Deploy automl models for tabular tasks on AWS Sagemaker with AutoGluon

predict bike sharing demand using the AWS AutoGloun Framework

🚲📈📊 Udacity AWS-MLE Nanodegree project 1️⃣ - Submission to the Bike Sharing Demand Prediction Competition on Kaggle using awslabs autogluon library, Competition link : https://www.kaggle.com/c/bike-sharing-demand/overview

Predicting Bike-sharing Demand Using Machine Learning with AutoGluon

First project in AWS Machine Learning Fundamentals Nanodegree

This is the Bike sharing project repo.

Predict if two products are substitute of each other using labelled training data

Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able rent a bike from a one location and return it to a different place on an as-needed basis. Currently, there are over 500 bike-sharing programs around the world. The data generated by these systems makes them attractive for researchers because the duration of travel, departure location, arrival location, and time elapsed is explicitly recorded. Bike sharing systems therefore function as a sensor network, which can be used for studying mobility in a city. In this competition, participants are asked to combine historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C.