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Build a multiple linear regression model for the prediction of demand for shared bikes

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Bike Sharing

A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free.

Table of Contents

General Information

  • A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" which is usually computer-controlled wherein the user enters the payment information, and the system unlocks it. This bike can then be returned to another dock belonging to the same system.

Problem Statement

  • 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.

Conclusions

Conclusion 1

Univariate Analysis inidcates on numerical variables,

Windspeed has outliers. Should we remove outliers? Univariate Analysis of categorical data indicates, Holiday Majority of bike hires are on holidays This information matches with Working day or Not working day Season, Month, Week Consistent bike rents across season, month and week. Weather Situation Less bike hires on snowy or rainy days.

Conclusion 2

Bivariate & Multicollinearity Analysis indicates,

Temp and atemp are highly correlated. Bike hires count is more on holidays compared to non-holidays. Bike hires are uniform across all week days. Weather situation impacts bike hires. Bad weather has less hires.

Conclusion 3

The Linear regression model indicates demand for bikes is highly dependent on variables such as year, temperature and weather situation.

Technologies Used

  • Python 3.10.11
  • pandas 2.1.0
  • numpy 1.24.2
  • matplotlib 3.7.2
  • seaborn 0.13.2
  • sklearn 1.4.2

Acknowledgements

  • This project was inspired by Upgrad Assignment
  • With Support from Dinesh J Babu , Associate Professor, IIIT-B

Contact

Created by @cmurakonda - feel free to contact me!

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