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