This project implements a logistic regression model to detect credit card fraud transactions. The model is trained on a dataset of credit card transactions with both fraudulent and non-fraudulent transactions, and is able to predict the likelihood of a transaction being fraudulent.
To install the necessary libraries, run:
pip install -r requirements.txt The dataset used in this project is the Credit Card Fraud Detection dataset from Kaggle. This dataset contains 284,807 transactions, with 492 fraudulent transactions and 284,315 non-fraudulent transactions. The dataset is highly imbalanced, with fraudulent transactions accounting for only 0.172% of all transactions.
The logistic regression model was trained on the preprocessed data using scikit-learn's LogisticRegression class. The model was evaluated using metrics such as accuracy, precision, recall, and F1 score.
The logistic regression model achieved an accuracy of 0.999, precision of 0.892, recall of 0.647, and F1 score of 0.751 on the test set.