08 Logistic_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "04f68a76-0638-4596-ad51-2be478fe7380",
   "metadata": {},
   "source": [
    "# Experiment 8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ff3671-8c3a-410e-b3fc-420218419cf9",
   "metadata": {},
   "source": [
    "## Implement a Python program to perform logistic regression on a dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "70b12cfa-f566-4020-9960-b14156b6b6c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicted vs Actual Values(for 30 data points): \n",
      "----------------------------\n",
      "Predicted: 0.00, Actual: 0.00\n",
      "Predicted: 1.00, Actual: 1.00\n",
      "Predicted: 0.00, Actual: 0.00\n",
      "Predicted: 0.00, Actual: 0.00\n",
      "Predicted: 0.00, Actual: 0.00\n",
      "Predicted: 0.00, Actual: 0.00\n",
      "Predicted: 1.00, Actual: 1.00\n",
      "Predicted: 0.00, Actual: 0.00\n",
      "Predicted: 1.00, Actual: 1.00\n",
      "Predicted: 1.00, Actual: 1.00\n",
      "Predicted: 0.00, Actual: 0.00\n",
      "Predicted: 1.00, Actual: 1.00\n",
      "Predicted: 0.00, Actual: 0.00\n",
      "Predicted: 1.00, Actual: 1.00\n",
      "Predicted: 0.00, Actual: 0.00\n",
      "Predicted: 0.00, Actual: 0.00\n",
      "Predicted: 1.00, Actual: 1.00\n",
      "Predicted: 1.00, Actual: 1.00\n",
      "Predicted: 0.00, Actual: 0.00\n",
      "Predicted: 0.00, Actual: 0.00\n",
      "Predicted: 0.00, Actual: 0.00\n",
      "Predicted: 1.00, Actual: 1.00\n",
      "Predicted: 0.00, Actual: 0.00\n",
      "Predicted: 1.00, Actual: 1.00\n",
      "Predicted: 1.00, Actual: 1.00\n",
      "Predicted: 1.00, Actual: 0.00\n",
      "Predicted: 0.00, Actual: 0.00\n",
      "Predicted: 0.00, Actual: 1.00\n",
      "Predicted: 1.00, Actual: 1.00\n",
      "Predicted: 1.00, Actual: 1.00\n",
      "Confusion Matrix \n",
      " [[135   5]\n",
      " [  7 102]]\n",
      "Accuracy of Model: 95.181%\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter a Value for Glucose:  80\n",
      "Enter a Value for Blood Pressure:  90\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Value for Given Values: 0\n",
      "Person is not Diabetic\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\venki\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "\n",
    "# Load the dataset from a CSV file\n",
    "df = pd.read_csv('./00 Lab_Files/Health_Dataset.csv')\n",
    "\n",
    "# Select features (all columns except the last one) and target (the last column)\n",
    "x = df.iloc[:, :2]  # Features (first two columns)\n",
    "y = df.iloc[:, -1]  # Target (the last column)\n",
    "\n",
    "# Split the dataset into training and testing sets (75% train, 25% test)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=40)\n",
    "\n",
    "# Initialize the Logistic Regression model\n",
    "model = LogisticRegression()\n",
    "\n",
    "# Fit the model using the training data\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "# Predict the target for the test data\n",
    "y_pred = model.predict(x_test)\n",
    "\n",
    "# Print the first 30 predicted vs. actual values\n",
    "print(\"\\nPredicted vs Actual Values(for 30 data points):\", '\\n----------------------------')\n",
    "for i in range(30):\n",
    "    print(f\"Predicted: {y_pred[i]:.2f}, Actual: {y_test.iloc[i]:.2f}\")\n",
    "\n",
    "# Compute the confusion matrix to evaluate the accuracy of the classification\n",
    "conf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
    "print('Confusion Matrix \\n', conf_matrix)\n",
    "\n",
    "# Calculate and print the accuracy of the model\n",
    "accuracy = metrics.accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy of Model: {accuracy*100:.3f}%')\n",
    "\n",
    "# Predict value for a new data point\n",
    "print()\n",
    "glu = int(input('Enter a Value for Glucose: '))\n",
    "bp = int(input('Enter a Value for Blood Pressure: '))\n",
    "y1 = model.predict([[glu, bp]])\n",
    "print('Predicted Value for Given Values:', y1[0])\n",
    "\n",
    "# Interpret and print the prediction result\n",
    "pred = 'Person is Diabetic :(' if y1 == 1 else 'Person is not Diabetic'\n",
    "print(pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8320733e-f1f6-426a-a219-d9c2451606c2",
   "metadata": {},
   "source": [
    "## Following are Additional Visualizations. Not Required in the Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "42fd05ee-d225-4894-8144-64e4969e9bc2",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Plot confusion matrix\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ae2b5bb-be78-4b5c-999d-3d550e5317ad",
   "metadata": {},
   "source": [
    "## ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eaddd3eb-30fd-4fdc-807e-377005064402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_proba = model.predict_proba(x_test)[:, 1]\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_proba)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic (ROC)')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d9b31f1-84df-42cf-ad27-db5990853a1f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}