Anomaly Detection Project

Welcome to the Anomaly Detection Project! This project focuses on detecting anomalies in various datasets using state-of-the-art machine learning techniques. Anomalies, or outliers, are data points that deviate significantly from the majority of the data, and detecting them is crucial in many applications, including fraud detection, network security, and quality control.

Overview

This project aims to provide a comprehensive pipeline for anomaly detection, from data preprocessing and feature engineering to model training and evaluation. We implement and compare various algorithms to identify the best approach for different types of datasets and anomalies.

Algorithms Implemented

The project includes implementations of the following anomaly detection algorithms:

Statistical Methods (e.g., Z-Score, IQR) Density-Based Methods (e.g., DBSCAN, Local Outlier Factor) Isolation Forest One-Class SVM

Getting Started

Prerequisites

Ensure you have the following installed:

Python 3.7+ pip (Python package installer) Git

Acknowledgements

This project uses the following open-source libraries:

Scikit-Learn, Pandas, NumPy, Matplotlib, Seaborn, TensorFlow / PyTorch (for deep learning models).

Thank you for checking out my project!