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Problem Description

This project implements a fraud detection model to identify fraudulent transactions in a dataset. The model is trained to classify transactions as either fraudulent (FRAUDE=1) or legitimate (FRAUDE=0).

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Model Architecture

The model uses a RandomForestClassifier with the following configuration:
  • Algorithm: Random Forest (ensemble of decision trees)
  • Number of estimators: 200 trees
  • Class weight: Balanced (to handle class imbalance)
  • Random state: 42 (for reproducibility)

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Key Features

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Handling Class Imbalance

The dataset exhibits class imbalance between fraudulent and legitimate transactions. To address this:
  • class_weight='balanced' automatically adjusts weights inversely proportional to class frequencies
  • This ensures the model doesn’t bias toward the majority class

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Model Performance

On the validation set, the model achieves:
  • Accuracy: 95%
  • AUC-ROC Score: 0.988 (98.8%)
These metrics indicate excellent discriminative power and overall performance.

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Model Initialization Code

from sklearn.ensemble import RandomForestClassifier

# Initialize Random Forest model
model = RandomForestClassifier(
    n_estimators=200,        # Number of trees in the forest
    random_state=42,         # Seed for reproducibility
    class_weight='balanced'  # Adjust weight of classes for imbalance
)

# Train model with training data
model.fit(X_train, y_train)

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Why Random Forest?

Random Forest was chosen for this fraud detection task because:
  1. Robustness: Handles both numerical and categorical features well
  2. Non-linearity: Captures complex patterns in transaction data
  3. Overfitting resistance: Ensemble approach reduces variance
  4. Feature importance: Provides insights into which features drive predictions
  5. Class imbalance handling: Built-in support through class_weight parameter

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Next Steps

To understand how the data is prepared for this model, see the Data Preprocessing page. For details on the training process, see Model Training. For performance metrics and interpretation, see Evaluation.

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