Implement an Anomaly Detection System

[Cgarg9]

🏗 Task Overview

Your goal is to implement an anomaly detection system that can identify outliers in a given dataset. This can be applied to various use cases, such as fraud detection, network intrusion detection, or industrial equipment failure prediction. You can use traditional machine learning techniques (Isolation Forest, DBSCAN, One-Class SVM) or deep learning approaches (Autoencoders, GANs).

🛠 Steps to Follow

1. Fork the Repository

• Start by forking this repository to your GitHub account.
• Clone it locally and set up your working environment.
• Select and Preprocess the Dataset

2. Choose a dataset related to anomaly detection (e.g., Credit Card Fraud, KDD Cup 1999, NASA Bearing Dataset).

• Perform data preprocessing (handling missing values, normalization, feature engineering).
• Implement Anomaly Detection Models

3. Start with traditional ML-based methods:

• Isolation Forest
• DBSCAN

4. Experiment with deep learning techniques:

• Autoencoders
• Variational Autoencoders (VAE)
• GAN-based anomaly detection

5. Evaluate the Model

• Use metrics like precision, recall, AUC-ROC, and F1-score to evaluate performance.
• Visualize anomalies using scatter plots, heatmaps, or t-SNE.

6. Build a simple UI using Streamlit to visualize results.

7. Submit a Pull Request

• Create a new branch and commit your changes.
• Open a pull request (PR) with a clear description of your implementation.
• Wait for review and feedback from maintainers.

📌 Guidelines

• Ensure proper documentation and clean code structure.
• Compare different approaches and justify your choice of model.
• Update the README.md if necessary.

🎉 Happy Coding! We look forward to your contributions! 🚀