4 AIML Hons: Medical Prognosis based on Patient Data.ipynb

4-aiml-hons-medical-prognosis-based-on-patient-data.ipynb

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        "<a href=\"https://colab.research.google.com/gist/Yugsolanki/6bcfa666a6146768598caa8ccbdb829c/4-aiml-hons-medical-prognosis-based-on-patient-data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "Experiment 4<br>\n",
        "Yug Solanki<br>\n",
        "BE-4, 58<br>\n",
        "21UF16526CM057<br>\n",
        "[Colab Link🔗](https://colab.research.google.com/drive/1Eq-GNoPpLZpJvnbAle1cNPYOHO9t-MZR?usp=sharing)"
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      "metadata": {
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    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d redwankarimsony/heart-disease-data"
      ],
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        "id": "diMGr0XeIe-B",
        "outputId": "68babbb3-efa6-4b20-a87f-735c3a75ad62",
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      "outputs": [
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          "text": [
            "Dataset URL: https://www.kaggle.com/datasets/redwankarimsony/heart-disease-data\n",
            "License(s): copyright-authors\n",
            "Downloading heart-disease-data.zip to /content\n",
            "  0% 0.00/12.4k [00:00<?, ?B/s]\n",
            "100% 12.4k/12.4k [00:00<00:00, 6.67MB/s]\n"
          ]
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    },
    {
      "cell_type": "code",
      "source": [
        "!unzip heart-disease-data.zip"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  heart-disease-data.zip\n",
            "  inflating: heart_disease_uci.csv   \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Libraries"
      ],
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    {
      "cell_type": "code",
      "source": [
        "# 1. to handle the data\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy import stats\n",
        "\n",
        "# to visualize the data\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "\n",
        "\n",
        "# To preprocess the data\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder,OneHotEncoder\n",
        "from sklearn.impute import SimpleImputer, KNNImputer\n",
        "# import iterative imputer\n",
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import IterativeImputer\n",
        "\n",
        "# machine learning\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
        "#for classification tasks\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, RandomForestRegressor\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "# pipeline\n",
        "from sklearn.pipeline import Pipeline\n",
        "# metrics\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, mean_absolute_error,mean_squared_error,r2_score\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
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      "outputs": []
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      "source": [
        "# Loading Dataset"
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      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"heart_disease_uci.csv\")\n",
        "df.head()"
      ],
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              "   id  age     sex    dataset               cp  trestbps   chol    fbs  \\\n",
              "0   1   63    Male  Cleveland   typical angina     145.0  233.0   True   \n",
              "1   2   67    Male  Cleveland     asymptomatic     160.0  286.0  False   \n",
              "2   3   67    Male  Cleveland     asymptomatic     120.0  229.0  False   \n",
              "3   4   37    Male  Cleveland      non-anginal     130.0  250.0  False   \n",
              "4   5   41  Female  Cleveland  atypical angina     130.0  204.0  False   \n",
              "\n",
              "          restecg  thalch  exang  oldpeak        slope   ca  \\\n",
              "0  lv hypertrophy   150.0  False      2.3  downsloping  0.0   \n",
              "1  lv hypertrophy   108.0   True      1.5         flat  3.0   \n",
              "2  lv hypertrophy   129.0   True      2.6         flat  2.0   \n",
              "3          normal   187.0  False      3.5  downsloping  0.0   \n",
              "4  lv hypertrophy   172.0  False      1.4    upsloping  0.0   \n",
              "\n",
              "                thal  num  \n",
              "0       fixed defect    0  \n",
              "1             normal    2  \n",
              "2  reversable defect    1  \n",
              "3             normal    0  \n",
              "4             normal    0  "
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2543771c-1790-483b-8b1e-5ce113e48783 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 920,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 265,\n        \"min\": 1,\n        \"max\": 920,\n        \"num_unique_values\": 920,\n        \"samples\": [\n          320,\n          378,\n          539\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 28,\n        \"max\": 77,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          64,\n          74,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dataset\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Hungary\",\n          \"VA Long Beach\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cp\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"asymptomatic\",\n          \"atypical angina\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trestbps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.066069518587458,\n        \"min\": 0.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          145.0,\n          172.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 110.78081035323044,\n        \"min\": 0.0,\n        \"max\": 603.0,\n        \"num_unique_values\": 217,\n        \"samples\": [\n          384.0,\n          333.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fbs\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"restecg\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"lv hypertrophy\",\n          \"normal\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thalch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.926276492797612,\n        \"min\": 60.0,\n        \"max\": 202.0,\n        \"num_unique_values\": 119,\n        \"samples\": [\n          185.0,\n          134.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"exang\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oldpeak\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0912262483465265,\n        \"min\": -2.6,\n        \"max\": 6.2,\n        \"num_unique_values\": 53,\n        \"samples\": [\n          2.4,\n          -1.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"slope\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"downsloping\",\n          \"flat\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ca\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9356530125599879,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thal\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"fixed defect\",\n          \"normal\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset Overview"
      ],
      "metadata": {
        "id": "sZMH4U5nFAA7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:51.929349Z",
          "iopub.execute_input": "2024-02-16T04:55:51.930113Z",
          "iopub.status.idle": "2024-02-16T04:55:51.947385Z",
          "shell.execute_reply.started": "2024-02-16T04:55:51.930063Z",
          "shell.execute_reply": "2024-02-16T04:55:51.946207Z"
        },
        "trusted": true,
        "id": "TG2MGi1PFAA7",
        "outputId": "b841affa-4f05-4efb-e98d-604814b9ff78",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 920 entries, 0 to 919\n",
            "Data columns (total 16 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   id        920 non-null    int64  \n",
            " 1   age       920 non-null    int64  \n",
            " 2   sex       920 non-null    object \n",
            " 3   dataset   920 non-null    object \n",
            " 4   cp        920 non-null    object \n",
            " 5   trestbps  861 non-null    float64\n",
            " 6   chol      890 non-null    float64\n",
            " 7   fbs       830 non-null    object \n",
            " 8   restecg   918 non-null    object \n",
            " 9   thalch    865 non-null    float64\n",
            " 10  exang     865 non-null    object \n",
            " 11  oldpeak   858 non-null    float64\n",
            " 12  slope     611 non-null    object \n",
            " 13  ca        309 non-null    float64\n",
            " 14  thal      434 non-null    object \n",
            " 15  num       920 non-null    int64  \n",
            "dtypes: float64(5), int64(3), object(8)\n",
            "memory usage: 115.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Separate Columns with Dtype\n"
      ],
      "metadata": {
        "id": "qcXKzMwJFAA8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# find only categorical columns\n",
        "categorical_cols = df.select_dtypes(include='object').columns.tolist()\n",
        "# find only numerical columns\n",
        "numeric_cols = df.select_dtypes(exclude='object').columns.tolist()\n",
        "\n",
        "print(f'Categorical Columns: {categorical_cols}')\n",
        "print(f'Numerical Columns: {numeric_cols}')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:51.950244Z",
          "iopub.execute_input": "2024-02-16T04:55:51.950607Z",
          "iopub.status.idle": "2024-02-16T04:55:51.959367Z",
          "shell.execute_reply.started": "2024-02-16T04:55:51.950577Z",
          "shell.execute_reply": "2024-02-16T04:55:51.958195Z"
        },
        "trusted": true,
        "id": "KpaeIgPsFAA9",
        "outputId": "f77df380-0570-465c-e203-156cbbed6d18",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Categorical Columns: ['sex', 'dataset', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'thal']\n",
