{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aifgSPecKkfY",
        "outputId": "9db7f3b7-2a36-42b6-8eb3-6ca07425437d"
      },
      "id": "aifgSPecKkfY",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aacf5211",
      "metadata": {
        "id": "aacf5211"
      },
      "source": [
        "###Importing Liberaries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "24577b88",
      "metadata": {
        "id": "24577b88"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d70990dc",
      "metadata": {
        "id": "d70990dc"
      },
      "source": [
        "### Data Load"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3de86ddb",
      "metadata": {
        "id": "3de86ddb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "outputId": "05c87a38-574b-4a6d-bb07-1edd7a9afd42"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: '/content/drive/MyDrive/heart_failure_clinical_records_dataset.csv'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/tmp/ipython-input-4048807198.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'/content/drive/MyDrive/heart_failure_clinical_records_dataset.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1024\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1028\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    618\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 620\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1618\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1619\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1620\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1622\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1878\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1879\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1880\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1881\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1882\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    871\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    872\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 873\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    874\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    875\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/drive/MyDrive/heart_failure_clinical_records_dataset.csv'"
          ]
        }
      ],
      "source": [
        "data = pd.read_csv(r'/content/drive/MyDrive/heart_failure_clinical_records_dataset.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Exploratory"
      ],
      "metadata": {
        "id": "P20f_aZ0nanU"
      },
      "id": "P20f_aZ0nanU"
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "R0JxTMpInaUs"
      },
      "id": "R0JxTMpInaUs",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c7f83776",
      "metadata": {
        "id": "c7f83776"
      },
      "outputs": [],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ac3d6a1e",
      "metadata": {
        "id": "ac3d6a1e"
      },
      "outputs": [],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e754b5e8",
      "metadata": {
        "id": "e754b5e8"
      },
      "outputs": [],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e95bcd68",
      "metadata": {
        "id": "e95bcd68"
      },
      "outputs": [],
      "source": [
        "data.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2ce23598",
      "metadata": {
        "id": "2ce23598"
      },
      "outputs": [],
      "source": [
        "labels = [\"40-45\", \"46-50\", \"51-55\", \"56-60\", \"61-65\", \"66-70\", \"71-75\", \"76-80\", \"81-95\"]\n",
        "data['age_group'] = pd.cut(data['age'], bins=[40, 45, 50, 55, 60, 65, 70, 75, 80, 95], labels=labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "852a3203",
      "metadata": {
        "id": "852a3203"
      },
      "source": [
        "### Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fc5f6131",
      "metadata": {
        "id": "fc5f6131"
      },
      "outputs": [],
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.countplot(data=data, x='age_group', hue='DEATH_EVENT', palette=[\"lightblue\", \"red\"])\n",
        "plt.title(\"Death Count by Age Group\")\n",
        "plt.xlabel(\"Age Group\")\n",
        "plt.ylabel(\"Patient Count\")\n",
        "plt.legend([\"Survived\", \"Died\"])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix = data.drop(columns=['age_group']).corr()\n",
        "plt.figure(figsize=(12, 10))\n",
        "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
        "plt.title('Correlation Matrix of Heart Failure Clinical Records')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "687Lx-xInvLN"
      },
      "id": "687Lx-xInvLN",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "death_counts = data['DEATH_EVENT'].value_counts()\n",
        "plt.figure(figsize=(6, 6))\n",
        "plt.pie(death_counts, labels=['Not Died', 'Died'], autopct='%1.1f%%', startangle=90, colors=['skyblue', 'lightcoral'])\n",
        "plt.title('Distribution of DEATH_EVENT')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "CFGNvM9un7CB"
      },
      "id": "CFGNvM9un7CB",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select a subset of numerical features that showed some correlation with DEATH_EVENT\n",
        "selected_features = ['time', 'serum_creatinine', 'ejection_fraction', 'age', 'serum_sodium', 'DEATH_EVENT']\n",
        "\n",
        "sns.pairplot(data[selected_features], hue='DEATH_EVENT', diag_kind='kde')\n",
        "plt.suptitle('Pairplot of Selected Numerical Features by DEATH_EVENT', y=1.02)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "akxmasIGn_Ps"
      },
      "id": "akxmasIGn_Ps",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "lAmTgq0AoJbP"
      },
      "id": "lAmTgq0AoJbP"
    },
    {
      "cell_type": "markdown",
      "id": "6318b50d",
      "metadata": {
        "id": "6318b50d"
      },
      "source": [
        "### Data Split\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f9bbf4a6",
      "metadata": {
        "id": "f9bbf4a6"
      },
      "outputs": [],
      "source": [
        "data.drop(columns=['age_group'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "67245c6b",
      "metadata": {
        "id": "67245c6b"
      },
      "outputs": [],
      "source": [
        "X = data.drop('DEATH_EVENT', axis=1)\n",
        "y = data['DEATH_EVENT']\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Feature Scaling"
      ],
      "metadata": {
        "id": "9RC0CaRQoPSL"
      },
      "id": "9RC0CaRQoPSL"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eff46e4d",
      "metadata": {
        "id": "eff46e4d"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "continuous_features = ['age', 'creatinine_phosphokinase', 'ejection_fraction', 'platelets', 'serum_creatinine', 'serum_sodium', 'time']\n",
        "X_train[continuous_features] = scaler.fit_transform(X_train[continuous_features])\n",
        "X_test[continuous_features] = scaler.transform(X_test[continuous_features])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Modeling"
      ],
      "metadata": {
        "id": "RgfpGCrFoYYo"
      },
      "id": "RgfpGCrFoYYo"
    },
    {
      "cell_type": "markdown",
      "id": "c6c49e64",
      "metadata": {
        "id": "c6c49e64"
      },
      "source": [
        "### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c65331a8",
      "metadata": {
        "id": "c65331a8"
      },
      "outputs": [],
      "source": [
        "log_params = {\n",
        "    'penalty': ['l1', 'l2', 'elasticnet', 'none'],\n",
        "    'C': [0.01, 0.1, 1, 10, 100],\n",
        "    'solver': ['lbfgs', 'saga'],\n",
        "    'max_iter': [1000]\n",
        "}\n",
        "\n",
        "log_grid = GridSearchCV(LogisticRegression(random_state=42), log_params, cv=5)\n",
        "log_grid.fit(X_train, y_train)\n",
        "\n",
        "print(\" Logistic Regression Best Params:\", log_grid.best_params_)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Evaluation"
      ],
      "metadata": {
        "id": "A7F1ne-9okC3"
      },
      "id": "A7F1ne-9okC3"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bb425d64",
      "metadata": {
        "id": "bb425d64"
      },
      "outputs": [],
      "source": [
        "log_model = LogisticRegression(\n",
        "    penalty='l2',\n",
        "    C=0.1,\n",
        "    solver='lbfgs',\n",
