Model Evaluation & Validation

Load and Preprocess Data

Create a function load_and_preprocess(filepath) that:

• Loads the CSV file and handles missing values
• Scales numerical features using StandardScaler
• Splits into features (X) and target (y)
• Returns X, y, and feature names

def load_and_preprocess(filepath):
    """Load data and prepare for modeling."""
    # Must use: StandardScaler for numerical features
    # Return: X (scaled features), y (target), feature_names
    pass

Create Stratified Train-Test Split

Create a function create_train_test_split(X, y, test_size=0.2, stratify=True) that:

• Uses stratified sampling if stratify=True to preserve class distribution
• Sets random_state for reproducibility
• Prints class distribution before and after split
• Returns X_train, X_test, y_train, y_test

def create_train_test_split(X, y, test_size=0.2, stratify=True):
    """Create stratified train-test split."""
    # Must use: train_test_split with stratify parameter
    pass

Perform Cross-Validation

Create a function perform_cross_validation(model, X, y, cv=5) that:

• Uses StratifiedKFold for imbalanced data
• Calculates multiple metrics per fold (accuracy, precision, recall, F1, ROC-AUC)
• Returns mean and std for each metric
• Prints formatted results table

def perform_cross_validation(model, X, y, cv=5):
    """Perform stratified k-fold cross-validation."""
    # Must use: StratifiedKFold, cross_val_score or manual fold iteration
    # Return: dict with metric names and (mean, std) tuples
    pass

Calculate Comprehensive Metrics

Create a function calculate_metrics(y_true, y_pred, y_proba=None) that:

• Calculates accuracy, precision, recall, F1-score
• Calculates ROC-AUC if probabilities provided
• Calculates Matthews Correlation Coefficient (MCC)
• Returns dictionary of all metrics

def calculate_metrics(y_true, y_pred, y_proba=None):
    """Calculate comprehensive classification metrics."""
    # Must include: accuracy, precision, recall, f1, roc_auc, mcc
    pass

Plot Confusion Matrix

Create a function plot_confusion_matrix(y_true, y_pred, model_name) that:

• Uses seaborn heatmap with annotations
• Shows both counts and percentages
• Labels axes clearly (Predicted vs Actual)
• Saves as PNG file

def plot_confusion_matrix(y_true, y_pred, model_name):
    """Create and visualize confusion matrix."""
    # Must use: confusion_matrix from sklearn, seaborn heatmap
    # Save to: confusion_matrices.png
    pass

Plot ROC Curves

Create a function plot_roc_curve(models_dict, X_test, y_test) that:

• Plots ROC curve for each model on the same figure
• Includes diagonal reference line (random classifier)
• Displays AUC in legend for each model
• Saves as roc_curves.png

def plot_roc_curve(models_dict, X_test, y_test):
    """Plot ROC curves for multiple models."""
    # Must use: roc_curve, auc from sklearn
    # Save to: roc_curves.png
    pass

Plot Precision-Recall Curves

Create a function plot_precision_recall_curve(models_dict, X_test, y_test) that:

• Plots PR curve for each model
• Includes baseline (no skill) reference line
• Displays Average Precision in legend
• Saves as pr_curves.png

def plot_precision_recall_curve(models_dict, X_test, y_test):
    """Plot precision-recall curves for multiple models."""
    # Must use: precision_recall_curve, average_precision_score
    # Save to: pr_curves.png
    pass

Compare Models

Create a function compare_models(models_dict, X_train, X_test, y_train, y_test) that:

• Trains each model on training data
• Evaluates on test data using all metrics
• Creates comparison DataFrame sorted by F1-score
• Visualizes comparison as bar chart (saved as model_comparison.png)

def compare_models(models_dict, X_train, X_test, y_train, y_test):
    """Train and compare multiple models."""
    # Evaluate: LogisticRegression, RandomForest, XGBoost, SVM (minimum)
    # Save to: model_comparison.png
    pass

Perform Grid Search

Create a function perform_grid_search(model, param_grid, X_train, y_train, cv=5) that:

• Uses StratifiedKFold for cross-validation
• Optimizes for F1-score (better for imbalanced data)
• Returns best model and best parameters
• Prints search results summary

def perform_grid_search(model, param_grid, X_train, y_train, cv=5):
    """Perform hyperparameter tuning with GridSearchCV."""
    # Must use: GridSearchCV with scoring='f1'
    # Return: best_model, best_params
    pass

Perform Randomized Search

Create a function perform_randomized_search(model, param_distributions, X_train, y_train, n_iter=50) that:

• Samples n_iter combinations from parameter distributions
• Uses StratifiedKFold cross-validation
• Compares search time and results with GridSearch
• Returns best model and parameters

Plot Learning Curves

Create a function plot_learning_curve(model, X, y, model_name) that:

• Uses sklearn's learning_curve function
• Plots training and validation scores vs training size
• Adds shaded regions for standard deviation
• Interprets overfitting/underfitting and saves as learning_curves.png

def plot_learning_curve(model, X, y, model_name):
    """Plot learning curves to analyze bias-variance tradeoff."""
    # Must use: learning_curve from sklearn.model_selection
    # Save to: learning_curves.png
    pass

Generate Evaluation Report

Create a function generate_evaluation_report(results, best_model_name) that:

• Summarizes all model performances
• Explains why best model was selected
• Includes trade-off analysis (precision vs recall)
• Writes to evaluation_report.txt

Main Program

Create a main() function that:

• Loads and preprocesses the data
• Creates train-test split
• Evaluates at least 4 different models
• Performs hyperparameter tuning on best model
• Generates all visualizations and reports
• Prints final recommendations

def main():
    # Load and preprocess
    X, y, features = load_and_preprocess("credit_fraud.csv")
    X_train, X_test, y_train, y_test = create_train_test_split(X, y)
    
    # Define models
    models = {
        'Logistic Regression': LogisticRegression(),
        'Random Forest': RandomForestClassifier(),
        'XGBoost': XGBClassifier(),
        'SVM': SVC(probability=True)
    }
    
    # Compare and evaluate
    results = compare_models(models, X_train, X_test, y_train, y_test)
    
    # Tune best model
    best_model = perform_grid_search(...)
    
    # Generate outputs
    generate_evaluation_report(results, best_model_name)
    
    print("Model evaluation complete!")

if __name__ == "__main__":
    main()