Development of a Machine Learning-Based Anomaly Detection System
Table Of Contents
<p>1. Introduction<br> 1.1 Background<br> 1.2 Motivation<br> 1.3 Objectives<br>2. Literature Review<br> 2.1 Anomaly Detection Techniques<br> 2.2 Applications of Anomaly Detection<br> 2.3 Challenges and Limitations<br>3. Methodology<br> 3.1 Data Preprocessing<br> 3.2 Feature Selection<br> 3.3 Model Selection<br> 3.4 Evaluation Metrics<br>4. Implementation<br> 4.1 Data Collection<br> 4.2 Model Development<br> 4.3 Real-time Anomaly Detection<br>5. Results and Evaluation<br> 5.1 Performance Metrics<br> 5.2 Comparative Analysis<br></p>
Project Abstract
The project aims to develop a machine learning-based anomaly detection system for identifying unusual patterns or behaviors in large datasets. Anomalies can indicate critical issues such as fraud, errors, or system malfunctions. The system will utilize various machine learning algorithms to detect anomalies in real-time data streams and historical datasets. The project will focus on evaluating the performance of different anomaly detection techniques and implementing a scalable and efficient solution for diverse applications
Project Overview
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