Predict Bike Sharing Demand with AutoGluon

This is the first project in the AWS Machine Learning Fundamentals Nanodegree by Udacity

Project Overview

In this project, I applied the knowledge and methods learned in the Introduction to Machine Learning course to compete in a Kaggle competition using the AutoGluon library. The objective is to predict bike sharing demand by training a model with the Bike Sharing Demand dataset and submitting predictions to Kaggle for ranking. I iterated on the process by adding features to the dataset and tuning hyperparameters to improve my score.

Dataset

The dataset used is the Bike Sharing Demand dataset from the Kaggle Bike Sharing Demand Competition.

Dependencies

• Python 3.7
• MXNet 1.8
• Pandas >= 1.2.4
• AutoGluon 0.2.0

Install with pip

Repository Structure

Bike Sharing Demand/
├── LICENSE.txt                           # License file
├── project_notebook.html                 # HTML export of the project notebook
├── project_notebook.ipynb                # Project jupyter notebook
├── project_report.md                     # Markdown file of the project report
├── README.md                             # Readme file for the project repository
├── sampleSubmission.csv                  # Sample submission file
├── submission.csv                        # CSV file for initial submission
├── submission_new_features.csv           # CSV file for submission with new features
├── submission_new_hpo.csv                # CSV file for submission after hyperparameter tuning
├── submission_new_hpo2.csv               # Additional CSV file for submission after hyperparameter tuning
├── submission_new_hpo3.csv               # Additional CSV file for submission after hyperparameter tuning
├── submission_new_hpo4.csv               # Additional CSV file for submission after hyperparameter tuning
├── test.csv                              # Test dataset
└── train.csv                             # Train dataset