Bike Share Multiple Linear Predictor. Projeect for upGrad & Woolf's GMBA program.
A US bike-sharing provider BoomBikes has recently suffered considerable dips in their revenues due to the ongoing Corona pandemic. The company is finding it very difficult to sustain in the current market scenario.
The Company wishes to find out which variables are significant in predicting the demand for shared bikes and how well those variables describe the bike demands. Based on various meteorological surveys and people's styles, the service provider firm has gathered a large dataset on daily bike demands across the American market based on a few factors.
We are coming up with a regression model to predict the total number of rides in a given day based on features such as weather conditions, and the day and season of the year(s).
The dataset being used is: 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, doi:10.1007/s13748-013-0040-3.
- Our model has a r2-score of over .831 which is very good
- Our model has 16 feature variables which are used to predict the total count of rides
- numpy
- pandas
- matplotlib
- seaborn
- sklearn
- statsmodels
- The dataset used in this project 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, doi:10.1007/s13748-013-0040-3.
Created by [@dbaweja]