This is a credit card fraud detection system built using logistic regression. It helps in identifying fraudulent credit card transactions.
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) account for 0.172% of all transactions.
It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.
- Python 3.x
- scikit-learn
- pandas
- numpy
- matplotlib
- seaborn
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Clone the repository:
git clone https://github.com/your_username/credit-card-fraud-detection.git
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Navigate to the project directory:
cd credit-card-fraud-detection -
Install the required dependencies:
pip install pandas sklearn numpy seaborn matplotlib
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Open
credit_card_fraud_detection.ipynbin a Jupyter Notebook environment. -
Follow the instructions provided in the notebook to train the logistic regression model and evaluate its performance.
The dataset used for this project is the [Credit Card Fraud Detection dataset] (https://www.kaggle.com/mlg-ulb/creditcardfraud) from Kaggle.
The model used for this project is Logistic Regression.
The performance of the model is evaluated based on accuracy and F1-score.
This project is licensed under the MIT License - see the LICENSE file for details.