This project dives into the exciting world of machine learning and its application in fraud detection. We analyze and compare various supervised and unsupervised learning algorithms through detailed case studies, using real-world datasets. You'll find Python implementations along with insights into how these models perform when tasked with detecting fraudulent behavior.
The project is divided into three major case studies, each showcasing different machine learning approaches: Random Forest, Comparative Supervised Methods (Logistic Regression, Naive Bayes, and Decision Trees), and Unsupervised Learning with clustering techniques.
This project revolves around three distinct case studies, each focusing on different machine learning techniques.
We explore the power of the Random Forest algorithm in fraud detection scenarios. Key topics covered include:
- Why Random Forest is effective for fraud detection.
- Outlier detection and feature engineering methods.
- Python implementation, including model building, evaluation, and recursive feature elimination.
- Results and analysis of fraud detection using Random Forest.
In this study, we compare three popular supervised learning algorithms: Logistic Regression, Naive Bayes, and Decision Trees. The following sections are explored:
- Comparative analysis of these methods for fraud detection.
- Explanation of how each algorithm works.
- Applications of each method in fraud detection.
- Python implementation, covering data preprocessing, model building, evaluation, and key findings.
We dive into clustering techniques for fraud detection, focusing on density-based clustering with DBSCAN. This case study includes:
- The role of unsupervised algorithms in fraud detection.
- Detailed steps for outlier detection using Z-Score and DBSCAN methods.
- Python code for clustering, data sampling, and result evaluation.
This project is licensed under the MIT License. You are free to use and modify the code for personal or commercial purposes, but please provide appropriate attribution. 📜