Project_Logistic_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8680405",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\ashwi\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [[ 0.6340052   0.56816143 -0.06708981  0.06527001  0.02304432  0.11045673\n",
      "   0.14707302 -0.03614445 -0.07748933 -0.04065042 -0.54464683 -0.08884577\n",
      "  -0.0677957   0.54061164 -0.00368717 -0.00592924  0.08578298  0.20933969\n",
      "   0.17583232 -0.06111436 -0.03086564]]\n",
      "Intercept: [-6.8302952]\n",
      "Test Accuracy is: 0.8645537685272785\n",
      "\n",
      "Logistic Regression Best Hyperparameters: {'C': 0.001}\n",
      "\n",
      "Top contributing features (Logistic Regression):\n",
      "                 Feature  Coefficient\n",
      "0                 HighBP     0.634005\n",
      "1               HighChol     0.568161\n",
      "13               GenHlth     0.540612\n",
      "17                   Sex     0.209340\n",
      "18                   Age     0.175832\n",
      "6   HeartDiseaseorAttack     0.147073\n",
      "5                 Stroke     0.110457\n",
      "16              DiffWalk     0.085783\n",
      "3                    BMI     0.065270\n",
      "4                 Smoker     0.023044\n",
      "14              MentHlth    -0.003687\n",
      "15              PhysHlth    -0.005929\n",
      "20                Income    -0.030866\n",
      "7           PhysActivity    -0.036144\n",
      "9                Veggies    -0.040650\n",
      "19             Education    -0.061114\n",
      "2              CholCheck    -0.067090\n",
      "12           NoDocbcCost    -0.067796\n",
      "8                 Fruits    -0.077489\n",
      "11         AnyHealthcare    -0.088846\n",
      "10     HvyAlcoholConsump    -0.544647\n",
      "Coefficients: [[1.08496285 0.65151019 0.32015078 0.57650304]]\n",
      "Intercept: [-2.83941123]\n",
      "Test Accuracy is: 0.8620440449910649\n",
      "\n",
      "Logistic Regression (Medical Conditions) Best Hyperparameters: {'C': 0.001}\n",
      "\n",
      "Top contributing features (Logistic Regression - Medical Conditions):\n",
      "                Feature  Coefficient\n",
      "0                HighBP     1.084963\n",
      "1              HighChol     0.651510\n",
      "3  HeartDiseaseorAttack     0.576503\n",
      "2                Stroke     0.320151\n",
      "Coefficients: [[ 0.07364474  0.28707846 -0.40515989 -0.02815141 -0.18436357 -0.38046698]]\n",
      "Intercept: [-3.6543608]\n",
      "Test Accuracy is: 0.8597051403342794\n",
      "\n",
      "Logistic Regression (Lifestyle Habits) Best Hyperparameters: {'C': 0.001}\n",
      "\n",
      "Top contributing features (Logistic Regression - Lifestyle Habits):\n"
     ]
    }
   ],
   "source": [
    "    \n",
    "# Method 1\n",
    "\n",
    "# Method 1: Logistic Regression\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# Load the dataset\n",
    "#url = \"/Users/sivasankaranrajasekaran/Desktop/UTD/AML/Project/archive (5)/diabetes_binary_health_indicators_BRFSS2015.csv\"\n",
    "df = pd.read_csv(\"Diabetes_dataset.csv\")\n",
    "\n",
    "# Display the first few rows of the dataset\n",
    "#print(df.head())\n",
    "\n",
    "# Define features and target variable\n",
    "X = df.drop(\"Diabetes_binary\", axis=1)\n",
    "y = df[\"Diabetes_binary\"]\n",
    "\n",
    "# Data Splitting\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Create and train the Logistic Regression model\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "# Hyperparameter tuning using GridSearchCV\n",
    "param_grid_logreg = {'C': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "grid_logreg = GridSearchCV(logreg, param_grid_logreg, cv=5)\n",
    "grid_logreg.fit(X_train, y_train)\n",
    "\n",
    "y_pred_grid_test = grid_logreg.predict(X_test)\n",
    "\n",
    "# Report coefficients and intercept\n",
    "print(\"Coefficients:\", grid_logreg.best_estimator_.coef_)\n",
    "print(\"Intercept:\", grid_logreg.best_estimator_.intercept_)\n",
    "\n",
    "# Calculate and report the accuracy\n",
    "print(\"Test Accuracy is:\", grid_logreg.best_estimator_.score(X_test, y_test))\n",
    "\n",
    "# Report the best hyperparameters\n",
    "print(\"\\nLogistic Regression Best Hyperparameters:\", grid_logreg.best_params_)\n",
    "\n",
    "# Get feature names\n",
    "feature_names_logreg = X.columns\n",
    "\n",
    "# Create a DataFrame to display feature names and their coefficients\n",
    "coef_df_logreg = pd.DataFrame({\"Feature\": feature_names_logreg, \"Coefficient\": grid_logreg.best_estimator_.coef_[0]})\n",
    "coef_df_logreg = coef_df_logreg.sort_values(by=\"Coefficient\", ascending=False)\n",
    "\n",
    "# Display the most important features\n",
    "print(\"\\nTop contributing features (Logistic Regression):\")\n",
    "print(coef_df_logreg)\n",
    "\n",
    "###########################################################################################\n",
    "\n",
    "# Method 2\n",
    "\n",
    "# Method 2: Logistic Regression with Medical Conditions and Lifestyle Habits\n",
