This assignment is a programming assignment wherein we had to build a MLR - Multiple Linear Regression model for the prediction of demand for shared bikes for A US bike-sharing provider BoomBikes. BoomBikes aspires to understand the demand for shared bikes among the people after this ongoing quarantine situation ends across the nation due to Covid-19. They have planned this to prepare themselves to cater to the people's needs once the situation gets better all around and stand out from other service providers and make huge profits. As a consulting company I have worked on understanding the Data, prepare it for the linear regression model, did a deep dive into the various independent variables and dependent variables. Finally I have built a MLR model to predict the demand for shared bikes which BoomBikes can use to plan their business.

• We see that temperature variable is having the highest coefficient 0.4695, which means if the temperature increases by one unit the number of bike rentals increases by 0.4695 units.

• We also see there are some variables with negative coefficients, A negative coefficient suggests that as the independent variable increases, the dependent variable tends to decrease. We have spring, mist cloudy , light snow variables with negative coefficient. The coefficient value signifies how much the mean of the dependent variable changes given a one-unit shift in the independent variable while holding other variables in the model constant.

• BoomBikes can focus more on Temperature

• BoomBikes can focus more on Summer & Winter season, September month, as they have good influence on bike rentals.

• Holiday, Spring, mistcloudy and lightsnow have negative coefficient and negatively correlated to bike rentals.