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.

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

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

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

TimeSeries Analysis in R

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.

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.

Using machine learning methods to predict demand for bike sharing.

ML Demand Project Folder

определить характеристики и с их помощью спрогнозировать длительность поездки такси

This project uses ARIMA and Prophet models to forecast sales and demand, with applications in inventory management and # strategies. Includes time series preprocessing and visualization

A default spare engine placement generator

Integer Programming Extreme Value Model

An Enterprize of Sentient Enterprise

Dynamic # 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.

Solution to the Data Science Game 2017 competition (final stage)

The objective is to predict 3 months of item-level sales data at different store locations.