| # Implementation Plan - Credit Card Fraud Detection | |
| ## Overview | |
| Build a machine learning pipeline to detect fraudulent credit card transactions and provide a web interface for real-time inference. | |
| ## Tech Stack | |
| - **Dataset**: `creditcard.csv` (provided) | |
| - **ML Framework**: Scikit-learn, Pandas, Numpy, Imbalanced-learn (SMOTE) | |
| - **Model**: Random Forest or XGBoost | |
| - **Backend**: Flask (Python) | |
| - **Frontend**: HTML5, Vanilla CSS (Modern/Premium design), JavaScript | |
| ## Steps | |
| ### 1. Data Preparation & EDA | |
| - Load `creditcard.csv`. | |
| - Analyze class distribution (fraud vs. non-fraud). | |
| - Visualize correlations and feature distributions. | |
| - Check for missing values. | |
| ### 2. Preprocessing | |
| - Scale `Time` and `Amount` features (V1-V28 are already PCA-transformed). | |
| - Split data into training and testing sets. | |
| - Apply SMOTE (Synthetic Minority Over-sampling Technique) to handle class imbalance. | |
| ### 3. Model Engineering | |
| - Train multiple models (Logistic Regression, Random Forest). | |
| - Evaluate using Precision-Recall curves and F1-score. | |
| - Save the best model using `joblib`. | |
| ### 4. Backend (Flask) | |
| - Create an API endpoint `/predict`. | |
| - Load the trained model and scaler. | |
| - Handle POST requests with transaction data. | |
| ### 5. Frontend (Web UI) | |
| - Build a premium, glassmorphic UI. | |
| - Form to input transaction details (or sample details). | |
| - Display prediction result with a visual indicator (Safe vs. Fraud). | |
| ### 6. Deployment Readiness | |
| - Create `requirements.txt`. | |
| - Ensure scripts are well-documented. | |