Project Overview This project aims to address the critical challenge of fraud detection in financial transactions. Utilizing a synthetic financial dataset, the focus is on applying various machine learning techniques to identify and predict fraudulent activities. The dataset, generated by the PaySim mobile money simulator, mimics real-world financial transactions while embedding simulated fraudulent patterns for analysis.
Key Objectives Data Exploration and Preprocessing: Investigate the dataset's characteristics, including cleaning, normalization, and feature selection, to prepare it for machine learning models. Model Comparison and Evaluation: Implement and evaluate multiple machine learning algorithms, including Logistic Regression, Decision Trees, K-Nearest Neighbors, Support Vector Machines, Naive Bayes, and Random Forests. The goal is to compare these models in terms of accuracy and efficiency in detecting fraudulent transactions. Hyperparameter Tuning: Utilize techniques like GridSearchCV for optimizing model parameters, ensuring the best possible performance in fraud detection. Model Interpretation and Validation: Interpret the models' results and validate their performance using metrics such as precision, recall, and the F1-score. Tools and Technologies Python: Primary programming language used for data analysis and machine learning. Pandas & NumPy: For data manipulation and numerical computations. Scikit-Learn: For implementing machine learning algorithms. Plotly and Matplotlib: For data visualization. Jupyter Notebook: As the development environment for interactive coding and documentation. Outcomes The project successfully demonstrates the application of various machine learning techniques in the context of financial fraud detection. The comparative analysis of different models provides insights into the effectiveness of each method, contributing valuable knowledge to the domain of financial security.
Usage This repository contains all the code and documentation for the project. The Jupyter Notebook includes detailed comments and explanations for each step, making it accessible for users interested in understanding and exploring machine learning in fraud detection.
Link to dataset - https://www.kaggle.com/datasets/ealaxi/paysim1