Credit Card Fraud Detection This repository contains a Jupyter Notebook for detecting fraudulent transactions in a credit card dataset using various machine learning techniques. The goal of this project is to build and evaluate models that can effectively identify fraudulent transactions from a dataset of credit card transactions.
Table of Contents Introduction Dataset Installation Usage Features Models and Evaluation Results Contributing License Introduction Credit card fraud detection is a critical task in the finance industry. Fraudulent transactions can cause significant financial losses and compromise the security of financial systems. This project aims to use machine learning algorithms to detect fraudulent transactions and minimize financial losses.
Dataset The dataset used in this project contains credit card transactions over a certain period. The data has been preprocessed to protect user privacy and includes the following features:
Time: Number of seconds elapsed between this transaction and the first transaction in the dataset. V1 to V28: Principal components obtained using PCA to anonymize the data. Amount: Transaction amount. Class: Label where 1 indicates a fraudulent transaction and 0 indicates a legitimate transaction. Installation To run the notebook and reproduce the results, you need to have Python installed along with the following libraries:
numpy pandas matplotlib seaborn scikit-learn imbalanced-learn
You can install these dependencies using pip: pip install numpy pandas matplotlib seaborn scikit-learn imbalanced-learn
Usage Clone this repository git clone https://github.com/yourusername/Credit-Card-Fraud-Detection.git
Navigate to the repository directory: cd Credit-Card-Fraud-Detection
Open the Jupyter Notebook: jupyter notebook "Credit Card Fraud Detection.ipynb"
Run the cells in the notebook to train and evaluate the models. Features Data loading and preprocessing Exploratory data analysis (EDA) Data visualization Handling class imbalance using techniques like SMOTE Building machine learning models (e.g., Logistic Regression, Decision Tree, Random Forest, etc.) Model evaluation using various metrics (e.g., accuracy, precision, recall, F1-score, ROC-AUC)
Models and Evaluation The notebook includes the implementation of several machine learning algorithms to detect fraudulent transactions. The models are evaluated using metrics such as:
Confusion Matrix Precision-Recall Curve ROC Curve and AUC Accuracy, Precision, Recall, and F1-Score Results The results section in the notebook provides a detailed analysis of the model performances. The best-performing model is highlighted, along with its evaluation metrics.
Contributing Contributions are welcome! If you have any suggestions or improvements, please create a pull request or open an issue.
License This project is licensed under the MIT License. See the LICENSE file for more details.