Linear Regression Assignment

• 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.
• Bikes are borrowed through computer-controlled docks and returned back to another dock.
• Revenue dips in US bike sharing provider due to Corona.
• Business plan is to accelerate the revenue as soon as ongoing lockdown come to an end.
• Understand the factors affecting the demand of the shard bikes in the American market
• Identify Variables significant in predicting the demand of shared bikes
• How well the variables describe the bike demands

Table of Contents

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

General Information

• Provide general information about your project here. Linear regression assignment - MLR for a bike sharing system.
• Background
  • 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.
  • Bikes are borrowed through computer-controlled docks and returned back to another dock.
  • Revenue dips in US bike sharing provider due to Corona.
  • Business plan is to accelerate the revenue as soon as ongoing lockdown come to an end.
• Goal
  • Linear regression model of Demand of Shared Bikes with the available Independent Variables.
  • Understand Demands variation with different featuires.
  • Manipulation of business strategy to meet the demand levels and expectations of the customer.
  • Understand Demand Dynamics of a new Market.
• What is the dataset that is being used?
  • day.csv (description in DataDictionary.txt)

Conclusions

--------------------------------------------------------
FIT IS WITH MIN-MAX SCALING ON DATA-SET
Initial RFE Features Considered =  14
-------------------------------------------------------
Final Features and Coefficients
------------------------------
temp                  0.447379
weatsit_3lightrain   -0.287028
yr_2019               0.234598
const                 0.214403
windspeed            -0.151694
m9                    0.091051
holiday              -0.090982
winter                0.086565
spring               -0.082791
weatsit_2cloudy      -0.079341
m5                    0.064284
m3                    0.054170
m4                    0.053375
m6                    0.036216
dtype: float64
-------------------------------------------------------
Best Fitted Line 
---------------- 
cnt =  (0.2144)*const+(-0.091)*holiday+(0.4474)*temp+(-0.1517)*windspeed+(-0.0793)*weatsit_2cloudy+(-0.287)*weatsit_3lightrain+(-0.0828)*spring+(0.0866)*winter+(0.0542)*m3+(0.0534)*m4+(0.0643)*m5+(0.0362)*m6+(0.0911)*m9+(0.2346)*yr_2019
-----------------SCORE ON TEST DATA--------------------
Root Means Square Error =  0.09486832980505137
MSE                     =  0.009
R2 Score                =  0.811
------------------SCORE ON TRAINING DATA---------------
Root Means Square Error =  0.09110433579144299
MSE                     =  0.0083
R2 Score                =  0.835

Technologies Used

• python - version 3.9.x
• numpy - version 1.21.5
• pandas - version 1.4.2
• seaborn - version 0.11.2
• sklearn - version 1.0.2
• statsmodels - version 0.13.2
• scipy- version 1.7.3
• matplotlib- version 3.5.1

import warnings warnings.filterwarnings('ignore')

Acknowledgements

Give credit here.

• This project executed is an graded assignment from upgrad
• References - upgrad-course material , upgrad documents.

Contact

Created by Shrinivas Bhat [@sshrinivasbhat] - feel free to contact me!