Bike sharing systems are a means of renting bicycles. The goal of the Bike Sharing Demand competition is to predict demand by combining historical usage patterns with weather data.

For this project I

• Explored the effect of features on the bike rental count using line and point plots
• Tested a variety of Regression models, including Linear regression, Ridge regression, Random forest regression, KNN and XGBoost
• Optimized the performance of the best performing models using GridSearch CV
• Used a voting ensemble on the optimized models to boost model performance resulting in a top 5% score

Click here to view this project on GitHub.

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