    "\n",
    "# Categorize features into \"Medical Conditions\" and \"Lifestyle Habits\"\n",
    "medical_conditions = ['HighBP', 'HighChol', 'Stroke', 'HeartDiseaseorAttack']\n",
    "lifestyle_habits = ['BMI', 'Smoker', 'PhysActivity', 'Fruits', 'Veggies', 'HvyAlcoholConsump']\n",
    "\n",
    "# Select features for each category\n",
    "X_medical_conditions = X[medical_conditions]\n",
    "X_lifestyle_habits = X[lifestyle_habits]\n",
    "\n",
    "# Data Splitting for Medical Conditions\n",
    "X_train_medical, X_test_medical, y_train, y_test = train_test_split(X_medical_conditions, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Create and train the Logistic Regression model for Medical Conditions\n",
    "logreg_medical = LogisticRegression()\n",
    "\n",
    "# Hyperparameter tuning using GridSearchCV\n",
    "param_grid_logreg_medical = {'C': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "grid_logreg_medical = GridSearchCV(logreg_medical, param_grid_logreg_medical, cv=5)\n",
    "grid_logreg_medical.fit(X_train_medical, y_train)\n",
    "\n",
    "y_pred_grid_test_medical = grid_logreg_medical.predict(X_test_medical)\n",
    "\n",
    "# Report coefficients and intercept\n",
    "print(\"Coefficients:\", grid_logreg_medical.best_estimator_.coef_)\n",
    "print(\"Intercept:\", grid_logreg_medical.best_estimator_.intercept_)\n",
    "\n",
    "# Calculate and report the accuracy\n",
    "print(\"Test Accuracy is:\", grid_logreg_medical.best_estimator_.score(X_test_medical, y_test))\n",
    "\n",
    "# Report the best hyperparameters\n",
    "print(\"\\nLogistic Regression (Medical Conditions) Best Hyperparameters:\", grid_logreg_medical.best_params_)\n",
    "\n",
    "# Get feature names\n",
    "feature_names_medical = X_medical_conditions.columns\n",
    "\n",
    "# Create a DataFrame to display feature names and their coefficients for Medical Conditions\n",
    "coef_df_medical = pd.DataFrame({\"Feature\": feature_names_medical, \"Coefficient\": grid_logreg_medical.best_estimator_.coef_[0]})\n",
    "coef_df_medical = coef_df_medical.sort_values(by=\"Coefficient\", ascending=False)\n",
    "\n",
    "# Display the most important features for Medical Conditions\n",
    "print(\"\\nTop contributing features (Logistic Regression - Medical Conditions):\")\n",
    "print(coef_df_medical)\n",
    "\n",
    "# Data Splitting for Lifestyle Habits\n",
    "X_train_lifestyle, X_test_lifestyle, y_train, y_test = train_test_split(X_lifestyle_habits, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Create and train the Logistic Regression model for Lifestyle Habits\n",
    "logreg_lifestyle = LogisticRegression()\n",
    "\n",
    "# Hyperparameter tuning using GridSearchCV\n",
    "param_grid_logreg_lifestyle = {'C': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "grid_logreg_lifestyle = GridSearchCV(logreg_lifestyle, param_grid_logreg_lifestyle, cv=5)\n",
    "grid_logreg_lifestyle.fit(X_train_lifestyle, y_train)\n",
    "\n",
    "y_pred_grid_test_lifestyle = grid_logreg_lifestyle.predict(X_test_lifestyle)\n",
    "\n",
    "# Report coefficients and intercept\n",
    "print(\"Coefficients:\", grid_logreg_lifestyle.best_estimator_.coef_)\n",
    "print(\"Intercept:\", grid_logreg_lifestyle.best_estimator_.intercept_)\n",
    "\n",
    "# Calculate and report the accuracy\n",
    "print(\"Test Accuracy is:\", grid_logreg_lifestyle.best_estimator_.score(X_test_lifestyle, y_test))\n",
    "\n",
    "\n",
    "# Report the best hyperparameters\n",
    "print(\"\\nLogistic Regression (Lifestyle Habits) Best Hyperparameters:\", grid_logreg_lifestyle.best_params_)\n",
    "\n",
    "# Get feature names\n",
    "feature_names_lifestyle = X_lifestyle_habits.columns\n",
    "\n",
    "# Create a DataFrame to display feature names and their coefficients for Lifestyle Habits\n",
    "#coef_df_lifestyle = pd.DataFrame({\"Feature\": feature_names_lifestyle, \"Coefficient\": grid_logreg_lifestyle.best_estimator_.coef_[0]})\n",
    "#coef_df_lifestyle = coef_df_lifestyle.sort_values(by=\"Coefficient\", ascending=False)\n",
    "\n",
    "# Display the most important features for Lifestyle Habits\n",
    "print(\"\\nTop contributing features (Logistic Regression - Lifestyle Habits):\")\n",
    "#print(coef_df_lifestyle)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "575a3510",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[64179  1426]\n",
      " [ 8882  1617]]\n",
      "Test accuracy is 0.8645537685272785\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "y_pred = grid_logreg.predict(X_test)\n",
    "accuracy = grid_logreg.score(X_test, y_test)\n",
    "confusion_mat = confusion_matrix(y_test, y_pred)\n",
    " \n",
    "print(f'Confusion Matrix:\\n{confusion_mat}')\n",
    "print(\"Test accuracy is\", accuracy)\n",
    " \n",
    "# Logistic Regression - Metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9330de34",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "822ee1a5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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