Rental bike ML prediction project.

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.

Table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

General Information

• Provide general information about your project here. task is to model the predict the demand for shared bikes with the available independent variables.
• What is the background of your project? EDA, data-processing, multiple linear regression, devoid of multicollinearity.
• What is the business probem that your project is trying to solve? predicting the future forecast.
• What is the dataset that is being used? Rental bike data.

Conclusions

• list of significant variables to predict the demand for shared bikes yr holiday workingday temp windspeed weekday_6 mnth_9 season_2 season_4 weathersit_2 weathersit_3

• Temp (co-efficient: 0.5983), year (co-efficient: 0.2349), and winter season (co-efficient: 0.1257) has important role.

• comapny should focus more on weathersit_3, windspeed and weekday_6 for better expansion.

Technologies Used

• numpy - 1.20.1
• pandas - 1.2.4
• matplotlib - 3.3.4
• seaborn - 0.11.1
• statsmodels - 0.12.2
• sklearn - 0.24.1

Acknowledgements

Give credit here.

• This project was inspired by Rental bike ML prediction project.
• This project was based on this tutorial.

Contact

Created by [@gautamk2190] - feel free to contact me!