This analysis is a basic exploratory project with the Kaggle bikeshare data set from Kaggle. The idea is to gain an understanding of how bike rentals vary by time of the day, month, temperature and weather conditions. I used both a basic and multivariate linear regression to illustrate that it is not the best machine learning approach to this problem, given the seasonality in the data. The multivariate model managed a negligable adjusted R squared despite favorable p values of many predictors used in the model.
LeondraJames/Bikeshare-Exploratory-Analysis
An exploratory analysis of the Kaggle bikeshare data set with the application of linear regression models, which are not optimal for this particular problem of predicting bikes rented.
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