Supply chain late delivery risk classifier · No leakage · No overfitting · LGB + XGB + CatBoost stacking · SHAP
Multi-class delivery risk prediction system using XGBoost, Random Forest, KNN and Logistic Regression on DataCo Supply Chain Dataset with real-time Gradio dashboard and agentic intervention system.
LogisAI is an AI-powered Delivery Delay Prediction & Route Intelligence System that predicts delivery ETA, delay risk, and optimal dispatch windows using machine learning, traffic, weather, and route analytics. It features interactive maps, route optimization, analytics dashboards, and smart logistics insights through a modern full-stack interface.
Machine Learning system for predicting delivery delays using order, customer, and logistics data.
AI-powered delivery delay prediction using machine learning and logistics data.
Delivery time prediction on 43K Amazon orders using Random Forest. 57% RMSE reduction vs mean baseline (RMSE 22.3, R² 0.813). SHAP feature ranking, MLflow tracking. Python · scikit-learn · Random Forest · SHAP
Delivery delay prediction using a Logistic Regression classifier, served via FastAPI and a Streamlit interface, containerized with Docker Compose.
A responsive, card-based web interface for an AI-powered delivery prediction engine. Built with HTML, CSS, and jQuery, it allows users to input delivery factors—such as distance, traffic, and vehicle type—to receive real-time, API-driven estimates from a secure FastAPI backend hosted on Google Cloud Run. Includes 1-click random data generation.
MoveEasy Delivery Risk Watchlist is a machine learning project that predicts which deliveries are most likely to arrive late. It ranks deliveries by risk so dispatchers can prioritise monitoring and intervention before service issues happen.
Delivery time prediction and late-delivery classification with PySpark ML — Linear & Logistic Regression, plus a dependency-free Flask API for real-time scoring.
Predicting Promised Delivery Time breaches in e-commerce supply chains using ML on the DataCo dataset.
Machine Learning project to predict food delivery time using features like traffic, weather, distance, and delivery partner details. Includes data analysis, feature engineering, and comparison of multiple regression models.
AI-powered machine learning system for predicting delivery delays and identifying factors that impact delivery performance.
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