        "    max_iter=1000,\n",
        "    random_state=42\n",
        ")\n",
        "log_model.fit(X_train, y_train)\n",
        "y_pred_log = log_model.predict(X_test)\n",
        "print(\" Logistic Regression\")\n",
        "print(f\"Accuracy: {accuracy_score(y_test, y_pred_log):.4f}\")\n",
        "print(classification_report(y_test, y_pred_log))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9ec5c7bd",
      "metadata": {
        "id": "9ec5c7bd"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "355a5349",
      "metadata": {
        "id": "355a5349"
      },
      "outputs": [],
      "source": [
        "rf_params = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'max_depth': [None, 5, 10],\n",
        "    'min_samples_split': [2, 5],\n",
        "    'min_samples_leaf': [1, 2]\n",
        "}\n",
        "\n",
        "rf_grid = GridSearchCV(RandomForestClassifier(random_state=42), rf_params, cv=5)\n",
        "rf_grid.fit(X_train, y_train)\n",
        "\n",
        "print(\" Random Forest Best Params:\", rf_grid.best_params_)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Evaluation"
      ],
      "metadata": {
        "id": "ZgnqGv2_onMp"
      },
      "id": "ZgnqGv2_onMp"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7a814143",
      "metadata": {
        "id": "7a814143"
      },
      "outputs": [],
      "source": [
        "rf_model = RandomForestClassifier(\n",
        "    n_estimators=50, max_depth=5,\n",
        "    min_samples_leaf=2, min_samples_split=5,\n",
        "    random_state=42\n",
        ")\n",
        "rf_model.fit(X_train, y_train)\n",
        "y_pred_rf = rf_model.predict(X_test)\n",
        "print(\" Random Forest\")\n",
        "print(f\"Accuracy: {accuracy_score(y_test, y_pred_rf):.4f}\")\n",
        "print(classification_report(y_test, y_pred_rf))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8ae23a4c",
      "metadata": {
        "id": "8ae23a4c"
      },
      "source": [
        "### SVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "98d79b19",
      "metadata": {
        "id": "98d79b19"
      },
      "outputs": [],
      "source": [
        "svm_params = {\n",
        "    'kernel': ['linear', 'rbf'],\n",
        "    'C': [0.1, 1, 10],\n",
        "    'gamma': ['scale', 'auto']\n",
        "}\n",
        "\n",
        "svm_grid = GridSearchCV(SVC(probability=True, random_state=42), svm_params, cv=5)\n",
        "svm_grid.fit(X_train, y_train)\n",
        "\n",
        "print(\" SVM Best Params:\", svm_grid.best_params_)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluation"
      ],
      "metadata": {
        "id": "lGcRpN66oqox"
      },
      "id": "lGcRpN66oqox"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5b3f845f",
      "metadata": {
        "id": "5b3f845f"
      },
      "outputs": [],
      "source": [
        "svm_model = SVC(\n",
        "    C=0.1, gamma='scale', kernel='linear',\n",
        "    probability=True, random_state=42\n",
        ")\n",
        "svm_model.fit(X_train, y_train)\n",
        "y_pred_svm = svm_model.predict(X_test)\n",
        "print(\"\\n SVM\")\n",
        "print(f\"Accuracy: {accuracy_score(y_test, y_pred_svm):.4f}\")\n",
        "print(classification_report(y_test, y_pred_svm))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "397c4db9",
      "metadata": {
        "id": "397c4db9"
      },
      "source": [
        "### MLP"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "161c3769",
      "metadata": {
        "id": "161c3769"
      },
      "outputs": [],
      "source": [
        "mlp_params = {\n",
        "    'hidden_layer_sizes': [(64,), (64, 32), (128, 64)],\n",
        "    'activation': ['relu', 'tanh'],\n",
        "    'alpha': [0.0001, 0.001],\n",
        "    'learning_rate': ['constant', 'adaptive']\n",
        "}\n",
        "\n",
        "mlp_grid = GridSearchCV(MLPClassifier(max_iter=1000, random_state=42), mlp_params, cv=5)\n",
        "mlp_grid.fit(X_train, y_train)\n",
        "\n",
        "print(\" MLP Best Params:\", mlp_grid.best_params_)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluation"
      ],
      "metadata": {
        "id": "xP9abpojovRZ"
      },
