This repository contains the implementation of a machine learning pipeline for detecting fraudulent credit card transactions. The project leverages common data science libraries to preprocess data, train multiple models, and evaluate their performance using appropriate classification metrics.
Note: The dataset used for this project is not included in the repository due to its large size. However, you can download a similar publicly available dataset from Kaggle - Credit Card Fraud Detection.
Fraud detection is a critical task in the financial sector. This project demonstrates building a robust fraud detection model using supervised learning techniques. It includes the following components:
- Data preprocessing
- Exploratory Data Analysis (EDA)
- Model training using Random Forests
- Model evaluation with metrics like accuracy, precision, recall, F1-score, and MCC
- Hyperparameter tuning and cross-validation
This project is licensed under the MIT License. You can use, modify, and distribute this code with proper attribution.