Bike Sharing Assignment

This assignment is a programming assignment to build a multiple linear regression model for the prediction of demand for shared bikes. In the assignment, concepts such as EDA, Visualization, p-value, VIF are used. This uses both statsmodels and linear regression model from Scikit-learn package

Table of Contents

• General Info
• Project Structure
• Business Context
• Business Goal
• Dataset and Data Dictionary
• Model building approach
• Technologies Used
• Conclusions
• Acknowledgements

General Information

• For this assignment, a Python notebook with the whole linear model, predictions, and evaluation is expected to submit
• This project aims to build and evaluate a multiple linear regression model to predict the dependent variable using several independent variables. The purpose is to understand how different features impact the target variable and to create a model that can be used for predictive analysis.

Project Structure

• dataset/: Contains the dataset and data dictionary used for the analysis.
• Bike-Sharing-Multiple-Linear-Regression-Assignment.ipynb: Jupyter notebooks with the exploratory data analysis (EDA) and model building steps.
• README.md: Project documentation and overview.

Business Context

• Bike-sharing provider BoomBikes has recently suffered considerable dips in their revenues due to the ongoing Corona pandemic.
• 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.

Business Goal

• To model the demand for shared bikes with the available independent variables.
• It will be used by the management to understand how exactly the demands vary with different features.
• They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations.
• Further, the model will be a good way for management to understand the demand dynamics of a new market

Dataset and Data Dictionary

The following files are present inside dataset folder:

• day.csv (dataset)
• Readme.txt (data dictionary)

Model building approach

At high level this assignment is divided into 9 steps:

• Importing and understanding data
• Visualizing the Data - numerical and categorical variables
• Data preparation - includes choice between Binary Encoding and One-Hot encoding, inferences
• Splitting the data into training and testing set
• Rescaling the features
• Building a linear model - includes comparison between various models
• Residual Analysis of the train data
• Making predictions using final model
• Model Evaluation
• Recommendation and Model Summary

Conclusions

A final model with following key predictors :

• yr : Reflects the yearly trend and is highly significant.
• workingday : Indicates whether the day is a working day, significant for demand fluctuations.
• temp : Temperature, a strong predictor of bike rentals.
• Spring and Winter : Season, significant for seasonal variations.
• Showers : Weather condition, important for understanding how different weather affects demand.
• Sunny : Another weather condition, significant for the model.
• month_9 : Month indicators capturing seasonal trends.
• Monday : Day of the week, significant for weekly trends.

Technologies Used

• Python - version 3.12.4
• Matplotlib - version 3.8.4
• Numpy - version 1.26.4
• Pandas - version 2.2.2
• Seaborn - version 0.13.2
• Statsmodels - version 0.14.2
• Scikit-learn - version 1.4.2

Acknowledgements

• Upgrad Team and instructors

Contact

Created by @GirishKolhe - feel free to contact us!