Feature request: Add Anomaly Detection Project

[sanchitc05]

Fixes #858

Related Issues or Bug

• This issue was related to anomaly detection not identifying edge case outliers in the data properly, especially in the Isolation Forest algorithm.

Fixes: #858

Proposed Changes

• Added the contamination and max_samples parameters to the Isolation Forest model to improve anomaly detection by allowing for more flexibility in handling the dataset.
• Implemented feature scaling in the preprocessing steps to ensure the data is appropriately normalized before applying the anomaly detection models.
• Enhanced the Jupyter notebook with additional visualizations to better understand the anomalies detected by the models.
• Added the KMeans clustering-based anomaly detection to identify outliers differently and compared the results with Isolation Forest.

Additional Info

• The changes will improve the accuracy and flexibility of the anomaly detection models, especially when working with unbalanced datasets.
• These changes help fine-tune model performance through hyperparameters and improve visualization.

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Name	Status	Preview	Comments	Updated (UTC)
ml-nexus	✅ Ready (Inspect)	Visit Preview	💬 Add feedback	Nov 9, 2024 7:29pm

[github-actions]

👋 Thank you for opening this pull request! We appreciate your contribution to improving this project. Your PR is under review, and we'll get back to you shortly.
Don't forget to mention the issue you solved!

[sanchitc05]

@UppuluriKalyani please check the pr and let me know if you want any modifications

[AKSHITHA-CHILUKA]

@UppuluriKalyani merge it and add labels

[github-actions]

🎉🎉 Thank you for your contribution! Your PR #859 has been merged! 🎉🎉

[sanchitc05]

Hey @UppuluriKalyani can you please assign a level 3 label? I don't think level 1 really appreciates my work.
Regards,
Sanchit Chauhan