            "Numerical Columns: ['id', 'age', 'trestbps', 'chol', 'thalch', 'oldpeak', 'ca', 'num']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_columns = ['thal', 'ca', 'slope', 'exang', 'restecg','fbs', 'cp', 'sex', 'num']\n",
        "bool_cols = ['fbs', 'exang']\n",
        "numerical_columns = ['oldpeak', 'thalch', 'chol', 'trestbps', 'age']"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:51.961341Z",
          "iopub.execute_input": "2024-02-16T04:55:51.961973Z",
          "iopub.status.idle": "2024-02-16T04:55:51.970456Z",
          "shell.execute_reply.started": "2024-02-16T04:55:51.961933Z",
          "shell.execute_reply": "2024-02-16T04:55:51.968919Z"
        },
        "trusted": true,
        "id": "_Wz0WYjWFAA9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Explore All Columns"
      ],
      "metadata": {
        "id": "svjm6i6AFAA-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ID Column\n"
      ],
      "metadata": {
        "id": "2moA3PdVFAA_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# drop id column\n",
        "df.drop(['id'],axis=1,inplace=True)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:51.974065Z",
          "iopub.execute_input": "2024-02-16T04:55:51.974499Z",
          "iopub.status.idle": "2024-02-16T04:55:51.983072Z",
          "shell.execute_reply.started": "2024-02-16T04:55:51.974467Z",
          "shell.execute_reply": "2024-02-16T04:55:51.981426Z"
        },
        "trusted": true,
        "id": "c_pBN61vFAA_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Age Column"
      ],
      "metadata": {
        "id": "JL_kEDQtFAA_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check minimum age\n",
        "print(\"minimun age is :\",df['age'].min())\n",
        "# check maximum\n",
        "print(\"maximun age is :\",df['age'].max())\n",
        "# check average of age\n",
        "print(\"average of age is :\",df['age'].mean().round(2),\"%\")\n",
        "# plot the mean of age column using sns\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.histplot(df['age'], kde=True)\n",
        "plt.axvline(df['age'].mean(), color='red')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:51.984735Z",
          "iopub.execute_input": "2024-02-16T04:55:51.986207Z",
          "iopub.status.idle": "2024-02-16T04:55:52.391Z",
          "shell.execute_reply.started": "2024-02-16T04:55:51.98615Z",
          "shell.execute_reply": "2024-02-16T04:55:52.389543Z"
        },
        "trusted": true,
        "id": "d-TvXt4uFABA",
        "outputId": "9931e409-2d73-4709-e717-193cb149d7fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "minimun age is : 28\n",
            "maximun age is : 77\n",
            "average of age is : 53.51 %\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7c4de0242c50>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Sex Column"
      ],
      "metadata": {
        "id": "DR6BlOlCFABA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"count of male & female\",df['sex'].value_counts())\n",
        "male_count = 563\n",
        "female_count = 182\n",
        "total_count = male_count + female_count\n",
        "\n",
        "# calculate percentages\n",
        "male_percentage = (male_count / total_count) * 100\n",
        "female_percentage = (female_count / total_count) * 100\n",
        "\n",
        "# display the results\n",
        "print(f\"Male percentage in the data: {male_percentage:.2f}%\")\n",
        "print(f\"Female Percentage in the data: {female_percentage:.2f}%\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:52.392796Z",
          "iopub.execute_input": "2024-02-16T04:55:52.393391Z",
          "iopub.status.idle": "2024-02-16T04:55:52.403526Z",
          "shell.execute_reply.started": "2024-02-16T04:55:52.39335Z",
          "shell.execute_reply": "2024-02-16T04:55:52.402253Z"
        },
        "trusted": true,
        "id": "M-jWKLasFABB",
        "outputId": "584a6169-f7f3-48fb-e4c4-65642926e1cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count of male & female sex\n",
            "Male      726\n",
            "Female    194\n",
            "Name: count, dtype: int64\n",
            "Male percentage in the data: 75.57%\n",
            "Female Percentage in the data: 24.43%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Column"
      ],
      "metadata": {
        "id": "V890Pa0HFABB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Data collect from :\",df['dataset'].value_counts())\n",
        "# print the values count of dataset column grouped by sex\n",
        "print(df.groupby('sex')['dataset'].value_counts())\n",
        "# barplot of dataset  base on sex\n",
        "fig = px.bar(df, x='dataset', color='sex')\n",
        "fig.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:52.408699Z",
          "iopub.execute_input": "2024-02-16T04:55:52.409402Z",
          "iopub.status.idle": "2024-02-16T04:55:52.50834Z",
          "shell.execute_reply.started": "2024-02-16T04:55:52.409359Z",
          "shell.execute_reply": "2024-02-16T04:55:52.507118Z"
        },
        "trusted": true,
        "id": "RsMDInguFABB",
        "outputId": "f7d118e4-8f36-4163-f672-8eaff225c610",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 840
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data collect from : dataset\n",
            "Cleveland        304\n",
            "Hungary          293\n",
            "VA Long Beach    200\n",
            "Switzerland      123\n",
            "Name: count, dtype: int64\n",
            "sex     dataset      \n",
            "Female  Cleveland         97\n",
            "        Hungary           81\n",
            "        Switzerland       10\n",
            "        VA Long Beach      6\n",
            "Male    Hungary          212\n",
            "        Cleveland        207\n",
            "        VA Long Beach    194\n",
            "        Switzerland      113\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### CP Column"
      ],
      "metadata": {
        "id": "dObrPtvdFABB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "# value count of cp column\n",
        "print(\"value_counts of :\",df['cp'].value_counts())\n",
        "# count plot of cp column by sex column\n",
        "fig = px.histogram(df, x='cp', color='sex', barmode='group')\n",
        "fig.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:52.510329Z",
          "iopub.execute_input": "2024-02-16T04:55:52.511109Z",
          "iopub.status.idle": "2024-02-16T04:55:52.608666Z",
          "shell.execute_reply.started": "2024-02-16T04:55:52.511051Z",
          "shell.execute_reply": "2024-02-16T04:55:52.607404Z"
        },
        "trusted": true,
        "id": "adZDuuFNFABB",
        "outputId": "1c60bfa1-7016-4b3c-8a15-5fea1330e126",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 684
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "value_counts of : cp\n",
            "asymptomatic       496\n",
            "non-anginal        204\n",
            "atypical angina    174\n",
            "typical angina      46\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
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        "# count plot of cp column by dataset columnplt\n",
        "fig = px.histogram(df, x='cp', color='dataset', barmode='group')\n",
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        "plt.figure(figsize=(10,6))\n",
        "# count plot of age column\n",
        "fig = px.histogram(data_frame=df, x='age', color='cp')\n",
        "fig.show()"
      ],
      "metadata": {
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          "iopub.status.busy": "2024-02-16T04:55:52.709624Z",
          "iopub.execute_input": "2024-02-16T04:55:52.710033Z",
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          "shell.execute_reply": "2024-02-16T04:55:52.814671Z"
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      "execution_count": null,
      "outputs": [
        {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
            ]
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    {
      "cell_type": "markdown",
      "source": [
        "### Trestbps Column"
      ],
      "metadata": {
        "id": "pxwjRsewFABC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['trestbps'].describe())\n",
        "# create a histplot of trestbps column\n",
        "sns.histplot(df['trestbps'], kde=True)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:52.817684Z",
          "iopub.execute_input": "2024-02-16T04:55:52.818103Z",
          "iopub.status.idle": "2024-02-16T04:55:53.271512Z",
          "shell.execute_reply.started": "2024-02-16T04:55:52.818068Z",
          "shell.execute_reply": "2024-02-16T04:55:53.270237Z"
        },
        "trusted": true,
        "id": "tJiWPsdHFABC",
        "outputId": "1d11b097-e3d0-4fc5-e0fc-341ef0c7c98f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    861.000000\n",
            "mean     132.132404\n",
            "std       19.066070\n",
            "min        0.000000\n",
            "25%      120.000000\n",
            "50%      130.000000\n",
            "75%      140.000000\n",
            "max      200.000000\n",
            "Name: trestbps, dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='trestbps', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Chol Column"
      ],
      "metadata": {
        "id": "7WiXNE-RFABD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['chol'].describe())\n",
        "\n",
        "sns.histplot(df['chol'], kde=True)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:53.273543Z",
          "iopub.execute_input": "2024-02-16T04:55:53.274344Z",
          "iopub.status.idle": "2024-02-16T04:55:53.684844Z",
          "shell.execute_reply.started": "2024-02-16T04:55:53.274295Z",
          "shell.execute_reply": "2024-02-16T04:55:53.683403Z"
        },
        "trusted": true,
        "id": "GyxBd5MOFABD",
