# 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.