💳 Credit Card Fraud Detection using Machine Learning

Detecting fraudulent credit card transactions using Logistic Regression, SMOTE balancing, and performance evaluation techniques. Includes an interactive web app built using Streamlit.

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🚀 Project Overview

This project aims to identify fraudulent transactions in credit card data. Due to the rarity of fraud cases, the dataset is highly imbalanced — a challenge tackled with SMOTE (Synthetic Minority Oversampling Technique). The model is deployed as a web app using Streamlit for easy interaction and real-time testing.

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📂 Folder Structure

CreditCardFraudDetection/ │ ├── data/ │ └── creditcard.csv # Dataset from Kaggle ├── fraud_detection.py # CLI model training and evaluation ├── fraud_webapp.py # Streamlit interactive web app ├── requirements.txt # All dependencies └── README.md # Project documentation (this file)

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🧠 Key Features

• ✅ Data preprocessing and cleaning
• ⚖️ Imbalanced data handling using SMOTE
• 📈 Model training using Logistic Regression
• 📊 Performance metrics: Confusion Matrix, F1-Score, ROC-AUC
• 🧪 Real-time transaction fraud simulation
• 🌐 Interactive web app with user controls (Streamlit)

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📊 Dataset

• Source: Kaggle - Credit Card Fraud Detection
• Size: 284,807 transactions
• Features: V1–V28 (PCA anonymized), Time, Amount, and Class

Class labels:

• 0 = Legit
• 1 = Fraud

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📈 Model Performance (Logistic Regression)

Metric	Value
Precision	0.76
Recall	0.86
F1-Score	0.81
ROC-AUC Score	0.9833 ✅

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🖥️ How to Run Locally

📌 Clone the Repo

git clone https://github.com/yourusername/CreditCardFraudDetection.git
cd CreditCardFraudDetection

for requirment 
pip install -r requirements.txt

for streamlit 
streamlit run fraud_webapp.py

for venv
python -m venv venv
venv\Scripts\activate