Automated PredictiveModeling Pipeline

An platform for building, training, and deploying machine learning models. Upload your data, train models, monitor performance, and generate predictions with automated preprocessing and feature engineering.

Advanced Model Explainability

Understand your model predictions with SHAP and LIME explanations. See exactly which features drive your predictions and why.

SHAP Waterfall Explanation

Prediction: 0.7300
Base: 0.5000
House Size (sqft)+0.1500
Location Score+0.1200
Age of Property-0.0800
Number of Bedrooms+0.0600
Neighborhood Rating+0.0500
Property Condition+0.0400
Distance to School-0.0300
0.47
0.57
0.67
0.77
0.87

SHAP waterfall visualization showing how each feature contributes to the final prediction

Feature Importance Analysis

House Size (sqft)0.2450
Location Score0.1980
Age of Property-0.1560
Number of Bedrooms0.1340
Neighborhood Rating0.1120
Distance to School-0.0890
Property Condition0.0780
Year Built0.0650
Garage Size0.0540
Lot Size0.0430

Global feature importance showing which features have the most impact on model predictions

System Architecture

Built with modern, scalable technologies. Our architecture ensures high performance, reliability, and seamless integration.

React Flow mini map
Component Types
Frontend
Backend
Database
Cache
MLflow
Orchestration
Storage
Workers
WebSocket
HTTP/REST
WebSocket
Pub/Sub
Data Flow

Scalable Backend

FastAPI-powered REST API with async operations, Redis caching, and cloud storage integration for optimal performance.

Modern Frontend

Next.js 14 with React 18, TypeScript, and Tailwind CSS for a responsive, interactive user experience.

Data Pipeline

Prefect orchestration with PostgreSQL + TimescaleDB for efficient time-series data management and ETL workflows.