        "outputId": "c56db40e-b337-4916-8d47-e959a02f72f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    890.000000\n",
            "mean     199.130337\n",
            "std      110.780810\n",
            "min        0.000000\n",
            "25%      175.000000\n",
            "50%      223.000000\n",
            "75%      268.000000\n",
            "max      603.000000\n",
            "Name: chol, dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='chol', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fbs Column"
      ],
      "metadata": {
        "id": "Do-wTJ7_FABD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['fbs'].value_counts()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:53.686594Z",
          "iopub.execute_input": "2024-02-16T04:55:53.687142Z",
          "iopub.status.idle": "2024-02-16T04:55:53.69796Z",
          "shell.execute_reply.started": "2024-02-16T04:55:53.687101Z",
          "shell.execute_reply": "2024-02-16T04:55:53.696504Z"
        },
        "trusted": true,
        "id": "_thBrlLHFABD",
        "outputId": "9d760a03-ee8a-4f6f-f5f5-9fef24470211",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fbs\n",
              "False    692\n",
              "True     138\n",
              "Name: count, dtype: int64"
            ],
            "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fbs</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>False</th>\n",
              "      <td>692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>True</th>\n",
              "      <td>138</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.histogram(df, x='fbs', color='sex', barmode='group')\n",
        "fig.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:53.700081Z",
          "iopub.execute_input": "2024-02-16T04:55:53.700881Z",
          "iopub.status.idle": "2024-02-16T04:55:53.792518Z",
          "shell.execute_reply.started": "2024-02-16T04:55:53.700814Z",
          "shell.execute_reply": "2024-02-16T04:55:53.79061Z"
        },
        "trusted": true,
        "id": "Zc5_YpqPFABD",
        "outputId": "178f52ed-d4e0-4bd0-d242-920bfe1370f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Restecg Column"
      ],
      "metadata": {
        "id": "fpugdE3SFABD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check unique values\n",
        "df['restecg'].unique()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:53.794333Z",
          "iopub.execute_input": "2024-02-16T04:55:53.794789Z",
          "iopub.status.idle": "2024-02-16T04:55:53.803418Z",
          "shell.execute_reply.started": "2024-02-16T04:55:53.794749Z",
          "shell.execute_reply": "2024-02-16T04:55:53.802086Z"
        },
        "trusted": true,
        "id": "02ZDnD-hFABE",
        "outputId": "3dd5d248-6c0e-45d9-826d-24831598a05f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['lv hypertrophy', 'normal', 'st-t abnormality', nan], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['restecg'][df['restecg'] == 0] = 'normal'\n",
        "df['restecg'][df['restecg'] == 1] = 'ST-T wave abnormality'\n",
        "df['restecg'][df['restecg'] == 2] = 'left ventricular hypertrophy'"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:53.805398Z",
          "iopub.execute_input": "2024-02-16T04:55:53.805995Z",
          "iopub.status.idle": "2024-02-16T04:55:53.822766Z",
          "shell.execute_reply.started": "2024-02-16T04:55:53.805947Z",
          "shell.execute_reply": "2024-02-16T04:55:53.821305Z"
        },
        "trusted": true,
        "id": "IkwTP7TcFABE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "restecg_counts = df['restecg'].value_counts()\n",
        "print(restecg_counts)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:53.824421Z",
          "iopub.execute_input": "2024-02-16T04:55:53.825147Z",
          "iopub.status.idle": "2024-02-16T04:55:53.835184Z",
          "shell.execute_reply.started": "2024-02-16T04:55:53.82511Z",
          "shell.execute_reply": "2024-02-16T04:55:53.833776Z"
        },
        "trusted": true,
        "id": "VgghGgXWFABE",
        "outputId": "9c55264e-3fb7-4f4d-e257-25bec14dc930",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "restecg\n",
            "normal              551\n",
            "lv hypertrophy      188\n",
            "st-t abnormality    179\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# remove row where are found Nan values\n",
        "df.dropna(subset=['restecg'], inplace=True)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:53.836965Z",
          "iopub.execute_input": "2024-02-16T04:55:53.837496Z",
          "iopub.status.idle": "2024-02-16T04:55:53.848701Z",
          "shell.execute_reply.started": "2024-02-16T04:55:53.837448Z",
          "shell.execute_reply": "2024-02-16T04:55:53.847451Z"
        },
        "trusted": true,
        "id": "1ShAx_UvFABF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Thalch Column"
      ],
      "metadata": {
        "id": "MBBE63piFABF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['thalch'].describe()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:53.850545Z",
          "iopub.execute_input": "2024-02-16T04:55:53.851013Z",
          "iopub.status.idle": "2024-02-16T04:55:53.867995Z",
          "shell.execute_reply.started": "2024-02-16T04:55:53.850972Z",
          "shell.execute_reply": "2024-02-16T04:55:53.866561Z"
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        "trusted": true,
        "id": "14eUvaB4FABF",
        "outputId": "0bf882ff-e10e-42fc-c773-24f5f893cd01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    863.000000\n",
              "mean     137.528389\n",
              "std       25.950226\n",
              "min       60.000000\n",
              "25%      120.000000\n",
              "50%      140.000000\n",
              "75%      157.000000\n",
              "max      202.000000\n",
              "Name: thalch, dtype: float64"
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              "      <th></th>\n",
              "      <th>thalch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>863.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>137.528389</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>25.950226</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>60.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>120.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>140.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>157.000000</td>\n",
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              "      <th>max</th>\n",
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              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make scatter plot between thalch and age column\n",
        "px.scatter (df,x='thalch',y='age')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:53.869807Z",
          "iopub.execute_input": "2024-02-16T04:55:53.871309Z",
          "iopub.status.idle": "2024-02-16T04:55:53.947627Z",
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          "shell.execute_reply": "2024-02-16T04:55:53.946158Z"
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        "id": "rVPjuA8NFABF",
        "outputId": "ce21ad1a-d6c1-4e02-9ce5-affde912af41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        }
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exang Column"
      ],
      "metadata": {
        "id": "Yt9YyQx3FABF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['exang'].value_counts()"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "exang\n",
              "False    526\n",
              "True     337\n",
              "Name: count, dtype: int64"
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              "      <th></th>\n",
              "      <th>count</th>\n",
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              "      <th>exang</th>\n",
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              "      <td>337</td>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
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          },
          "metadata": {},
          "execution_count": 25
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      ]
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      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "\n",
        "fig = px.histogram(df, x='exang', color='dataset', barmode='group')\n",
        "fig.show()"
      ],
      "metadata": {
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          "iopub.status.busy": "2024-02-16T04:55:54.332789Z",
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      "execution_count": null,
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          "metadata": {}
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        {
          "output_type": "display_data",
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            "text/plain": [
              "<Figure size 1000x600 with 0 Axes>"
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    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Oldpeak Column"
      ],
      "metadata": {
        "id": "uuBQBrewFABG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['oldpeak'].describe())"
      ],
      "metadata": {
        "execution": {
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          "iopub.execute_input": "2024-02-16T04:55:54.772552Z",
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          "shell.execute_reply": "2024-02-16T04:55:54.782321Z"
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        "trusted": true,
        "id": "WwwANG4sFABG",
        "outputId": "4efd3c67-a06b-4255-e794-422b0a8781cd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    856.000000\n",
            "mean       0.880607\n",
            "std        1.091840\n",
            "min       -2.600000\n",
            "25%        0.000000\n",
            "50%        0.500000\n",
            "75%        1.500000\n",
            "max        6.200000\n",
            "Name: oldpeak, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for negative values\n",
