ML-Powered Analytics Platform
Project Overview
An end-to-end analytics platform that leverages machine learning to provide predictive insights from complex datasets. The platform achieves 95% accuracy in its predictions and handles real-time data processing for immediate analysis.
Key Features
Real-time Processing
Processes and analyzes data streams in real-time with minimal latency using Apache Kafka and custom stream processing pipelines.
Advanced ML Models
Implements state-of-the-art machine learning models using TensorFlow, achieving 95% prediction accuracy across various metrics.
Interactive Visualizations
Beautiful, interactive data visualizations powered by D3.js, allowing users to explore and understand their data intuitively.
Scalable Architecture
Built on AWS with auto-scaling capabilities, handling millions of data points while maintaining optimal performance.
Technical Details
- Backend: Python with FastAPI, Redis for caching, and PostgreSQL for data storage
- ML Pipeline: TensorFlow, Scikit-learn, and custom algorithms for predictive modeling
- Frontend: React with TypeScript, Redux for state management, and D3.js for visualizations
- Infrastructure: AWS (EC2, S3, RDS, Lambda) with Terraform for infrastructure as code