In this project, I implemented LSTM model to predict hourly Bike Share Demand
A project that aims to estimate the correlation between Guadalajara's crimes and its public bike system. Done as part of the Saturdays.AI program in its Guadalajara 2nd edition.
Having some fun with Capital Bikeshare's data
Analyzing and predicting the demand for bikes using a Spatio-Temporal Graph Convolutional Network (STGCN) model.
Linear Regression model for Bike sharing dataset
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.
London Bike Sharing Dataset
Developing a business strategy to meet the demand levels and meet the customer's expectations.
Pandas script that corresponds the start/end coordinates of Citibike rides with NYC neighborhoods
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.
Bikes' Rental Analysis - The repository contains data analysis aiming on understanding behavior of people renting bikes in Washington D.C. in years 2012-2018 (based on Capital Bikeshare data).
FOSSCON Indego Presentation / Cycling Through The Indego Bike-Share API
Collect and pre-process historical trip data from major bike sharing companies
Machine Learning
Predict near-term Capital Bikeshare availability using a random forest and Poisson regression. Display current status and predictions with leaflet.js map visualization.
An simple example of data exploration and test-driving development
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)
🚲 Bike Share Route Planner
Customer Analytics: Explore and analyze data related to bikeshare systems for three major cities
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