FraudDetectivePy

Python-based supervised machine learning project developed as the final project for INFO 6105 (Data Science Eng Methods).

Overview

The goal of this project is twofold: first, to understand how an imbalanced dataset can impact the analysis and results of credit card fraud detection; and second, to evaluate the effectiveness of different classification models and techniques aimed at enhancing the accuracy and reliability of fraud detection systems.

Key Objectives

• Understand the effects of imbalanced datasets on fraud detection analysis.
• Compare Undersampling and Oversampling techniques for addressing data imbalance.
• Benchmark the performance of different classification models.
• Address outliers and normalize features to refine analysis and predictions.

Project Structure

• dataset/: Contains dataset used for the project.
• src/: Contains Python scripts for data preprocessing, model training, and evaluation.
• report/: Stores Report files.

Dependencies

• Python
• NumPy
• Pandas
• Scikit-learn
• xgboost
• Matplotlib
• Seaborn

Usage

1. Clone the repository: git clone https://github.com/Faridghr/FraudDetectivePy.git
2. Navigate to the project directory: cd FraudDetectivePy
3. Install dependencies: pip install -r requirements.txt
4. Download the dataset and extract it to the dataset folder.
5. Run the main script to preprocess data, train models, and evaluate performance: src/FraudDetectivePy.ipynb

Acknowledgements

The dataset used in this project was obtained from Kaggle.