This project aims to build a robust machine learning model for detecting fraudulent credit card transactions. The model is trained on a dataset containing credit card transactions labeled as fraudulent or legitimate. By analyzing various features and patterns in the data, the model can effectively identify potential fraudulent activities.
The credit card fraud detection system is developed using Python and leverages popular machine learning libraries. The project follows a structured approach, including data exploration, preprocessing, model training, evaluation, and deployment. The main steps involved are:
- Data Exploration: Analyzing the dataset to understand its characteristics, such as imbalanced data distribution and feature correlations.
- Data Preprocessing: Handling missing values, scaling features, and separating input and target variables.
- Model Training: Training a Random Forest Classifier on the preprocessed data.
- Model Evaluation: Assessing the model's performance using metrics like accuracy, precision, recall, F1-score, and Matthews correlation coefficient.
- Visualization: Plotting the confusion matrix to visualize the model's predictions.
The project utilizes a credit card fraud detection dataset available on Kaggle. The dataset consists of credit card transactions, with the target variable indicating whether a transaction is fraudulent or legitimate.
The following Python libraries are utilized in this project:
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-learn
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Clone the repository: git clone https://github.com/R4hulR/FraudDetectionML.git
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Install the required libraries: pip install -r requirements.txt
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Download the dataset from Kaggle and place it in the appropriate directory.
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Run the Jupyter Notebook or Python script to execute the project.
Contributions to this project are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.
- The dataset used in this project is sourced from Kaggle.
- The project is inspired by the tutorial on GeeksforGeeks.
This project is licensed under the MIT License.