        "negative_values = df[df['oldpeak'] < 0]\n",
        "\n",
        "# Count of negative values\n",
        "count_negative_values = len(negative_values)\n",
        "print(f\"Count of negative values: {count_negative_values}\")\n",
        "\n",
        "# Convert negative values to positive\n",
        "df['oldpeak'] = df['oldpeak'].abs()\n",
        "\n",
        "# Now, print the negative values after converting to positive\n",
        "negative_values_after_conversion = df[df['oldpeak'] < 0]\n",
        "count_negative_values_after_conversion = len(negative_values_after_conversion)\n",
        "\n",
        "print(f\"\\nCount of negative values after conversion: {count_negative_values_after_conversion}\")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:54.797956Z",
          "iopub.execute_input": "2024-02-16T04:55:54.799736Z",
          "iopub.status.idle": "2024-02-16T04:55:54.812069Z",
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          "shell.execute_reply": "2024-02-16T04:55:54.810534Z"
        },
        "trusted": true,
        "id": "FCWe4XS4FABG",
        "outputId": "be437ae6-3c89-4a05-8aaf-174bdf4117cd",
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          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Count of negative values: 12\n",
            "\n",
            "Count of negative values after conversion: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# scatter plot between oldpeak and age\n",
        "px.scatter (df,x='oldpeak',y='age')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:55.085199Z",
          "iopub.execute_input": "2024-02-16T04:55:55.085757Z",
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          "shell.execute_reply": "2024-02-16T04:55:55.162059Z"
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    {
      "cell_type": "markdown",
      "source": [
        "### Slope Column"
      ],
      "metadata": {
        "id": "lr8I-0RHFABG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# value count of slope\n",
        "df['slope'].value_counts()"
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        "execution": {
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "slope\n",
              "flat           345\n",
              "upsloping      202\n",
              "downsloping     63\n",
              "Name: count, dtype: int64"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>flat</th>\n",
              "      <td>345</td>\n",
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              "    <tr>\n",
              "      <th>upsloping</th>\n",
              "      <td>202</td>\n",
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              "    <tr>\n",
              "      <th>downsloping</th>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
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      "cell_type": "code",
      "source": [
        "# Create a histrogram based on 'slope ' and 'sex' using Plotly\n",
        "fig = px.histogram(data_frame=df, x='slope', color='sex', barmode='group')\n",
        "fig.show()"
      ],
      "metadata": {
        "execution": {
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          "base_uri": "https://localhost:8080/",
          "height": 562
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
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      "source": [
        "### Ca Column"
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          "height": 355
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    309.000000\n",
              "mean       0.676375\n",
              "std        0.935653\n",
              "min        0.000000\n",
              "25%        0.000000\n",
              "50%        0.000000\n",
              "75%        1.000000\n",
              "max        3.000000\n",
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              "      <th>ca</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>309.000000</td>\n",
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              "      <td>0.676375</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.935653</td>\n",
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              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.000000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
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          },
          "metadata": {},
          "execution_count": 33
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " ### Thal Column\n",
        "\n"
      ],
      "metadata": {
        "id": "TJJTrhxBFABI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['thal'].value_counts()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:55.39532Z",
          "iopub.execute_input": "2024-02-16T04:55:55.397232Z",
          "iopub.status.idle": "2024-02-16T04:55:55.40829Z",
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          "base_uri": "https://localhost:8080/",
          "height": 229
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "thal\n",
              "normal               196\n",
              "reversable defect    192\n",
              "fixed defect          46\n",
              "Name: count, dtype: int64"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thal</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>normal</th>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reversable defect</th>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fixed defect</th>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create a histrogram based on 'thal ' and 'sex' using Plotly\n",
        "fig = px.histogram(data_frame=df, x='thal', color='sex', barmode='group')\n",
        "fig.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:55.515173Z",
          "iopub.execute_input": "2024-02-16T04:55:55.516224Z",
          "iopub.status.idle": "2024-02-16T04:55:55.606986Z",
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          "shell.execute_reply": "2024-02-16T04:55:55.605584Z"
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        "id": "VzEo7jFTFABI",
        "outputId": "8c240da9-3e96-4342-d6c5-ec851049d44c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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      "cell_type": "code",
      "source": [
        "# create a histrogram based on 'thal ' and 'sex' using Plotly\n",
        "fig = px.histogram(data_frame=df, x='thal', color='dataset', barmode='group')\n",
        "fig.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:55.609545Z",
          "iopub.execute_input": "2024-02-16T04:55:55.610537Z",
          "iopub.status.idle": "2024-02-16T04:55:55.712689Z",
          "shell.execute_reply.started": "2024-02-16T04:55:55.610483Z",
          "shell.execute_reply": "2024-02-16T04:55:55.711004Z"
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        "id": "W3y_RiyfFABI",
        "outputId": "2a606f51-ca3b-46a5-9c65-813ab9695a38",
        "colab": {
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          "height": 562
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      },
      "execution_count": null,
      "outputs": [
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Num Column\n"
      ],
      "metadata": {
        "id": "9Lm1Pve2FABI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# unique values in num\n",
        "print(df['num'].unique())\n",
        "# check value count\n",
        "print(df['num'].value_counts())"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:55.714159Z",
          "iopub.execute_input": "2024-02-16T04:55:55.714562Z",
          "iopub.status.idle": "2024-02-16T04:55:55.724072Z",
          "shell.execute_reply.started": "2024-02-16T04:55:55.714529Z",
          "shell.execute_reply": "2024-02-16T04:55:55.722689Z"
        },
        "trusted": true,
        "id": "GNKwMjXJFABJ",
        "outputId": "53aebe35-89ed-474f-9ad5-d760f7bf37d6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 2 1 3 4]\n",
            "num\n",
            "0    411\n",
            "1    263\n",
            "2    109\n",
            "3    107\n",
            "4     28\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Missing Values"
      ],
      "metadata": {
        "id": "Q-xXT7sGFABJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Explore Missing Values"
      ],
      "metadata": {
        "id": "BHOe4yijFABJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check null values\n",
        "null_values=df.isnull().sum().sort_values(ascending=False)\n",
        "print(\"null_values:\\n\",null_values)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:55.727243Z",
          "iopub.execute_input": "2024-02-16T04:55:55.727652Z",
          "iopub.status.idle": "2024-02-16T04:55:55.743097Z",
          "shell.execute_reply.started": "2024-02-16T04:55:55.727621Z",
          "shell.execute_reply": "2024-02-16T04:55:55.741707Z"
        },
        "trusted": true,
        "id": "GCf8EGi6FABJ",
        "outputId": "30be6330-9e30-41df-98e7-c232dc2ca8e7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "null_values:\n",
            " ca          609\n",
            "thal        484\n",
            "slope       308\n",
            "fbs          89\n",
            "oldpeak      62\n",
            "trestbps     59\n",
            "thalch       55\n",
            "exang        55\n",
            "chol         30\n",
            "age           0\n",
            "sex           0\n",
            "dataset       0\n",
            "cp            0\n",
            "restecg       0\n",
            "num           0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### HeatMap"
      ],
      "metadata": {
        "id": "f5SENTItFABJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming df is your DataFrame\n",
        "plt.figure( figsize=(10, 6))\n",
        "sns.heatmap(df.isnull(), cbar=False)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:55.745208Z",
          "iopub.execute_input": "2024-02-16T04:55:55.745759Z",
