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.In such an attempt, BoomBikes aspires to understand the demand for shared bikes among the people after this ongoing quarantine situation ends across the nation due to Covid-19. They have planned this to prepare themselves to cater to the people's needs once the situation gets better all around and stand out from other service providers and make huge profits.
The company wants to know:
- Which variables are significant in predicting the demand for shared bikes.
- How well those variables describe the bike demands
- Loading the relevent libraries
- Loading the dataset
- Dropping irrelevent columns
- Exploring correlation matrix
- Data Preparation (Changing variables into categorical variables)
- Performing Exploratory Data Analysis
- Creating dummy variables
- Splitting into train and test set
- Rescaling the features(MinMaxScaler)
- Model Building based on RFE
- Residual Analysis on Training set
- Making predictions on Test set using final model
- Conclusions
You are required to model the demand for shared bikes with the available independent variables. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations. Further, the model will be a good way for management to understand the demand dynamics of a new market.
- Numpy
- Pandas
- Matplotlib
- Seaborn
- FilterWarnings
- Statsmodel
- Sklearn