In this project, we use the AutoGluon library to train several models for the Bike Sharing Demand competition in Kaggle. You will be using Tabular Prediction to fit data from CSV files provided by the competition.

We take an iterative approach to improve our model and the results. This will be accomplished by adding more features to the dataset and tuning some hyperparameters available with AutoGluon.

Note: This repository relates to AWS Machine Learning Engineer nanodegree provided by Udacity.

Python 3.7
MXNet 1.8
Pandas >= 1.2.4
AutoGluon 0.2.0 

For local development, we need to setup a jupyter lab instance.

• Follow the jupyter install link for best practices to install and start a jupyter lab instance.
• If you have a python virtual environment already installed you can just pip install it.

pip install jupyterlab

• There are also docker containers containing jupyter lab from Jupyter Docker Stacks.

You can read the report of the code results from here.