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GitHub / NatenaelTBekele / Bike_Sharing_Demand_Prediction

What are the different factors which affect the target variable? What business recommendations can we give based on the analysis? How can we use different ensemble techniques - Bagging, Boosting, and Stacking to build a model to predict the count of bikes rented?

JSON API: https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NatenaelTBekele%2FBike_Sharing_Demand_Prediction

Stars: 0
Forks: 0
Open Issues: 0

License: None
Language: Jupyter Notebook
Repo Size: 3.43 MB
Dependencies: 0

Created: about 2 years ago
Updated: 8 months ago
Last pushed: over 1 year ago
Last synced: 8 months ago

Topics: adaboost, decision-tree, gradient-boosting, hyperparameter-tuning, random-forest, stacking, xgboost

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