💻 Machine Learning for Fraud Detection in E-commerce 🚨

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.

🔍 Case Studies Overview

This project revolves around three distinct case studies, each focusing on different machine learning techniques.

Case Study 1: 🌲 Random Forest Algorithm for Fraud Detection

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.

Case Study 2: 🔄 Comparative Analysis of Supervised Learning Methods

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.

Case Study 3: 🔗 Unsupervised Learning for Fraud Detection

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.

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⚖️ License

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. 📜

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