https://github.com/naqvijafar91/bike-demand-prediction-linear-regression

https://github.com/naqvijafar91/bike-demand-prediction-linear-regression

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• Host: GitHub
• URL: https://github.com/naqvijafar91/bike-demand-prediction-linear-regression
• Owner: naqvijafar91
• Created: 2023-10-12T13:28:10.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-12T13:39:00.000Z (about 1 year ago)
• Last Synced: 2023-10-13T12:18:42.434Z (about 1 year ago)
• Language: Jupyter Notebook
• Size: 1.38 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Project Name

Bike Demand Prediction with Linear Regression

## Table of Contents

- [Project Name](#project-name)

  - [Table of Contents](#table-of-contents)

  - [General Information](#general-information)

  - [Conclusions](#conclusions)

  - [Technologies Used](#technologies-used)

  - [Acknowledgements](#acknowledgements)

  - [Contact](#contact)

## General Information

- **Background**: This project aims to build a multiple linear regression model to predict the demand for shared bikes in the American market. The project is inspired by the challenges faced by a bike-sharing provider, BoomBikes, due to the COVID-19 pandemic and their need to understand the factors influencing bike demand.

- **Business Problem**: The primary business problem this project is trying to solve is to understand the significant variables affecting bike demand and how these variables can be used to adjust the business strategy. The goal is to be well-prepared to meet customer expectations and accelerate revenue as the economy recovers.

- **Dataset**: The project utilizes a dataset containing information on daily bike demands across the American market, including various independent variables like weather conditions, season, year, and more. The target variable is 'cnt,' representing the total number of bike rentals.

## Conclusions

- **Conclusion 1**: The analysis revealed that temperature plays a very important role in bike demand. Thats why we have lower demand during spring while the best demand is in fall and summer.

- **Conclusion 2**: The 'yr' variable, indicating the year of 2018 or 2019, was found to be valuable for prediction, showing that the demand for shared bikes is increasing each year.

- **Conclusion 3**: The multiple linear regression model built in this project provided insights wrt to the overall weather on the day as well, the demand for bikes was lowest when it was snowing while it was more when the sky was clear.

- **Conclusion 4**: It was found that bike demand was less during holidays.

## Technologies Used

- Python - version 3.7

- Jupyter Notebook - version 6.0

- scikit-learn - version 0.24

- pandas - version 1.2

- numpy - version 1.19

- matplotlib - version 3.3

## Acknowledgements

- This project was inspired by the business challenges faced by BoomBikes, a bike-sharing provider.

- The analysis techniques and linear regression methodology used in this project were based on various online resources and tutorials.

## Contact

Created by [Your Name] - feel free to contact me!

- GitHub: [Syed Jafar Naqvi](https://github.com/naqvijafar91)

- LinkedIn: [Syed Jafar Naqvi](https://www.linkedin.com/in/syed-jafar-naqvi-343698a9/)