astle-sudo/linearregression

Bike sharing

This project centers around the dataset provided, which contains information sales og bikes, and the features that seem to have contributed for the same.

Table of Contents

• General Info
• Technologies Used
• Conclusions

General Information

• The columns that were encoded where 'season', 'month' and 'weekday'
• The variable to be predicted was 'count' or the number of bikes sold in a day

Conclusions

• The most important features or indicators for the sale of bikes were:
• Number of registered users
• Number of casual users
• The tempreture for the day

Technologies Used

• sklearn == version 1.4.2
• pandas == version 1.5.3
• statsmodel == version 0.14.2

Contact

Created by [[email protected]] - feel free to contact me!