This repo contains the assigment details of the bike sharing project by UpGrad

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

This project follow the following steps

• Loading the data and doing initial analysis to understand that there are no missing values
• divide the variables into categorical and numeric variables
• do exploratory data analysis to understand the data
• conevert the categorical variables to dummy variables
• splt the data into train and test set
• scale the numeric variableds
• build a coars grain model by using REF
• fine tune the model by using statsmodel with the help of p-value and VIF
• do a residual analysis on the data
• and finally run the model on the test set and find out the r-squared value

To predict the demand the following variables are important

• temp
• year
• const
• Sun
• workingday
• s_mist
• s_spring
• s_light_rain

• numpy
• pandas
• seaborn
• statsmodel
• sklearn

[1] 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.

@article{ year={2013}, issn={2192-6352}, journal={Progress in Artificial Intelligence}, doi={10.1007/s13748-013-0040-3}, title={Event labeling combining ensemble detectors and background knowledge}, url={http://dx.doi.org/10.1007/s13748-013-0040-3}, publisher={Springer Berlin Heidelberg}, keywords={Event labeling; Event detection; Ensemble learning; Background knowledge}, author={Fanaee-T, Hadi and Gama, Joao}, pages={1-15} }

Created by [@tikluganguly] - feel free to contact me!