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Analyzing the bike-share data of US from Motivate, for three popular cities Washington, New York, and Chicago and showing statistics for different users, stations, and times of travel. Also filters the data sets according to the user's choice and shows statistics on the filtered data.
In this project, I thoroughly clean bike-share data from 2014-2015 and build a simplistic ARIMA model to forecast daily revenue per bike station in 2016. (Repo in progress)
This is my First Viz Project using Tableau . I have done this project with the help of the video done by youtuber data with mo. https://www.youtube.com/@datawithmo
The Udacity US Bikeshare Data Analysis project is a hands-on project that involves using Python to explore and analyze bike share data from three major cities in the United States: Chicago, New York City, and Washington DC. You will gain insights into trends in bike share usage in different demographics, locations, and times of the year.
Predict near-term Capital Bikeshare availability using a random forest and Poisson regression. Display current status and predictions with leaflet.js map visualization.
Hubway Bike Share was a SQLite3 analytics project focusing on traffics of the bike utilizations in each station and across stations. The project also illustrated user patterns and preference to strategize user experience enhancement.
In this project, the dataset provided by Motivate (https://www.motivateco.com/), a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. It is designed to be interactive and allows you to compare usage between three large cities: Chicago, New York City, and Washington, DC.
This project demostrates my SQL and Excel skills, tools common for any organization. The datasets were too big to analyse in Excel alone and so I used SQL to do much of the heavy lifting and did visualizations in excel.
This repository is done as part of my Data Analyst Nanoodegree at Udacity, a modular program written in python 3 to help users interact with and explore US Bike-Share data of 3 major cities .