This project is a collection of recent research in areas such as new infrastructure and urban computing, including white papers, academic papers, AI lab and dataset etc.

The primary objective of this project is to build a Real-Time Taxi Demand Prediction Model for every district and zone of NYC.

Machine Learning Model for Order Demand Prediction based on historical Order data - Built for Swiggy Hackathon 2018

this repository contains main project of Rahnema college machine learning bootcamp

Integrated real-time data analytics for optimized public transport, innovative road monitoring using demand prediction, and conditioning tech for sustainability, real time pothole detection either by image or video, smart parking count system for efficiency using AI/ML.

A spare engine placement generator based on a Finite-Horizon Markov Decision Process

Online Product Forecasting Using Machine Learning is a machine learning-based system that predicts future product demand using historical sales data. It helps businesses improve inventory management, reduce stock shortages, optimize resources, and make data-driven decisions through accurate sales forecasting and trend analysis.

Dynamic Pricing is an application of data science that involves adjusting the prices of a product or service based on various factors in real time. It is used by companies to optimize revenue by setting flexible prices that respond to market demand, demographics, customer behaviour and competitor prices.

This repository contains an Excel-based dataset of original daily/monthly sales data intended for use in time series forecasting tasks. The dataset is suitable for training LSTM (Long Short-Term Memory) models and benchmarking forecasting performance.

Spatio-temporal traffic demand forecasting on a 15-min geohash grid. 42 leak-free features (target encodings, spatial-neighbour spillover, geo-clusters, cyclical time) + stacked ensemble of LightGBM, XGBoost, CatBoost, HGBR, ExtraTrees → 97.85 accuracy. Streamlit demo, geospatial viz, ablation study, error analysis, CI/CD, Docker, MLOps.

Reproducible Python data pipeline for transforming Austin shared micromobility trip records into cleaned e-scooter dataset, hourly pickup/dropoff demand images, and geospatial activity mask for spatiotemporal deep learning.

TimeSeries Analysis in R

Using machine learning methods to predict demand for bike sharing.

AI-powered Smart Metro Operations & Passenger Navigation Platform featuring QR ticketing, demand prediction, fraud detection, real-time analytics, and intelligent interchange guidance for Bengaluru Metro.

An AI-based inventory optimization system that leverages machine learning to predict demand, recommend menu items, and streamline stock management for restaurants and food service businesses.. — all deployed through a real-time Stream lit web app.

Retail sales forecasting project using ARIMA, Prophet, XGBoost, and LSTM — compares time series and machine learning models for demand prediction.

Machine learning project to analyze and predict electricity consumption trends in India using Python and Streamlit.

AI-powered web-based billing and sales management system for manufacturing enterprises with predictive analytics, inventory management, invoice automation, and machine learning–based demand forecasting.

Dynamic pricing system for cement sales, using XGBoost and demand forecasting to optimize profit based on internal operations and external market conditions.