Pulls Capital Bikeshare API data into DB. Currently running on Amazon EC2 and RDS.

Predict near-term Capital Bikeshare availability using a random forest and Poisson regression. Display current status and predictions with leaflet.js map visualization.

Prediction of bike rental count hourly or daily based on the environmental and seasonal settings using data set from two-years historical log corresponding to years 2011 and 2012 from Capital Bikeshare system, Washington D.C., USA

CLI for finding a bike share bike

Data Analysis of Capital Bikeshare

Using a variety of machine learning methods, we predict the individual daily demand on routes within the Capital Bikeshare network. Featured models include deep neural net classifier, random forest regressor, ridge regression, & gradient tree boosted regression. Originally submitted on 6/7/2020 as a class project for UC Davis' STA 208.

Time-series forecast using Neural Prophet to predict bike sharing trip

Forecast bike rental demand in the Capital Bikeshare program in Washington, D.C. based on weather data and historical usage patterns.

Capital Bikeshare dock spotter / data retriever. Text-based app for SMS devices.

Demand analysis and forecast for a bike-sharing company

This repository is intended for documenting Team 18's codes and outputs for the ANLY511 Project.

This is a data analysis project from Dicoding to pass the Learning Data Analysis with Python class. This project aims to analyse and create a simple dashboard based on data from Capital Bikeshare.