This project attempts to answer the question posed by the Kaggle Bike Share question.

Clone the repo and save all files and directories in src/Rhtml/ to a place on your machine and open bike_share_report.html in a browser (make sure the figure and static folders are in the same directory as the .html file).

The input data can be found in data/input

• test.csv The test dataset
• train.csv The train dataset

The output data (predicted values) can be found in data/output

• predicted values for all models
• random_forest_counts.csv contains the random forest regression predicted counts in the proper submission format

An Rhtml scipt src/Rhtml/bike_share_report.Rhtml can be run using RStudio and knitr. The R dependencies can be found in src/R/includes.R - please make sure you install these packages before running the Rhtml script.

Python dependencies include having Python3 installed and the following packages installed:

• scikit-learn
• numpy
• pandas
• statsmodels

Make sure necessary execute permissions have been added to the python script src/python/machine_learning_algorithm.py. From the root directory bike_share,

cd src/python/
python3 ./machine_learning_algorithm.py

This will run the following models:

• Linear Regression
• Poisson Regression
• Lasso Regression
• Ridge Regression
• Random Forest Regression

Note that you can change the constant values in that file to test models with different arguments and features.