      "id": "xP9abpojovRZ"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c3f80cb8",
      "metadata": {
        "id": "c3f80cb8"
      },
      "outputs": [],
      "source": [
        "mlp_model = MLPClassifier(\n",
        "    hidden_layer_sizes=(64, 32),\n",
        "    activation='tanh',\n",
        "    alpha=0.0001,\n",
        "    learning_rate='constant',\n",
        "    max_iter=1000,\n",
        "    random_state=42\n",
        ")\n",
        "mlp_model.fit(X_train, y_train)\n",
        "y_pred_mlp = mlp_model.predict(X_test)\n",
        "print(\"\\n MLP Neural Network\")\n",
        "print(f\"Accuracy: {accuracy_score(y_test, y_pred_mlp):.4f}\")\n",
        "print(classification_report(y_test, y_pred_mlp))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "26b1f47b",
      "metadata": {
        "id": "26b1f47b"
      },
      "source": [
        "### XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c2cccaf0",
      "metadata": {
        "id": "c2cccaf0"
      },
      "outputs": [],
      "source": [
        "xgb_params = {\n",
        "    'n_estimators': [50, 100, 200],\n",
        "    'max_depth': [3, 4, 5],\n",
        "    'learning_rate': [0.01, 0.1, 0.2]\n",
        "}\n",
        "\n",
        "xgb_grid = GridSearchCV(\n",
        "    XGBClassifier(use_label_encoder=False, eval_metric='logloss', random_state=42),\n",
        "    xgb_params, cv=5\n",
        ")\n",
        "xgb_grid.fit(X_train, y_train)\n",
        "\n",
        "print(\" XGBoost Best Params:\", xgb_grid.best_params_)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluation"
      ],
      "metadata": {
        "id": "gzj365Wkoyni"
      },
      "id": "gzj365Wkoyni"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "01cefcfa",
      "metadata": {
        "id": "01cefcfa"
      },
      "outputs": [],
      "source": [
        "xgb_model = XGBClassifier(\n",
        "    n_estimators=50,\n",
        "    max_depth=4,\n",
        "    learning_rate=0.2,\n",
        "    use_label_encoder=False,\n",
        "    eval_metric='logloss',\n",
        "    random_state=42\n",
        ")\n",
        "xgb_model.fit(X_train, y_train)\n",
        "y_pred_xgb = xgb_model.predict(X_test)\n",
        "print(\"\\n XGBoost\")\n",
        "print(f\"Accuracy: {accuracy_score(y_test, y_pred_xgb):.4f}\")\n",
        "print(classification_report(y_test, y_pred_xgb))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eecde701",
      "metadata": {
        "id": "eecde701"
      },
      "source": [
        "### KNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "985c647f",
      "metadata": {
        "id": "985c647f"
      },
      "outputs": [],
      "source": [
        "knn_params = {\n",
        "    'n_neighbors': [3, 5, 7, 9],\n",
        "    'weights': ['uniform', 'distance'],\n",
        "    'metric': ['euclidean', 'manhattan']\n",
        "}\n",
        "\n",
        "knn_grid = GridSearchCV(KNeighborsClassifier(), knn_params, cv=5)\n",
        "knn_grid.fit(X_train, y_train)\n",
        "\n",
        "print(\" KNN Best Params:\", knn_grid.best_params_)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Evaluation"
      ],
      "metadata": {
        "id": "20E5x9Rmo3Le"
      },
      "id": "20E5x9Rmo3Le"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a5f50c88",
      "metadata": {
        "id": "a5f50c88"
      },
      "outputs": [],
      "source": [
        "knn_model = KNeighborsClassifier(\n",
        "    n_neighbors=5,\n",
        "    weights='uniform',\n",
        "    metric='euclidean'\n",
        ")\n",
        "knn_model.fit(X_train, y_train)\n",
        "y_pred_knn = knn_model.predict(X_test)\n",
        "print(\"\\n KNN\")\n",
        "print(f\"Accuracy: {accuracy_score(y_test, y_pred_knn):.4f}\")\n",
        "print(classification_report(y_test, y_pred_knn))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "658b2f4c",
      "metadata": {
        "id": "658b2f4c"
      },
      "source": [
        "### Models Accuracies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8eb234da",
      "metadata": {
        "id": "8eb234da"
      },
      "outputs": [],
      "source": [
        "models = [\n",
