Anomaly Detection in Highly Imbalanced Multivariate Data Using Gaussian Mixture Model

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Project Description:
This web application is designed to detect anomalies within highly imbalanced multivariate data using the Gaussian Mixture Model, a powerful probabilistic approach. It offers an array of functionalities aimed at providing users with an interactive and intuitive experience, including:

Real-Time Data Exploration (EDA): Users can explore and analyze their data in real-time, gaining immediate insights and understanding of the dataset’s structure and key features.
Hyperparameter Tuning: The application includes tools for tuning hyperparameters, enabling users to optimize the Gaussian Mixture Model for their specific data and detection needs.
Data Visualization: Comprehensive data visualization capabilities are integrated, allowing users to visualize both the data and the model’s outputs, enhancing interpretability and decision-making.
By combining these features, the application supports users in effectively identifying and understanding anomalies in complex, imbalanced data environments.
  1. Arman bari
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