Credit card fraud is a significant issue in financial institutions worldwide. This project implements a logistic regression model to identify fraudulent transactions from a dataset of credit card transactions. The main objectives are:
To build a logistic regression model to classify transactions as fraudulent or non-fraudulent. To evaluate the performance of the model using appropriate metrics.
The dataset used in this project is the Credit Card Fraud Detection Dataset available on Kaggle. The dataset contains 284,807 transactions, of which 492 are fraudulent. The dataset is highly imbalanced, with fraudulent transactions accounting for only 0.172% of all transactions.
Python Streamlit scikit-learn
Taken sample of equal number of data points of legit transaction and fraud transactions.
The logistic regression model is used to classify the legit and fraud transactions based on the provided parameters. The model is trained using the LogisticRegression class from the scikit-learn library.
The model achieves an accuracy of approximately 91.91% on the test set.
Contributions are welcome! Please fork this repository and create a pull request with your changes. Ensure your code follows the project's coding standards and include relevant tests.
The application is deployed using Streamlit. You can access it here = https://ml-project-10-credit-card-fraud-detection-7xxqchevqpmzgdq5i9s6.streamlit.app/
If you have any questions or suggestions, feel free to contact me at [email protected] .