          "iopub.status.idle": "2024-02-16T04:55:56.314885Z",
          "shell.execute_reply.started": "2024-02-16T04:55:55.745707Z",
          "shell.execute_reply": "2024-02-16T04:55:56.313682Z"
        },
        "trusted": true,
        "id": "ZnNcK-zPFABJ",
        "outputId": "649770e0-7848-4a80-b2b4-c0bf79307511",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Percentage of Null Values"
      ],
      "metadata": {
        "id": "ebn_cckAFABJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate the percentage of null values in each column\n",
        "null_percentage = (df.isnull().sum().sort_values(ascending=False) / len(df)) * 100\n",
        "\n",
        "# round the percentage to 2 decimal places\n",
        "null_percentage = null_percentage.round(2)\n",
        "\n",
        "# print the percentage of null values\n",
        "print(\"Percentage of null values:\\n\", null_percentage)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:56.316676Z",
          "iopub.execute_input": "2024-02-16T04:55:56.317339Z",
          "iopub.status.idle": "2024-02-16T04:55:56.328432Z",
          "shell.execute_reply.started": "2024-02-16T04:55:56.317301Z",
          "shell.execute_reply": "2024-02-16T04:55:56.327424Z"
        },
        "trusted": true,
        "id": "Z0fqd8TbFABJ",
        "outputId": "19f3ab62-7f79-4f2e-9e86-cb07dae6df06",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of null values:\n",
            " ca          66.34\n",
            "thal        52.72\n",
            "slope       33.55\n",
            "fbs          9.69\n",
            "oldpeak      6.75\n",
            "trestbps     6.43\n",
            "thalch       5.99\n",
            "exang        5.99\n",
            "chol         3.27\n",
            "age          0.00\n",
            "sex          0.00\n",
            "dataset      0.00\n",
            "cp           0.00\n",
            "restecg      0.00\n",
            "num          0.00\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Columns of Missing Values\n"
      ],
      "metadata": {
        "id": "7QlrIqZ7FABJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print missing data columns\n",
        "df.isnull().sum()[df.isnull().sum() > 0].sort_values(ascending=False)\n",
        "missing_data_cols = df.isnull().sum()[df.isnull().sum() > 0].index.tolist()\n",
        "print(\"missing_data_cols\",missing_data_cols)\n",
        "print(\"----------------------------------------------\")\n",
        "#  print  numerical columns who have missing values\n",
        "numerical_columns = df.select_dtypes(include='number').columns\n",
        "missing_numerical_columns = df[numerical_columns].isnull().sum()\n",
        "missing_numerical_columns = missing_numerical_columns[missing_numerical_columns > 0]\n",
        "print(\"Numerical columns with missing values:\")\n",
        "for column in missing_numerical_columns.index:\n",
        "    print(column)\n",
        "print(\"----------------------------------------------\")\n",
        "# Print categorical columns who have missing values\n",
        "categorical_columns = df.select_dtypes(include='object').columns\n",
        "missing_categorical_columns = df[categorical_columns].isnull().sum()\n",
        "missing_categorical_columns = missing_categorical_columns[missing_categorical_columns > 0]\n",
        "print(\"Categorical columns with missing values:\")\n",
        "for column in missing_categorical_columns.index:\n",
        "    print(column)\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:56.329884Z",
          "iopub.execute_input": "2024-02-16T04:55:56.330265Z",
          "iopub.status.idle": "2024-02-16T04:55:56.357174Z",
          "shell.execute_reply.started": "2024-02-16T04:55:56.330231Z",
          "shell.execute_reply": "2024-02-16T04:55:56.355887Z"
        },
        "trusted": true,
        "id": "kLyTS700FABK",
        "outputId": "ed35f3ed-8a20-4789-bf63-fd8b77b8f165",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "missing_data_cols ['trestbps', 'chol', 'fbs', 'thalch', 'exang', 'oldpeak', 'slope', 'ca', 'thal']\n",
            "----------------------------------------------\n",
            "Numerical columns with missing values:\n",
            "trestbps\n",
            "chol\n",
            "thalch\n",
            "oldpeak\n",
            "ca\n",
            "----------------------------------------------\n",
            "Categorical columns with missing values:\n",
            "fbs\n",
            "exang\n",
            "slope\n",
            "thal\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Impute Missing Values with Machine Learning"
      ],
      "metadata": {
        "id": "Yf0W4MiwFABK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_cols = ['slope', 'exang', 'restecg','fbs', 'cp', 'sex', 'num','thal']\n",
        "bool_cols = ['fbs', 'exang']\n",
        "numeric_cols = ['oldpeak', 'thalch', 'chol', 'trestbps', 'age','ca']"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:56.359176Z",
          "iopub.execute_input": "2024-02-16T04:55:56.360218Z",
          "iopub.status.idle": "2024-02-16T04:55:56.368103Z",
          "shell.execute_reply.started": "2024-02-16T04:55:56.360164Z",
          "shell.execute_reply": "2024-02-16T04:55:56.366746Z"
        },
        "trusted": true,
        "id": "4SzmSAA2FABK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# define the function to impute the missing values\n",
        "\n",
        "def impute_categorical_missing_data(passed_col):\n",
        "\n",
        "    df_null = df[df[passed_col].isnull()]\n",
        "    df_not_null = df[df[passed_col].notnull()]\n",
        "\n",
        "    X = df_not_null.drop(passed_col, axis=1)\n",
        "    y = df_not_null[passed_col]\n",
        "\n",
        "    other_missing_cols = [col for col in missing_data_cols if col != passed_col]\n",
        "\n",
        "    label_encoder = LabelEncoder()\n",
        "\n",
        "    for col in X.columns:\n",
        "        if X[col].dtype == 'object' or X[col].dtype == 'category':\n",
        "            X[col] = label_encoder.fit_transform(X[col])\n",
        "\n",
        "    if passed_col in bool_cols:\n",
        "        y = label_encoder.fit_transform(y)\n",
        "\n",
        "    iterative_imputer = IterativeImputer(estimator=RandomForestRegressor(random_state=42), add_indicator=True)\n",
        "\n",
        "    for col in other_missing_cols:\n",
        "        if X[col].isnull().sum() > 0:\n",
        "            col_with_missing_values = X[col].values.reshape(-1, 1)\n",
        "            imputed_values = iterative_imputer.fit_transform(col_with_missing_values)\n",
        "            X[col] = imputed_values[:, 0]\n",
        "        else:\n",
        "            pass\n",
        "\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "    rf_classifier = RandomForestClassifier()\n",
        "\n",
        "    rf_classifier.fit(X_train, y_train)\n",
        "\n",
        "    y_pred = rf_classifier.predict(X_test)\n",
        "\n",
        "    acc_score = accuracy_score(y_test, y_pred)\n",
        "\n",
        "    print(\"The feature '\"+ passed_col+ \"' has been imputed with\", round((acc_score * 100), 2), \"accuracy\\n\")\n",
        "\n",
        "    X = df_null.drop(passed_col, axis=1)\n",
        "\n",
        "    for col in X.columns:\n",
        "        if X[col].dtype == 'object' or X[col].dtype == 'category':\n",
        "            X[col] = label_encoder.fit_transform(X[col])\n",
        "\n",
        "    for col in other_missing_cols:\n",
        "        if X[col].isnull().sum() > 0:\n",
        "            col_with_missing_values = X[col].values.reshape(-1, 1)\n",
        "            imputed_values = iterative_imputer.fit_transform(col_with_missing_values)\n",
        "            X[col] = imputed_values[:, 0]\n",
        "        else:\n",
        "            pass\n",
        "\n",
        "    if len(df_null) > 0:\n",
        "        df_null[passed_col] = rf_classifier.predict(X)\n",
        "        if passed_col in bool_cols:\n",
        "            df_null[passed_col] = df_null[passed_col].map({0: False, 1: True})\n",
        "        else:\n",
        "            pass\n",
        "    else:\n",
        "        pass\n",
        "\n",
        "    df_combined = pd.concat([df_not_null, df_null])\n",
        "\n",
        "    return df_combined[passed_col]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:56.374804Z",
          "iopub.execute_input": "2024-02-16T04:55:56.375325Z",
          "iopub.status.idle": "2024-02-16T04:55:56.40833Z",
          "shell.execute_reply.started": "2024-02-16T04:55:56.375278Z",
          "shell.execute_reply": "2024-02-16T04:55:56.407033Z"
        },
        "trusted": true,
        "id": "aIt3uUS7FABL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def impute_continuous_missing_data(passed_col):\n",
        "\n",
        "    df_null = df[df[passed_col].isnull()]\n",
        "    df_not_null = df[df[passed_col].notnull()]\n",
        "\n",
        "    X = df_not_null.drop(passed_col, axis=1)\n",
        "    y = df_not_null[passed_col]\n",
        "\n",
        "    other_missing_cols = [col for col in missing_data_cols if col != passed_col]\n",
        "\n",
        "    label_encoder = LabelEncoder()\n",
        "\n",
        "    for col in X.columns:\n",
        "        if X[col].dtype == 'object' or X[col].dtype == 'category':\n",
        "            X[col] = label_encoder.fit_transform(X[col])\n",
        "\n",
        "    iterative_imputer = IterativeImputer(estimator=RandomForestRegressor(random_state=42), add_indicator=True)\n",
        "\n",
        "    for col in other_missing_cols:\n",
        "        if X[col].isnull().sum() > 0:\n",
        "            col_with_missing_values = X[col].values.reshape(-1, 1)\n",
        "            imputed_values = iterative_imputer.fit_transform(col_with_missing_values)\n",
        "            X[col] = imputed_values[:, 0]\n",
