implementation_plan.md

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.