/BoomBike

Primary LanguageJupyter Notebook

BoomBike

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. So, it has decided to come up with a mindful business plan to be able to accelerate its revenue as soon as the ongoing lockdown comes to an end, and the economy restores to a healthy state

Table of Contents

General Information

  • 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. So, it has decided to come up with a mindful business plan to be able to accelerate its revenue as soon as the ongoing lockdown comes to an end, and the economy restores to a healthy state.
  • 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 want to understand the factors affecting the demand for these shared bikes in the American market. The company wants to know:
    • Which variables are significant in predicting the demand for shared bikes.
    • How well those variables describe the bike demands

Conclusions

BoomBikes can focus more on Temperature

  • They have good influence on bike rentals, so focus more on Summer & Winter season, August, September month, Weekends, Working days.
  • If they have more offers in spring season, as it have negative coefficients and negatively correlated to bike rentals.
  • In Mist +cloudy and Lightsnow weathers also need to provide offers, as we have got negative coefficients on weathersit

Technologies Used

  • Python - 3.8.8
  • NumPy - 1.21.4
  • Seaborn
  • scikit-learn
  • matplotlib
  • statsmodels

Acknowledgements

Give credit here.

Contact

Created by J syam koteswar(syamkoteswar22@gmail.com) - feel free to contact me!