Bike Share Toronto 2021 Data Analysis & Interactive Visualization

A CPLEX implementation to solve the specific problem of Bike Sharing Redistribution

cycle animation with wheel and button animation in one page

Implement a neural network in Numpy to predict bike rentals.

Exploring US Bike-Share data of 3 major cities at the year 2016.

Analyze Bay Area Bike Share Data

Interactive visualization of available bikes and e-bikes at Divvy stations across Chicago.

it has the 2 projects of fwd scholarship 🚴‍♂️ 🎬 :)

You will perform an exploratory analysis on data , You will compare the bike system usage between three large cities: New York City, Chicago, and Washington, DC. You will also see if there are any differences within each system for those users that are registered, regular users and those users that are short-term, casual users.

Analyze network interactions among stations and attributes related to trips. Also, identify communities and analyze interactions within communities to study if they exhibit relationships with the demographics of an area. Lastly, perform network classification to predict the demand at a station in the foreseeable future.

These projects helps you how we can apply the machine learning algorithm to solve a real world problems.

Python: Analyse shared-bike usage in U.S. cities

In this analysis, one of my goals was to identify when most trips are taken in terms of time of hour, weekday or month of the year. Second I wanted to know who the Ford GoBikes users are by age, gender and type. At last see how do the bike trips usually look by trip duration, distance and speed.

Google Data Analyst Capstone Project

A case study about a bike-share company to help understand the difference between different users

Rebalancing the Citibike system using Time Series Analysis and Clustering.