Bikes Demand Prediction - Multiple Linear Regression

Problem Statement:

A US bike-sharing provider BoomBikes has a daily dataset on the rental bikes based on various environmental and seasonal settings. It wishes to use this data to understand the factors affecting the demand for these shared bikes in the American market and come up with a mindful business plan to be able to accelerate its revenue as soon as the ongoing lockdown due to corona pandemic comes to an end.

Essentially, the company wants to know —

• Which variables are significant in predicting the demand for shared bikes.

• How well those variables describe the bike demands

Pipeline Followed :

• Data understanding and exploration

• Data Visualization

• Data preparation

Dataset :

DATASET DOWNLOAD

DATA DICTIONARY

Solution :

linear regression.ipynb

Download IPYNB • 2.48MB

Insights Drawn :

Interepretation of results

• Analysing the above model, the comapany should focus on the following features:

• Company should focus on expanding business during Spring.

• Company should focus on expanding business during September.

• Based on previous data it is expected to have a boom in number of users once situation comes back to normal, compared to 2019.

• There would be less bookings during Light Snow or Rain, they could probably use this time to serive the bikes without having business impact.

• Hence when the situation comes back to normal, the company should come up with new offers during spring when the weather is pleasant and also advertise a little for September as this is when business would be at its best.