        "        else:\n",
        "            pass\n",
        "\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "    rf_regressor = RandomForestRegressor()\n",
        "\n",
        "    rf_regressor.fit(X_train, y_train)\n",
        "\n",
        "    y_pred = rf_regressor.predict(X_test)\n",
        "\n",
        "    print(\"MAE =\", mean_absolute_error(y_test, y_pred), \"\\n\")\n",
        "    print(\"RMSE =\", mean_squared_error(y_test, y_pred, squared=False), \"\\n\")\n",
        "    print(\"R2 =\", r2_score(y_test, y_pred), \"\\n\")\n",
        "\n",
        "    X = df_null.drop(passed_col, axis=1)\n",
        "\n",
        "    for col in X.columns:\n",
        "        if X[col].dtype == 'object' or X[col].dtype == 'category':\n",
        "            X[col] = label_encoder.fit_transform(X[col])\n",
        "\n",
        "    for col in other_missing_cols:\n",
        "        if X[col].isnull().sum() > 0:\n",
        "            col_with_missing_values = X[col].values.reshape(-1, 1)\n",
        "            imputed_values = iterative_imputer.fit_transform(col_with_missing_values)\n",
        "            X[col] = imputed_values[:, 0]\n",
        "        else:\n",
        "            pass\n",
        "\n",
        "    if len(df_null) > 0:\n",
        "        df_null[passed_col] = rf_regressor.predict(X)\n",
        "    else:\n",
        "        pass\n",
        "\n",
        "    df_combined = pd.concat([df_not_null, df_null])\n",
        "\n",
        "    return df_combined[passed_col]"
      ],
      "metadata": {
        "id": "mro5ubUWJv8c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# impute missing values using our functions\n",
        "for col in missing_data_cols:\n",
        "    print(\"Missing Values\", col, \":\", str(round((df[col].isnull().sum() / len(df)) * 100, 2))+\"%\")\n",
        "    if col in categorical_cols:\n",
        "        df[col] = impute_categorical_missing_data(col)\n",
        "    elif col in numeric_cols:\n",
        "        df[col] = impute_continuous_missing_data(col)\n",
        "    else:\n",
        "        pass"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:55:56.410408Z",
          "iopub.execute_input": "2024-02-16T04:55:56.410881Z",
          "iopub.status.idle": "2024-02-16T04:56:00.196812Z",
          "shell.execute_reply.started": "2024-02-16T04:55:56.410813Z",
          "shell.execute_reply": "2024-02-16T04:56:00.195421Z"
        },
        "trusted": true,
        "id": "vSd5_x05FABL",
        "outputId": "e4a379a4-34c5-4d6a-d569-9ec8e4f8f52c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Values trestbps : 6.43%\n",
            "MAE = 12.605523255813951 \n",
            "\n",
            "RMSE = 17.118009029660396 \n",
            "\n",
            "R2 = 0.05848553110670818 \n",
            "\n",
            "Missing Values chol : 3.27%\n",
            "MAE = 51.23814606741573 \n",
            "\n",
            "RMSE = 69.78789397528499 \n",
            "\n",
            "R2 = 0.5669485098418734 \n",
            "\n",
            "Missing Values fbs : 9.69%\n",
            "The feature 'fbs' has been imputed with 81.93 accuracy\n",
            "\n",
            "Missing Values thalch : 5.99%\n",
            "MAE = 17.0128901734104 \n",
            "\n",
            "RMSE = 21.775728586922863 \n",
            "\n",
            "R2 = 0.31637518354125416 \n",
            "\n",
            "Missing Values exang : 5.99%\n",
            "The feature 'exang' has been imputed with 78.61 accuracy\n",
            "\n",
            "Missing Values oldpeak : 6.75%\n",
            "MAE = 0.5239593023255814 \n",
            "\n",
            "RMSE = 0.7614563196560825 \n",
            "\n",
            "R2 = 0.43706823041083553 \n",
            "\n",
            "Missing Values slope : 33.55%\n",
            "The feature 'slope' has been imputed with 68.85 accuracy\n",
            "\n",
            "Missing Values ca : 66.34%\n",
            "MAE = 0.6643548387096775 \n",
            "\n",
            "RMSE = 0.9130302259774247 \n",
            "\n",
            "R2 = 0.11405822504838259 \n",
            "\n",
            "Missing Values thal : 52.72%\n",
            "The feature 'thal' has been imputed with 71.26 accuracy\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check after imputation\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:56:00.198773Z",
          "iopub.execute_input": "2024-02-16T04:56:00.199953Z",
          "iopub.status.idle": "2024-02-16T04:56:00.213311Z",
          "shell.execute_reply.started": "2024-02-16T04:56:00.199895Z",
          "shell.execute_reply": "2024-02-16T04:56:00.211777Z"
        },
        "trusted": true,
        "id": "38AGMKqeFABL",
        "outputId": "247ce848-b312-4dae-bb2b-58399b75f12d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "dataset     0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalch      0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "slope       0\n",
              "ca          0\n",
              "thal        0\n",
              "num         0\n",
              "dtype: int64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dataset</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fbs</th>\n",
              "      <td>0</td>\n",
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              "      <th>restecg</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>thalch</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exang</th>\n",
              "      <td>0</td>\n",
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              "      <th>oldpeak</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure( figsize=(10, 6))\n",
        "sns.heatmap(df.isnull(), cbar=False)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:56:00.215389Z",
          "iopub.execute_input": "2024-02-16T04:56:00.216178Z",
          "iopub.status.idle": "2024-02-16T04:56:00.808782Z",
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          "shell.execute_reply": "2024-02-16T04:56:00.807623Z"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Outliers"
      ],
      "metadata": {
        "id": "YSJJ2TJ4FABL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Check Outliers with Graphical Technique"
      ],
      "metadata": {
        "id": "0LcmulA3FABL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 20))\n",
        "\n",
        "colors = ['red', 'green', 'blue', 'orange', 'magenta']\n",
        "\n",
        "# Assuming numeric_cols is a list of numeric column names\n",
        "for i, col in enumerate(numeric_cols):\n",
        "    if i < len(colors):  # Check if the index is within the range of colors\n",
        "        plt.subplot(3, 3, i+1)\n",
        "        sns.boxplot(x=df[col], color=colors[i])\n",
        "        plt.title(col)\n",
        "    else:\n",
        "        break  # Exit the loop if there are no more colors available\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:56:00.810154Z",
          "iopub.execute_input": "2024-02-16T04:56:00.811072Z",
          "iopub.status.idle": "2024-02-16T04:56:01.621554Z",
          "shell.execute_reply.started": "2024-02-16T04:56:00.811028Z",
          "shell.execute_reply": "2024-02-16T04:56:01.620194Z"
        },
        "trusted": true,
        "id": "oeswGehaFABL",
        "outputId": "34a7e859-49dc-4f60-82b0-9f7702c2f3e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 5 Axes>"
            ],
            "image/png": 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2Nv/xH/+RsWPHpq6uruxS39ZnP/vZyve9e/dOnz59sssuu2Ty5MkZNGhQFSt776688soMHz48bdu2bbS8OfbP2s7LsD7stttumTlzZhYtWpQbbrghI0aMyJQpU6pdVov07LPP5qSTTsqkSZNWO7fw7mysc7pqaWhoyP7775/zzjsvSbLPPvvkscceyxVXXJERI0ZUubqWb01zdQAANm5Vv5VThw4d0rp167zwwguNlr/wwgvp0qVLlap650488cTcdtttuffee7PDDju87bb9+/dPksyZM6eM0t6T9u3b533ve1/mzJmTLl265I033sjChQsbbdPc+2ru3Lm566678uUvf/ltt2tJ/bJqf7/duOnSpctqHyC/YsWKvPLKK822v1aFEnPnzs2kSZMaXS2xJv3798+KFSvyzDPPlFPge7DzzjunQ4cOleOrJfZPkvzpT3/K7Nmz1zmekur3z9rOy005l3Xp0mWN42vVOkiS2tra7Lrrrtlvv/0yduzY9O3bNxdffHGLfb+spocffjgvvvhi9t1337Rp0yZt2rTJlClTcskll6RNmzbp3LmzffoebQxzumrq2rVr9txzz0bL9thjj8rtsZoyN2PN1jRXd4wCAGz8qh5M1NbWZr/99svdd99dWdbQ0JC77747AwYMqGJlTVMURU488cTcfPPNueeee9KzZ891PmbmzJlJ3voBp7l79dVX89RTT6Vr167Zb7/9stlmmzXqq9mzZ2fevHnNuq+uuuqqdOrUKYcddtjbbteS+qVnz57p0qVLo75YvHhxHnrooUpfDBgwIAsXLszDDz9c2eaee+5JQ0NDJYRpTlaFEk8++WTuuuuubLfddut8zMyZM9OqVavVbonUHD333HN5+eWXK8dXS+ufVX75y19mv/32S9++fde5bbX6Z13n5aacywYMGJBHH320UXi0Kiz7119MwSoNDQ1Zvnx5i32/rKZBgwbl0UcfzcyZMytf+++/f4YPH1753j59bzaGOV01fehDH8rs2bMbLfuf//mf9OjRI0nT5mas2Zrm6o5RAIBNQJU/fLsoiqIYP358UVdXV1x99dXFrFmziuOOO65o3759sWDBgmqXtk4nnHBCsfXWWxeTJ08u5s+fX/launRpURRFMWfOnOI73/lOMX369OLpp58uJkyYUOy8887FAQccUOXK1+zrX/96MXny5OLpp58u/vznPxeDBw8uOnToULz44otFURTF8ccfX+y4447FPffcU0yfPr0YMGBAMWDAgCpXvXYrV64sdtxxx2LMmDGNlreEflmyZEnxyCOPFI888kiRpLjwwguLRx55pJg7d25RFEVx/vnnF+3bty8mTJhQ/PWvfy2GDh1a9OzZs1i2bFnlOQ499NBin332KR566KHivvvuK3r16lUMGzas2bXnjTfeKI444ohihx12KGbOnNloLC1fvrwoiqK4//77ix/96EfFzJkzi6eeeqq49tpri44dOxZf/OIXm117lixZUpx66qnFAw88UDz99NPFXXfdVey7775Fr169itdff73yHC2lf1ZZtGhRsfnmmxeXX375ao9vTv2zrvNyUaz7XLZixYpir732Kg4++OBi5syZxR133FF07NixOOOMM0pvD83TN77xjWLKlCnF008/Xfz1r38tvvGNbxQ1NTXFH/7wh6IoWt77ZXN04IEHFieddFLl//bpO7OxzemqberUqUWbNm2K73//+8WTTz5ZXHfddcXmm29eXHvttZVtmjI3o7G1zdWLwjEKALCxaxbBRFEUxaWXXlrsuOOORW1tbdGvX7/iwQcfrHZJTZJkjV9XXXVVURRFMW/evOKAAw4ott1226Kurq7Yddddi9NOO61YtGhRdQtfi6OOOqro2rVrUVtbW2y//fbFUUcdVcyZM6eyftmyZcVXvvKVYptttik233zz4hOf+EQxf/78Klb89u68884iSTF79uxGy1tCv9x7771rPLZGjBhRFEVRNDQ0FGeddVbRuXPnoq6urhg0aNBq7Xz55ZeLYcOGFVtuuWVRX19fjBw5sliyZEkVWvP27Xn66afXOpbuvffeoiiK4uGHHy769+9fbL311kXbtm2LPfbYozjvvPMa/aK/ubRn6dKlxcEHH1x07Nix2GyzzYoePXoUxx577Gpha0vpn1V++tOfFu3atSsWLly42uObU/+s67xcFE07lz3zzDPFkCFDinbt2hUdOnQovv71rxdvvvlmya2huTrmmGOKHj16FLW1tUXHjh2LQYMGVUKJomh575fN0b8GE/bpO7Oxzemag1tvvbXYa6+9irq6umL33XcvfvaznzVa35S5GY2tba5eFI5RAICNXU1RFMWGuRYDAAAAAACgsap/xgQAAAAAALDpEEwAAAAAAAClEUwAAAAAAAClEUwAAAAAAAClEUwAAAAAAAClEUwAAAAAAAClEUwAAAAAAAClEUzARuroo4/Oxz/+8WqXAQAAAADQiGACNqCBAwfm5JNP3qCv8cwzz6SmpiYzZ87coK8DAAAAALA+CCagioqiyIoVK6pdBgAAAABAaQQTsIEcffTRmTJlSi6++OLU1NSkpqYmV199dWpqajJx4sTst99+qaury3333ZeGhoaMHTs2PXv2TLt27dK3b9/ccMMNlef65z//meHDh6djx45p165devXqlauuuipJ0rNnzyTJPvvsk5qamgwcOLBRHeeee246duyY+vr6HH/88XnjjTcq6wYOHJgTTzwxJ554Yrbeeut06NAhZ511VoqiqGzzk5/8JL169Urbtm3TuXPnfPrTn96Aew0AaE7uuOOOfPjDH0779u2z3Xbb5WMf+1ieeuqpyvr7778/e++9d9q2bZv9998/t9xyy2pXcj722GMZMmRIttxyy3Tu3Dlf+MIX8tJLL1WhNQAAQHMhmIAN5OKLL86AAQNy7LHHZv78+Zk/f366d++eJPnGN76R888/P0888UT69OmTsWPH5pprrskVV1yRxx9/PKeccko+//nPZ8qUKUmSs846K7NmzcrEiRPzxBNP5PLLL0+HDh2SJFOnTk2S3HXXXZk/f35uuummSg133313nnjiiUyePDnXX399brrpppx77rmN6hw3blzatGmTqVOn5uKLL86FF16YX/ziF0mS6dOn52tf+1q+853vZPbs2bnjjjtywAEHbPB9BwA0D6+99lpGjx6d6dOn5+67706rVq3yiU98Ig0NDVm8eHEOP/zw9O7dOzNmzMh3v/vdjBkzptHjFy5cmIMOOij77LNPpk+fnjvuuCMvvPBCPvOZz1SpRQAAQHNQU/zfP40G1quBAwdm7733zkUXXZQkmTx5cv7t3/4tt9xyS4YOHZokWb58ebbddtvcddddGTBgQOWxX/7yl7N06dL86le/yhFHHJEOHTrkyiuvXO01nnnmmfTs2TOPPPJI9t5778ryo48+OrfeemueffbZbL755kmSK664IqeddloWLVqUVq1aZeDAgXnxxRfz+OOPp6amJslbocnvfve7zJo1KzfddFNGjhyZ5557LltttdUG2ksAQEvx0ksvpWPHjnn00Udz33335Vvf+laee+65tG3bNknyi1/8Iscee2xlXvK9730vf/rTn3LnnXdWnuO5555L9+7dM3v27Lzvfe+rVlMAAIAqcsUEVMH+++9f+X7OnDlZunRpPvrRj2bLLbesfF1zzTWVWyWccMIJGT9+fPbee++cfvrpuf/++5v0On379q2EEkkyYMCAvPrqq3n22Wcry/7f//t/lVBi1TZPPvlkVq5cmY9+9KPp0aNHdt5553zhC1/Iddddl6VLl77X5gMALcSTTz6ZYcOGZeedd059fX122mmnJMm8efMye/bs9OnTpxJKJEm/fv0aPf4vf/lL7r333kZznN133z1JGt0SCgAA2LS0qXYBsCnaYostKt+/+uqrSZLf//732X777RttV1dXlyQZMmRI5s6dm9tvvz2TJk3KoEGDMmrUqPzwhz/coHVutdVWmTFjRiZPnpw//OEPOfvss/Ptb38706ZNS/v27TfoawMA1Xf44YenR48e+fnPf55u3bqloaEhe+21V6PPrHo7r776ag4//PD853/+52rrunbtur7LBQAAWgjBBGxAtbW1Wbly5dtus+eee6auri7z5s3LgQceuNbtOnbsmBEjRmTEiBH5yEc+ktNOOy0//OEPU1tbmyRrfJ2//OUvWbZsWdq1a5ckefDBB7PllltWPusiSR566KFGj3nwwQfTq1evtG7dOknSpk2bDB48OIMHD84555yT9u3b55577sknP/nJpu0EAKBFevnllzN79uz8/Oc/z0c+8pEkyX333VdZv9tuu+Xaa6/N8uXLK39MMW3atEbPse++++bGG2/MTjvtlDZt/OgBAAC8xa2cYAPaaaed8tBDD+WZZ57JSy+9lIaGhtW22WqrrXLqqafmlFNOybhx4/LUU09lxowZufTSSzNu3Lgkydlnn50JEyZkzpw5efzxx3Pbbbdljz32SJJ06tQp7dq1q3yY5KJFiyrP/cYbb+RLX/pSZs2aldtvvz3nnHNOTjzxxLRq9b9Df968eRk9enRmz56d66+/PpdeemlOOumkJMltt92WSy65JDNnzszcuXNzzTXXpKGhIbvtttuG3G0AQDOwzTbbZLvttsvPfvazzJkzJ/fcc09Gjx5dWf+5z30uDQ0NOe644/LEE0/kzjvvrFzNueo2kaNGjcorr7ySYcOGZdq0aXnqqady5513ZuTIkev84w0AAGDjJZiADejUU09N69ats+eee6Zjx46ZN2/eGrf77ne/m7POOitjx47NHnvskUMPPTS///3v07NnzyRvXXlxxhlnpE+fPjnggAPSunXrjB8/PslbVzRccskl+elPf5pu3bpVPlQ7SQYNGpRevXrlgAMOyFFHHZUjjjgi3/72txu99he/+MUsW7Ys/fr1y6hRo3LSSSfluOOOS5K0b98+N910Uw466KDsscceueKKK3L99dfn/e9//wbYWwBAc9KqVauMHz8+Dz/8cPbaa6+ccsopueCCCyrr6+vrc+utt2bmzJnZe++9881vfjNnn312klQ+d6Jbt27585//nJUrV+bggw9O7969c/LJJ6d9+/aN/lACAADYtNQURVFUuwigOgYOHJi99947F110UbVLAQA2Atddd11GjhyZRYsWVW4lCQAA8K/c6BUAAHhXrrnmmuy8887Zfvvt85e//CVjxozJZz7zGaEEAADwtgQTAADAu7JgwYKcffbZWbBgQbp27Zojjzwy3//+96tdFgAA0My5lRMAAAAAAFAanzgHAAAAAACURjABAAAAAACURjABAAAAAACURjABAAAAAACURjABAAAAAACURjABAAAAAACURjABAAAAAACURjABAAAAAACURjABAAAAAACU5v8DOVwLI8NO2sYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Check Outliers with IQR Method"
      ],
      "metadata": {
        "id": "iRvOrIu-FABL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# check number outliers in numeric_cols using IQR\n",
        "Q1 = df[numeric_cols].quantile(0.25)\n",
        "Q3 = df[numeric_cols].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "outliers_count_specified = ((df[numeric_cols] < (Q1 - 1.5 * IQR)) | (df[numeric_cols] > (Q3 + 1.5 * IQR))).sum()\n",
        "\n",
        "outliers_count_specified"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:56:01.623714Z",
          "iopub.execute_input": "2024-02-16T04:56:01.624182Z",
          "iopub.status.idle": "2024-02-16T04:56:01.646916Z",
          "shell.execute_reply.started": "2024-02-16T04:56:01.624145Z",
          "shell.execute_reply": "2024-02-16T04:56:01.645977Z"
        },
        "trusted": true,
        "id": "UuGNkpGKFABL",
        "outputId": "489d81da-9045-4024-987b-e93b6e254d95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "oldpeak       2\n",
              "thalch        2\n",
              "chol        184\n",
              "trestbps     25\n",
              "age           0\n",
              "ca           21\n",
              "dtype: int64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalch</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Deal with Outliers"
      ],
      "metadata": {
        "id": "FVYmFjmXFABL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### trestbps"
      ],
      "metadata": {
        "id": "usUhWn9IFABL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['trestbps'].describe()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:56:01.648565Z",
          "iopub.execute_input": "2024-02-16T04:56:01.649276Z",
          "iopub.status.idle": "2024-02-16T04:56:01.661671Z",
          "shell.execute_reply.started": "2024-02-16T04:56:01.64924Z",
          "shell.execute_reply": "2024-02-16T04:56:01.660346Z"
        },
        "trusted": true,
        "id": "ajNj1HOkFABM",
        "outputId": "97b13189-48cd-42c4-ed8e-272c3d8c6b46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    918.000000\n",
              "mean     132.579390\n",
              "std       18.580314\n",
              "min        0.000000\n",
              "25%      120.000000\n",
              "50%      130.000000\n",
              "75%      142.000000\n",
              "max      200.000000\n",
              "Name: trestbps, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>trestbps</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>918.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>132.579390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>18.580314</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>120.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>130.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>142.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# remove rows with values less than 80 in the 'trestbps' column\n",
        "df = df[df['trestbps'] >= 80]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:56:01.663409Z",
          "iopub.execute_input": "2024-02-16T04:56:01.664141Z",
          "iopub.status.idle": "2024-02-16T04:56:01.673308Z",
          "shell.execute_reply.started": "2024-02-16T04:56:01.664072Z",
          "shell.execute_reply": "2024-02-16T04:56:01.671972Z"
        },
        "trusted": true,
        "id": "HLngWrlVFABM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['dataset'].value_counts()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:56:01.675269Z",
          "iopub.execute_input": "2024-02-16T04:56:01.675795Z",
          "iopub.status.idle": "2024-02-16T04:56:01.688235Z",
          "shell.execute_reply.started": "2024-02-16T04:56:01.675746Z",
          "shell.execute_reply": "2024-02-16T04:56:01.687174Z"
        },
        "trusted": true,
        "id": "W4uw2BQVFABN",
        "outputId": "a473b727-1a15-452d-bfc7-afc0c51d07cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dataset\n",
              "Cleveland        304\n",
              "Hungary          292\n",
              "VA Long Beach    199\n",
              "Switzerland      122\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dataset</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cleveland</th>\n",
              "      <td>304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hungary</th>\n",
              "      <td>292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VA Long Beach</th>\n",
              "      <td>199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Switzerland</th>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### thalch"
      ],
      "metadata": {
        "id": "GYYPOnA8FABN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# remove rows with values less than 71 in the 'thalch' column\n",
        "df = df[df['thalch'] >= 71]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:56:01.689977Z",
          "iopub.execute_input": "2024-02-16T04:56:01.690388Z",
          "iopub.status.idle": "2024-02-16T04:56:01.70348Z",
          "shell.execute_reply.started": "2024-02-16T04:56:01.690356Z",
          "shell.execute_reply": "2024-02-16T04:56:01.702126Z"
        },
        "trusted": true,
        "id": "FU4NYkl5FABN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['thalch'].describe()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:56:01.705241Z",
          "iopub.execute_input": "2024-02-16T04:56:01.705626Z",
          "iopub.status.idle": "2024-02-16T04:56:01.721074Z",
          "shell.execute_reply.started": "2024-02-16T04:56:01.705596Z",
          "shell.execute_reply": "2024-02-16T04:56:01.720016Z"
        },
        "trusted": true,
        "id": "YFO-DPBBFABN",
        "outputId": "35d64807-c9d1-4857-d908-9ff22b24dac6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    912.000000\n",
              "mean     136.940055\n",
              "std       25.144596\n",
              "min       71.000000\n",
              "25%      119.337500\n",
              "50%      138.000000\n",
              "75%      156.000000\n",
              "max      202.000000\n",
              "Name: thalch, dtype: float64"
            ],
            "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>thalch</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>912.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>136.940055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>25.144596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>71.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>119.337500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>138.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>156.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>202.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### oldpeak"
      ],
      "metadata": {
        "id": "0MqJkL6tFABN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# remove outliers in 'oldpeak' column\n",
        "Q1 = df['oldpeak'].quantile(0.25)\n",
