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This project is a simplified (basic) version of "Bike Rental" project (without any data analysis step).
The objective of the project is - using historical usage patterns and weather data, forecast(predict) bike rental demand (number of bike users (‘cnt’)) on hourly basis.
Use the provided “Bikes Rental” data set to predict the bike demand (bike users count - 'cnt') using various best possible models (ML algorithms). Also, report the model that performs best, and fine-tune the same model using one of the model fine-tuning techniques, and report the best possible combination of hyperparameters for the selected model.
Lastly, use the selected model to make final predictions and compare the predicted values with the actual values.
Cloudxlab is using this “Bike Sharing Demand” problem for its machine learning learners for learning and practicing. This dataset was provided by Hadi Fanaee Tork using data from Capital Bikeshare. We also thank the UCI machine learning repository for hosting the dataset.
Fanaee-T, Hadi, and Gama, Joao, Event labeling combining ensemble detectors and background knowledge, Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg.