eta-prediction

An Intelligent Logistics Analytics, Route Efficiency, and Predictive Decision Support System for Confectionery Freight Networks with Multi-Model Machine Learning and Real-World Geospatial Mapping.

A Non-homogeneous Navier-Stokes Framework for Global Logistics ETA

Live train tracking with delay prediction and conservative ETA (SIH 2026 SIH26028) — frozen 46-feature contract, fail-safe live adapter, route-level ETA windows. FastAPI + Python.

A web-based vessel tracking and maritime intelligence system using Datalastic AIS APIs for real-time vessel tracking, port monitoring, and analytics.

A machine learning-powered platform to accurately predict delivery times in hyperlocal logistics by fusing Google Maps, live weather, and historical trip data.

End-to-end machine learning project that predicts food delivery time using Gradient Boosting regression. Includes data analysis, feature engineering (distance calculation), model evaluation, and a Streamlit-based web interface.

Adaptive ETA prediction for inland waterway cargo: PyTorch MLP base model, NLP delay severity from operational logs (keyword lexicon + DistilBERT), and Q-learning vs linear correction. Leak-free 5-seed evaluation: the linear correction cuts MAE by 7.6%.

Trip duration prediction from request-time features only — leakage-safe time split, gradient boosting, and a FastAPI endpoint measured at 9.2 ms p50.

End-to-end Deep Learning & ML pipeline predicting delivery ETA using KerasTuner, Scikit-Learn ColumnTransformer, and Streamlit.

End-to-end Machine Learning system for food delivery ETA prediction with geospatial features, real-world routing and FastAPI deployment.

Shipment final delivery ETA prediction, severe delay probability (>48h), and stage-by-stage delay propagation explanations.

Predict food delivery ETA from courier, traffic and weather

A full-stack machine learning architecture for food delivery ETA prediction, leveraging a DVC-driven pipeline, automated CI/CD workflows, cloud artifact management, and LGBM-based stacked regression ensemble for high-fidelity time estimations.

PyTorch LSTM notebooks for last-mile delivery ETA prediction.

Production-oriented food delivery ETA prediction system using LightGBM, geospatial feature engineering, and ML benchmarking with 3.12 min MAE.

Bus arrival time prediction on a real GTFS feed (MBTA Boston): gradient-boosting delay model cuts ETA error by 73% vs the timetable. Python / pandas / scikit-learn.

Production-grade ML system for food delivery ETA prediction using FastAPI, DVC, and MLflow.

AI-powered emergency ambulance dispatch and routing system for intelligent ambulance selection, traffic-aware routing, live rerouting, ETA prediction, and hospital matching.

Predicción de tiempos de entrega (ETA) y análisis de retrasos en domicilios con machine learning y simulador en Streamlit.

Different types of ML projects