DemandIQ
Hybrid ensemble ML model for supply chain demand forecasting with time-series analytics.
Tech Stack
PythonLSTMXGBoostRandom ForestStreamlitScikit-learnMatplotlib
Project Overview
Designed a hybrid ensemble model combining LSTM, XGBoost, and Random Forest for accurate supply chain demand forecasting. Engineered time-series features with rolling statistics and lag variables, and deployed an interactive Streamlit dashboard for supply chain insights.
Key Features
Hybrid LSTM + XGBoost + Random Forest ensemble model
Time-series feature engineering with rolling stats and lag variables
Interactive Streamlit dashboard with Matplotlib charts
Supply chain demand prediction and visualization
Scikit-learn pipelines for model orchestration