This project aims to develop a credit card fraud detection system using machine learning techniques. By analyzing the "Credit Card Fraud Detection" dataset obtained from Kaggle, which contains transactions made by credit cards in September 2013 by European cardholders.
This project was completed as a minor project in the 3rd semester of MCA at Kalyani University under the supervision of Professor Dr. Kalyani Mali.
Project Author : Mouli Dutta.
The objective of this project is to develop an efficient system that can accurately detect and prevent fraudulent credit card transactions in real-time.
- Python
- Libraries: scikit-learn, pandas, numpy, matplotlib
- Jupyter Notebook
The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) accounts for 0.172% of all transactions.
- Logistic Regression
- Random Forest
- Gradient Boosting
We evaluated the models using the following metrics:
- Accuracy
- Precision
- Recall
- F1 Score
We visualized the confusion matrix for each model to better understand the performance of the classifier.