Machine Learning & Time Series Forecasting with Interactive Data Visualization

Train, compare, serve, and visualize machine-learning models through REST APIs, persistent data flows, and an interactive application dashboard.

Categories

Tech Used

Problem

Machine-learning models are most useful when predictions can be served reliably, compared clearly, and reviewed through an interface accessible to non-technical stakeholders.

Approach

• Trained and compared multiple machine-learning and deep-learning model families for prediction tasks
• Built REST API endpoints for online and batch inference workflows
• Connected models with persistent data and application-level prediction pipelines
• Created dashboard views for predictions, evaluation metrics, and model-facing interaction

Results

• Delivered an end-to-end workflow from model development to API-based prediction and visualization
• Created a reusable architecture for production-style ML applications and proof-of-concept platforms
• Made model outputs easier for non-technical stakeholders to inspect and compare

Demo Videos