        "Q3 = df['oldpeak'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "lower_bound = Q1 - 1.5 * IQR\n",
        "upper_bound = Q3 + 1.5 * IQR\n",
        "df=df[(df['oldpeak'] >= lower_bound) & (df['oldpeak'] <= upper_bound)]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:56:01.72246Z",
          "iopub.execute_input": "2024-02-16T04:56:01.722914Z",
          "iopub.status.idle": "2024-02-16T04:56:01.735797Z",
          "shell.execute_reply.started": "2024-02-16T04:56:01.722874Z",
          "shell.execute_reply": "2024-02-16T04:56:01.734436Z"
        },
        "trusted": true,
        "id": "feAI-AT_FABO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### chol ( Cholesterol )"
      ],
      "metadata": {
        "id": "4tyZMc7aFABO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print the row from df where chol value is 0\n",
        "print(\"zero_counts :\",(df['chol'] == 0).sum())\n",
        "# remove this row from data\n",
        "df = df[df['chol'] != 0]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:56:01.737989Z",
          "iopub.execute_input": "2024-02-16T04:56:01.739321Z",
          "iopub.status.idle": "2024-02-16T04:56:01.750069Z",
          "shell.execute_reply.started": "2024-02-16T04:56:01.73926Z",
          "shell.execute_reply": "2024-02-16T04:56:01.74862Z"
        },
        "trusted": true,
        "id": "PpgeRpSNFABO",
        "outputId": "61f303cc-048f-4f1a-cbfa-b10e8d8e9bfa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "zero_counts : 166\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# remove values less than 126\n",
        "df = df[df['chol'] >= 126]\n",
        "# remove values greater than 400\n",
        "df = df[df['chol'] <= 400]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:56:01.756635Z",
          "iopub.execute_input": "2024-02-16T04:56:01.757398Z",
          "iopub.status.idle": "2024-02-16T04:56:01.767731Z",
          "shell.execute_reply.started": "2024-02-16T04:56:01.757352Z",
          "shell.execute_reply": "2024-02-16T04:56:01.766672Z"
        },
        "trusted": true,
        "id": "7TknMb_gFABO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Machine Learning"
      ],
      "metadata": {
        "id": "b97dLDc2FABO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Encoded the Data"
      ],
      "metadata": {
        "id": "2zNyaQinFABO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  # apply labelencoder on categorical column\n",
        "for col in df.columns:\n",
        "      if df[col].dtype == \"category\" or df[col].dtype == \"object\":\n",
        "          df[col] =LabelEncoder().fit_transform(df[col])\n",
        "      else:\n",
        "          pass"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:56:01.769622Z",
          "iopub.execute_input": "2024-02-16T04:56:01.770058Z",
          "iopub.status.idle": "2024-02-16T04:56:01.786001Z",
          "shell.execute_reply.started": "2024-02-16T04:56:01.770021Z",
          "shell.execute_reply": "2024-02-16T04:56:01.784175Z"
        },
        "trusted": true,
        "id": "-iJBo0N-FABO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Split the Data into X and y"
      ],
      "metadata": {
        "id": "mwHNU21NFABO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# split data into X and y\n",
        "X = df.drop('num', axis=1)\n",
        "y = df['num']\n",
        "# data into train and split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-02-16T04:56:01.788012Z",
          "iopub.execute_input": "2024-02-16T04:56:01.788556Z",
          "iopub.status.idle": "2024-02-16T04:56:01.80146Z",
          "shell.execute_reply.started": "2024-02-16T04:56:01.788507Z",
          "shell.execute_reply": "2024-02-16T04:56:01.800103Z"
        },
        "trusted": true,
        "id": "TCmCVIUwFABO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Enlist some  models that use to predict the heart disease. These models should be classifiers for multi-class classification.\n",
        "\n",
        "1. logistic regression\n",
        "2. KNN\n",
        "3. NB\n",
        "4. SVM\n",
        "5. Decision Tree\n",
        "6. Random Forest\n",
        "7. XGBoost\n",
        "8. GradientBoosting\n",
        "9. AdaBoost\n",
        "10. lightGBM"
      ],
      "metadata": {
        "id": "Hep7jdq9FABO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Select the Best Model"
      ],
      "metadata": {
        "id": "lR6siPyNFABP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, VotingClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "8AiUN2i4IMc2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a list of models to evaluate\n",
        "models = [\n",
        "    ('Random Forest', RandomForestClassifier(random_state=42),\n",
        "        {'model__n_estimators': [50, 100, 200],\n",
        "         'model__max_depth': [None, 10, 20]}),  # Add hyperparameters for Random Forest\n",
        "    ('Gradient Boosting', GradientBoostingClassifier(random_state=42),\n",
        "        {'model__n_estimators': [50, 100, 200],\n",
        "         'model__learning_rate': [0.05, 0.1, 0.5]}),  # Add hyperparameters for Gradient Boosting\n",
        "    ('Support Vector Machine', SVC(random_state=42, class_weight='balanced'),\n",
        "        {'model__C': [0.1, 1, 10],\n",
        "         'model__gamma': ['scale', 'auto']}),  # Add hyperparameters for SVM\n",
        "    ('Logistic Regression', LogisticRegression(random_state=42, class_weight='balanced'),\n",
        "        {'model__C': [0.1, 1, 10],\n",
        "         'model__penalty': ['l1', 'l2']}),  # Add hyperparameters for Logistic Regression\n",
        "    ('K-Nearest Neighbors', KNeighborsClassifier(),\n",
        "        {'model__n_neighbors': [3, 5, 7],\n",
        "         'model__weights': ['uniform', 'distance']}),  # Add hyperparameters for KNN\n",
        "    ('Decision Tree', DecisionTreeClassifier(random_state=42),\n",
        "        {'model__max_depth': [None, 10, 20],\n",
        "         'model__min_samples_split': [2, 5, 10]}),  # Add hyperparameters for Decision Tree\n",
        "    ('Ada Boost', AdaBoostClassifier(random_state=42),\n",
        "        {'model__n_estimators': [50, 100, 200],\n",
        "         'model__learning_rate': [0.05, 0.1, 0.5]}),  # Add hyperparameters for Ada Boost\n",
        "    ('XG Boost', XGBClassifier(random_state=42),\n",
        "        {'model__n_estimators': [50, 100, 200],\n",
        "         'model__learning_rate': [0.05, 0.1, 0.5]}),  # Add hyperparameters for XG Boost\n",
        "    ('Naive Bayes', GaussianNB(), {})  # No hyperparameters for Naive Bayes\n",
        "]\n",
        "\n",
        "best_model = None\n",
        "best_accuracy = 0.0"
      ],
      "metadata": {
        "id": "ALYQHpUaIOUW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Iterate over the models and evaluate their performance\n",
        "for name, model, param_grid in models:\n",
        "    # Create a pipeline for each model\n",
        "    pipeline = Pipeline([\n",
        "        ('scaler', MinMaxScaler()),  # Feature Scaling\n",
        "        ('model', model)\n",
        "    ])\n",
        "\n",
        "    # Hyperparameter tuning using GridSearchCV\n",
        "    if param_grid:\n",
        "        grid_search = GridSearchCV(pipeline, param_grid, cv=5)\n",
        "        grid_search.fit(X_train, y_train)\n",
        "        pipeline = grid_search.best_estimator_\n",
        "\n",
        "    # Fit the pipeline on the training data\n",
        "    pipeline.fit(X_train, y_train)\n",
        "\n",
        "    # Make predictions on the test data\n",
        "    y_pred = pipeline.predict(X_test)\n",
        "\n",
        "    # Calculate accuracy score\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "    # Print the performance metrics\n",
        "    print(\"Model:\", name)\n",
        "    print(\"Test Accuracy:\", accuracy)\n",
        "    print()\n",
        "\n",
        "    # Check if the current model has the best accuracy\n",
        "    if accuracy > best_accuracy:\n",
        "        best_accuracy = accuracy\n",
        "        best_model = pipeline"
      ],
      "metadata": {
        "id": "5YWdc3biIQTR",
        "outputId": "101300f8-0ae5-4536-efdd-87c651e739c1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: Random Forest\n",
            "Test Accuracy: 0.726027397260274\n",
            "\n",
            "Model: Gradient Boosting\n",
            "Test Accuracy: 0.6986301369863014\n",
            "\n",
            "Model: Support Vector Machine\n",
            "Test Accuracy: 0.6712328767123288\n",
            "\n",
            "Model: Logistic Regression\n",
            "Test Accuracy: 0.6027397260273972\n",
            "\n",
            "Model: K-Nearest Neighbors\n",
            "Test Accuracy: 0.6484018264840182\n",
            "\n",
            "Model: Decision Tree\n",
            "Test Accuracy: 0.6164383561643836\n",
            "\n",
            "Model: Ada Boost\n",
            "Test Accuracy: 0.6621004566210046\n",
            "\n",
            "Model: XG Boost\n",
            "Test Accuracy: 0.7031963470319634\n",
            "\n",
            "Model: Naive Bayes\n",
            "Test Accuracy: 0.634703196347032\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Best Model:\")\n",
        "print(\"Test Accuracy:\", best_accuracy)\n",
        "print(\"Model Pipeline:\", best_model, \"with accuracy\", round(best_accuracy * 100, 2), \"%\")"
      ],
      "metadata": {
        "id": "Z4Bpjf0YJ8zQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c27cdef-b219-4664-d753-f63f3a962f5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Model:\n",
            "Test Accuracy: 0.726027397260274\n",
            "Model Pipeline: Pipeline(steps=[('scaler', MinMaxScaler()),\n",
            "                ('model',\n",
            "                 RandomForestClassifier(n_estimators=50, random_state=42))]) with accuracy 72.6 %\n"
          ]
        }
      ]
    }
  ]
}