Data Mining Hub
2024
Advanced data mining and clustering analytics platform built with Next.js frontend and Flask backend. Features multiple clustering algorithms, real-time data preprocessing, statistical analysis, and comprehensive visualization tools for complex dataset analysis.
About this project
Data Mining Hub is a sophisticated analytics platform designed for advanced data exploration and clustering analysis. The application provides a seamless integration between a modern Next.js frontend and a powerful Flask backend, enabling researchers and data scientists to upload datasets, perform real-time statistical analysis, apply multiple clustering algorithms, and visualize results with interactive charts and dendrograms. The platform supports K-Means, K-Medoids, DBSCAN, and Hierarchical clustering with comprehensive performance metrics including Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Score.
Key Features
- CSV and ARFF file upload with automatic parsing
- Real-time data preview with column selection
- Comprehensive statistical analysis (mean, median, mode, missing values)
- Data preprocessing with multiple fill strategies
- Data normalization and standardization
- K-Means clustering with elbow method visualization
- K-Medoids robust clustering with representative points
- DBSCAN density-based spatial clustering
- Hierarchical clustering with dendrogram visualization
- Box plot and distribution analysis
- PCA dimensionality reduction for high-dimensional data
- Multi-algorithm performance comparison
- Silhouette Score calculation and visualization
- Davies-Bouldin Index computation
- Calinski-Harabasz Score evaluation
- Real-time plot generation (matplotlib backend)
- Cluster visualization with 2D/3D projections
- Export results in CSV format
- CORS-enabled REST API
- Responsive tabbed interface
- Real-time error handling and logging
My Role
Full-Stack Developer & Data Science Engineer
Challenge
Building a complex full-stack application that integrates a responsive Next.js frontend with a robust Flask backend capable of handling large datasets, implementing multiple clustering algorithms with real-time visualization, and ensuring smooth data flow between frontend and backend with proper error handling and performance optimization.
Solution
Implemented modular component architecture with TypeScript for type safety, created a comprehensive Flask API with 20+ endpoints for data processing and clustering, integrated scikit-learn for machine learning algorithms, utilized pandas and NumPy for efficient data manipulation, implemented real-time visualization with matplotlib, added comprehensive error handling with logging, and optimized data transfer between frontend and backend with efficient JSON serialization.