🚨 Credit Card Fraud Detection using Machine Learning 📌 Project Overview
This project builds a fraud detection system using machine learning to identify fraudulent credit card transactions from highly imbalanced data. A Random Forest classifier is trained and evaluated to distinguish between genuine and fraudulent transactions with high reliability.
📊 Dataset
Source: Credit Card Transactions dataset
Samples used: 50,000
Features: 29 anonymized numerical features + Amount
Target: Class
0 → Legitimate transaction
1 → Fraudulent transaction
Class imbalance: ~0.16% fraud cases
⚙️ Workflow
Data loading and exploration
Handling severe class imbalance
Train-test split
Model training using Random Forest
Model evaluation
Feature importance analysis
🤖 Model Used
Random Forest Classifier
Handles non-linear relationships well
Robust to noisy and imbalanced datasets
Provides feature importance for explainability
📈 Model Performance
High precision and recall for fraud class
Effectively minimizes false positives
Captures important transaction patterns
🔍 Feature Importance
Top contributing features were identified using Random Forest feature importance, helping understand which transaction attributes most influence fraud detection.
🧠 Key Learnings
Handling imbalanced datasets is critical in fraud detection
Tree-based models perform well without heavy preprocessing
Feature importance improves model interpretability
🛠 Tech Stack
Python
Pandas, NumPy
Scikit-learn
Matplotlib, Seaborn
Jupyter Notebook
📌 Conclusion
This project demonstrates a practical, industry-relevant approach to fraud detection using machine learning, focusing on performance, interpretability, and business impact.