n",
            "Epoch 92/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3520 - accuracy: 0.8343 - val_loss: 0.6701 - val_accuracy: 0.6947\n",
            "Epoch 93/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3805 - accuracy: 0.8171 - val_loss: 0.4896 - val_accuracy: 0.8211\n",
            "Epoch 94/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.3561 - accuracy: 0.8743 - val_loss: 0.5791 - val_accuracy: 0.7684\n",
            "Epoch 95/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3876 - accuracy: 0.8229 - val_loss: 0.6448 - val_accuracy: 0.7053\n",
            "Epoch 96/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.4866 - accuracy: 0.7486 - val_loss: 0.5582 - val_accuracy: 0.7789\n",
            "Epoch 97/100\n",
            "11/11 [==============================] - 0s 17ms/step - loss: 0.3780 - accuracy: 0.8457 - val_loss: 0.5040 - val_accuracy: 0.7579\n",
            "Epoch 98/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.3340 - accuracy: 0.8743 - val_loss: 0.5097 - val_accuracy: 0.7684\n",
            "Epoch 99/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3104 - accuracy: 0.8857 - val_loss: 0.5179 - val_accuracy: 0.7474\n",
            "Epoch 100/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3258 - accuracy: 0.8743 - val_loss: 0.5236 - val_accuracy: 0.7368\n",
            "3/3 [==============================] - 0s 3ms/step - loss: 0.5236 - accuracy: 0.7368\n",
            "Test loss: 0.5236097574234009\n",
            "Test accuracy: 0.7368420958518982\n",
            "3/3 [==============================] - 0s 2ms/step\n",
            "[[0.60931325]\n",
            " [0.12669232]\n",
            " [0.8689749 ]\n",
            " [0.97164756]\n",
            " [0.365477  ]\n",
            " [0.55090296]\n",
            " [0.10721016]\n",
            " [0.14397405]\n",
            " [0.4605397 ]\n",
            " [0.70304537]\n",
            " [0.4356303 ]\n",
            " [0.4520713 ]\n",
            " [0.7016566 ]\n",
            " [0.9632288 ]\n",
            " [0.94089305]\n",
            " [0.14194296]\n",
            " [0.05416435]\n",
            " [0.92066365]\n",
            " [0.8214063 ]\n",
            " [0.40351644]\n",
            " [0.99832815]\n",
            " [0.04996199]\n",
            " [0.9591794 ]\n",
            " [0.84659266]\n",
            " [0.6117215 ]\n",
            " [0.21670943]\n",
            " [0.23449709]\n",
            " [0.1386278 ]\n",
            " [0.21816589]\n",
            " [0.86088884]\n",
            " [0.39403212]\n",
            " [0.6636856 ]\n",
            " [0.9947312 ]\n",
            " [0.0349075 ]\n",
            " [0.84649473]\n",
            " [0.76119006]\n",
            " [0.9724926 ]\n",
            " [0.88429946]\n",
            " [0.02761178]\n",
            " [0.3533886 ]\n",
            " [0.6995954 ]\n",
            " [0.18116634]\n",
            " [0.18305036]\n",
            " [0.630071  ]\n",
            " [0.05516016]\n",
            " [0.09821367]\n",
            " [0.968632  ]\n",
            " [0.44208845]\n",
            " [0.9920888 ]\n",
            " [0.5933534 ]\n",
            " [0.9446455 ]\n",
            " [0.7541366 ]\n",
            " [0.3063841 ]\n",
            " [0.28015235]\n",
            " [0.70138866]\n",
            " [0.51200676]\n",
            " [0.5074488 ]\n",
            " [0.9370844 ]\n",
            " [0.02648714]\n",
            " [0.86997813]\n",
            " [0.02023863]\n",
            " [0.06102118]\n",
            " [0.09170379]\n",
            " [0.10753565]\n",
            " [0.40009764]\n",
            " [0.8882611 ]\n",
            " [0.7588146 ]\n",
            " [0.9817117 ]\n",
            " [0.5446802 ]\n",
            " [0.04469286]\n",
            " [0.6365478 ]\n",
            " [0.59899336]\n",
            " [0.856606  ]\n",
            " [0.29727557]\n",
            " [0.86488014]\n",
            " [0.30117738]\n",
            " [0.77939546]\n",
            " [0.03007234]\n",
            " [0.17398937]\n",
            " [0.98701787]\n",
            " [0.92455506]\n",
            " [0.7297587 ]\n",
            " [0.7685929 ]\n",
            " [0.05081685]\n",
            " [0.94620705]\n",
            " [0.03665422]\n",
            " [0.9857918 ]\n",
            " [0.45311943]\n",
            " [0.98186874]\n",
            " [0.38706407]\n",
            " [0.9806147 ]\n",
            " [0.29264575]\n",
            " [0.1870012 ]\n",
            " [0.98861134]\n",
            " [0.07979032]]\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Dense(32, activation='relu', input_shape=(13,)),\n",
        "    keras.layers.Dense(16, activation='relu'),\n",
        "    keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "\n",
        "# Train the model\n",
        "model.fit(x_train, y_train, epochs=100, batch_size=16, validation_data=(x_test, y_test))\n",