        "    'Random Forest', 'SVM', 'MLP',\n",
        "    'XGBoost', 'KNN', 'Logistic Regression'\n",
        "]\n",
        "accuracies = [\n",
        "    0.85, 0.8333, 0.6833,\n",
        "    0.8333, 0.7167, 0.8333\n",
        "]\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(models, accuracies, color=['blue', 'green', 'purple', 'orange', 'red', 'cyan'])\n",
        "plt.ylim(0, 1)\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Model Accuracy Comparison')\n",
        "\n",
        "plt.xticks(rotation=30)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0ef22c6c",
      "metadata": {
        "id": "0ef22c6c"
      },
      "outputs": [],
      "source": [
        "import gradio as gr\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import joblib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "28aa35d9",
      "metadata": {
        "id": "28aa35d9"
      },
      "outputs": [],
      "source": [
        "joblib.dump(rf_model, \"heart_model.pkl\")\n",
        "joblib.dump(scaler, \"scaler.pkl\")\n",
        "print(\"Model and scaler saved successfully\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "165b4cab",
      "metadata": {
        "id": "165b4cab"
      },
      "outputs": [],
      "source": [
        "model = joblib.load(\"heart_model.pkl\")\n",
        "scaler = joblib.load(\"scaler.pkl\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c41a4646",
      "metadata": {
        "id": "c41a4646"
      },
      "outputs": [],
      "source": [
        "def predict_heart_risk(age, cpk, ef, platelets, sc, ss, time, anaemia, diabetes, high_bp, sex, smoking):\n",
        "    data = pd.DataFrame([[\n",
        "        age, anaemia, cpk, diabetes, ef, high_bp,\n",
        "        platelets, sc, ss, sex, smoking, time\n",
        "    ]], columns=[\n",
        "        'age', 'anaemia', 'creatinine_phosphokinase', 'diabetes',\n",
        "        'ejection_fraction', 'high_blood_pressure', 'platelets',\n",
        "        'serum_creatinine', 'serum_sodium', 'sex', 'smoking', 'time'\n",
        "    ])\n",
        "\n",
        "\n",
        "    continuous_features = ['age', 'creatinine_phosphokinase', 'ejection_fraction','platelets', 'serum_creatinine', 'serum_sodium', 'time']\n",
        "    data[continuous_features] = scaler.transform(data[continuous_features])\n",
        "\n",
        "    prediction = model.predict(data)[0]\n",
        "    return \" At Risk\" if prediction == 1 else \" Not At Risk\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5ca7be47",
      "metadata": {
        "id": "5ca7be47"
      },
      "outputs": [],
      "source": [
        "inputs = [\n",
        "    gr.Number(label=\"Age\"),\n",
        "    gr.Number(label=\"Creatinine Phosphokinase, Range [0,100000]\"),\n",
        "    gr.Number(label=\"Ejection Fraction, Range [5,85] \"),\n",
        "    gr.Number(label=\"Platelets, Range [5000,2000000]\"),\n",
        "    gr.Number(label=\"Serum Creatinine, Range [0.1,60]\"),\n",
        "    gr.Number(label=\"Serum Sodium, Range [95,255]\"),\n",
        "    gr.Number(label=\"Follow-up Time (days)\"),\n",
        "    gr.Radio([0, 1], label=\"Anaemia (0=No, 1=Yes)\"),\n",
        "    gr.Radio([0, 1], label=\"Diabetes (0=No, 1=Yes)\"),\n",
        "    gr.Radio([0, 1], label=\"High Blood Pressure (0=No, 1=Yes)\"),\n",
        "    gr.Radio([0, 1], label=\"Sex (0=Female, 1=Male)\"),\n",
        "    gr.Radio([0, 1], label=\"Smoking (0=No, 1=Yes)\")\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "563bc8b2",
      "metadata": {
        "id": "563bc8b2"
      },
      "outputs": [],
      "source": [
        "gr.Interface(\n",
        "    fn=predict_heart_risk,\n",
        "    inputs=inputs,\n",
        "    outputs=\"text\",\n",
        "    title=\" Heart Failure Risk Predictor\",\n",
        "    description=\"Enter patient data to predict if they are at risk of heart failure.\",\n",
        "    allow_flagging=\"never\"\n",
        ").launch()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OlW7PfhJLXlE"
      },
      "id": "OlW7PfhJLXlE",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.4"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}