Bike Sharing Assignment

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

Table of Contents

• General Info
• Technologies Used
• Conclusions

General Information

• Here US bike-sharing provider BoomBikesis a service in which bikes are made available for shared use to individuals on a short term basis for a price or free.Company has recently suffered considerable dips in their revenues.
• 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.
• This Model helps management to understand how exactly the demands vary with different features
• The service provider firm has gathered a large dataset on daily bike demands across the American market based on some factors.

Conclusions

• when temp increases number of bike riders also increases ....also bike riders prefer low windspeed and hummidity range of 40 to 90
• The Seaon Spring has the lowest number of bike rider among all season in general and summer and fall see's high number of bike riders
• Month Aug ,jun and sep are 3 month where there are high number of bike riders
• Fitted a Linear model and got the r2 score of 0.83 and 0.82 for Test and Train data respectively
• when weather situation is snow then there is very low number of riders compare to other weather situations,also clear weather situation showing highest number of ike riders across weekdays

Technologies Used

• pandas - Version: 2.0.3
• numpy - Version: 1.24.3
• matplotlib - Version: 3.7.1
• seaborn - Version: 0.12.2
• statsmodels - Version: 0.14.0

Contact

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