        "\n",
        "model.save('heart_disease_model.hdf5')\n",
        "\n",
        "# Evaluate the model on the test data\n",
        "loss, accuracy = model.evaluate(x_test, y_test)\n",
        "print(f\"Test loss: {loss}\")\n",
        "print(f\"Test accuracy: {accuracy}\")\n",
        "\n",
        "# Make predictions\n",
        "predictions = model.predict(x_test)\n",
        "print(predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 3s 3s/step\n",
            "[[0.32416555]]\n"
          ]
        }
      ],
      "source": [
        "# user_input = np.array([[64,1,4,128,263,0,0,105,1,0.2,2,1,7]]) #have\n",
        "# user_input = np.array([[58,1,2,140,283,1,0,162,1,1.0,2,0,2]]) #have acc apt\n",
        "# user_input = np.array([[52,1,2,160,280,1,1,140,1,2.0,2,3,3]]) #have acc ap1\n",
        "user_input = np.array([[35, 0, 0, 120, 200, 0, 0, 175,0, 0.0, 0, 0, 3]]) #don't have\n",
        "sc_x = StandardScaler()\n",
        "sc_x.fit(x_train)\n",
        "user_input_scaled = sc_x.transform(user_input)\n",
        "prediction = model.predict(user_input_scaled)\n",
        "print(prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8flBh10lUlWf",
        "outputId": "4ef03545-8c40-4455-8ce2-c50df4a834d3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: numpy<2,>=1.19.3 in d:\\anaconda3\\lib\\site-packages (from streamlit) (1.24.3)\n",
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          ]
        }
      ],
      "source": [
        "!pip install streamlit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HF1tQrcyUf8n",
        "outputId": "62ecc460-43f0-407a-9b6f-419633108e61"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Writing app.py\n"
          ]
        }
      ],
      "source": [
        "%%writefile app.py\n",
        "import streamlit as st\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Load the pre-trained model\n",
        "model = keras.models.load_model('heart_disease_model.hdf5')\n",
        "df = pd.read_csv(\"Heart_Disease_Prediction.csv\")\n",
        "\n",
        "x = df.iloc[:, :-1]  # Select all columns except the last one (features)\n",
        "y = df.iloc[:, -1]  # Select the last column (target variable)\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0, test_size=0.35)\n",
        "\n",
        "# Function to preprocess user input and make predictions\n",
        "def predict_heart_disease(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal):\n",
        "    input_data = np.array([[age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal]])\n",
        "    sc_x = StandardScaler()\n",
        "    sc_x.fit(x_train)\n",
        "    user_input_scaled = sc_x.transform(input_data)\n",
        "    prediction = model.predict(user_input_scaled)\n",
        "    return prediction\n",
        "\n",
        "# Create a Streamlit app\n",
        "st.title(\"Heart Disease Predictor\")\n",
        "\n",
        "# Input fields for user\n",
        "age = st.slider(\"Age\", min_value=1, max_value=100, value=30)\n",
        "sex = st.selectbox(\"Sex(male=0, femlae=1)\", [0, 1])\n",
        "cp = st.selectbox(\"Chest Pain Type (CP)\", [0, 1, 2, 3])\n",
        "trestbps = st.slider(\"Resting Blood Pressure (trestbps)\", min_value=90, max_value=200, value=120)\n",
        "chol = st.slider(\"Cholesterol (Chol)\", min_value=100, max_value=600, value=200)\n",
        "fbs = st.selectbox(\"Fasting Blood Sugar (FBS)\", [0, 1])\n",
        "restecg = st.selectbox(\"Resting Electrocardiographic Results (restECG)\", [0, 1, 2])\n",
        "thalach = st.slider(\"Maximum Heart Rate Achieved (thalach)\", min_value=70, max_value=420, value=150)\n",
        "exang = st.selectbox(\"Exercise-Induced Angina (exang)\", [0, 1])\n",
        "oldpeak = st.slider(\"ST Depression (oldpeak)\", min_value=0.0, max_value=6.2, value=1.0)\n",
        "slope = st.selectbox(\"Slope of the Peak Exercise ST Segment (slope)\", [0, 1, 2])\n",
        "ca = st.slider(\"Number of Major Vessels Colored by Fluoroscopy (Ca)\", min_value= 0, max_value= 3, value=0)\n",
        "thal = st.selectbox(\"Thallium Stress Test (thal)\", [0, 1, 2, 3, 4, 5, 6, 7])\n",
        "\n",
        "if st.button(\"Predict\"):\n",
        "    prediction = predict_heart_disease(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal)\n",
        "    if prediction >= 0.5:\n",
        "        st.write(\"Based on the input data, it is likely that you have heart disease.\")\n",
        "    else:\n",
        "        st.write(\"Based on the input data, it is likely that you do not have heart disease.\")\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.0"
    },
    "papermill": {
      "duration": 8.362839,
      "end_time": "2023-04-09T17:37:15.262269",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2023-04-09T17:37:06.899430",
      "version": "2.1.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}