🕵️‍♀️ Fraud Detection using Machine Learning

This project implements a machine learning model to detect fraudulent transactions, helping financial institutions prevent losses and improve trust. It uses supervised learning techniques on transaction data to identify suspicious activity.

🚀 Features

• Preprocessing of imbalanced transaction datasets
• Training and evaluation of models like Logistic Regression, Decision Trees, and Random Forest
• Performance metrics: Accuracy, Precision, Recall, F1-score
• Visualization of results and confusion matrix

🧰 Tech Stack

• Python 3
• pandas, NumPy
• scikit-learn
• matplotlib, seaborn

🗂️ Project Structure

• fraud_detection.ipynb: Main notebook with data loading, training, and evaluation
• data/: Dataset files (if available)
• README.md: Project overview and instructions

⚙️ How to Run

1. Clone the repo:

   git clone https://github.com/Shakshi-das/Fraud-detection.git
   cd Fraud-detection

2. Open the Jupyter Notebook:

   jupyter notebook fraud_detection.ipynb

📈 Future Improvements

• Use of advanced models (XGBoost, LightGBM)
• Deployment as an API for real-time detection
• Integration with dashboards