ass8(Classification )

 # Step 1: Import libraries 

import pandas as pd 

import numpy as np 

import seaborn as sns 

import matplotlib.pyplot as plt 

from sklearn.model_selection import train_test_split 

from sklearn.preprocessing import StandardScaler 

from sklearn.metrics import ( 

accuracy_score, recall_score, precision_score, f1_score, 

confusion_matrix, roc_curve, auc 

) 

# Step 2: Upload the dataset 

from google.colab import files 

uploaded = files.upload() 

# Step 3: Load the dataset 

df = pd.read_csv('pima-indians-diabetes.data.csv', header=None) 

df.head() 

# Step 4: Prepare features and labels 

X = df.iloc[:, :-1] 

y = df.iloc[:, -1] 

# Step 5: Normalize and split 

scaler = StandardScaler() 

X_scaled = scaler.fit_transform(X) 

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42) 

# Step 6: Import models 

from sklearn.svm import SVC 

from sklearn.naive_bayes import GaussianNB 

from sklearn.tree import DecisionTreeClassifier 

from sklearn.neighbors import KNeighborsClassifier 

 

models = { 

    "SVM": SVC(probability=True), 

    "Naive Bayes": GaussianNB(), 

    "Decision Tree": DecisionTreeClassifier(), 

    "KNN": KNeighborsClassifier() 

} 

 

metrics = {} 

 

# Step 7: Train and Evaluate 

for name, model in models.items(): 

    model.fit(X_train, y_train) 

    y_pred = model.predict(X_test) 

    y_prob = model.predict_proba(X_test)[:, 1] 

 

    acc = accuracy_score(y_test, y_pred) 

    rec = recall_score(y_test, y_pred) 

    prec = precision_score(y_test, y_pred) 

    f1 = f1_score(y_test, y_pred) 

    cm = confusion_matrix(y_test, y_pred) 

 

    metrics[name] = { 

        "Accuracy": acc, 

        "Recall": rec, 

        "Precision": prec, 

        "F1 Score": f1, 

        "Confusion Matrix": cm, 

        "y_prob": y_prob 

    } 

 

# Step 8: Print metrics 

for name in metrics: 

    print(f"\n    {name}") 

    print(f"Accuracy: {metrics[name]['Accuracy']:.4f}") 

print(f"Recall: {metrics[name]['Recall']:.4f}") 

print(f"Precision: {metrics[name]['Precision']:.4f}") 

print(f"F1 Score: {metrics[name]['F1 Score']:.4f}") 

# Plot all confusion matrices 

plt.figure(figsize=(14, 10)) 

for i, name in enumerate(metrics): 

plt.subplot(2, 2, i+1) 

sns.heatmap(metrics[name]["Confusion Matrix"], annot=True, fmt='d', cmap="Blues") 

plt.title(f"{name} Confusion Matrix") 

plt.xlabel("Predicted") 

plt.ylabel("Actual") 

plt.tight_layout() 

plt.show() 

# ROC Curve comparison 

plt.figure(figsize=(10, 6)) 

for name in models: 

fpr, tpr, _ = roc_curve(y_test, metrics[name]["y_prob"]) 

plt.plot(fpr, tpr, label=f'{name} (AUC = {auc(fpr, tpr):.2f})') 

plt.plot([0, 1], [0, 1], 'k--') 

plt.title("ROC Curve Comparison") 

plt.xlabel("False Positive Rate") 

plt.ylabel("True Positive Rate") 

plt.legend() 

plt.grid(True) 

plt.show()