This project builds a predictive model to forecast bike-sharing demand based on various environmental and seasonal factors. By applying machine learning algorithms, the model helps optimize bike availability and improve user experience by anticipating peak demand periods.

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.

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this repository contains main project of Rahnema college machine learning bootcamp

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.

TimeSeries Analysis in R

This repository contains the code and data for a project focused on improving the prediction accuracy of rideshare demand in New York City during the Covid-19 pandemic.

Integer Programming Extreme Value Model

Using machine learning methods to predict demand for bike sharing.

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

An Enterprize of Sentient Enterprise

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

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

A default spare engine placement generator

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

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