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https://github.com/coder5omkar/linear-regression-bike-sharing-assignment
This project aims to model the demand for shared bikes using various independent variables. The goal is to provide insights to the management team, helping them understand how demand fluctuates with different features
https://github.com/coder5omkar/linear-regression-bike-sharing-assignment
data-science eda matplotlib ml mlr pandas python3 seaborn
Last synced: 4 days ago
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This project aims to model the demand for shared bikes using various independent variables. The goal is to provide insights to the management team, helping them understand how demand fluctuates with different features
- Host: GitHub
- URL: https://github.com/coder5omkar/linear-regression-bike-sharing-assignment
- Owner: coder5omkar
- License: mit
- Created: 2024-12-23T13:27:03.000Z (15 days ago)
- Default Branch: main
- Last Pushed: 2024-12-23T13:55:38.000Z (15 days ago)
- Last Synced: 2024-12-23T14:45:57.102Z (14 days ago)
- Topics: data-science, eda, matplotlib, ml, mlr, pandas, python3, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Shared Bike Demand Prediction 😄
## Table of Contents
* [General Info](#general-information)
* [Technologies Used](#technologies-used)
* [Conclusions](#conclusions)
* [Acknowledgements](#acknowledgements)
## General Information
```
This project aims to model the demand for shared bikes using various independent
variables. The goal is to provide insights to the management team, helping them
understand how demand fluctuates with different features. By analyzing these patterns,
management can make informed decisions to adjust business strategies, optimize
resource allocation, and align their operations with customer expectations. This model
will serve as a valuable tool to anticipate demand and improve service delivery.
```
## Technologies Used
- [Python](https://www.python.org/) version: 3.12.4
- [Numpy](https://numpy.org/) version: 1.26.4
- [Pandas](https://pandas.pydata.org/) version: 2.2.2
- [Seaborn](https://seaborn.pydata.org/) version: 0.13.2
- [Matplotlib](https://matplotlib.org/) version: 3.9.2
## Conclusions 🔥
### Positive Coefficients:
- **year (0.2340)**: As the year increases, the demand for bikes increases slightly.
- **temp (0.4782)**: Higher temperature increases the demand for bikes, indicating people prefer to cycle in warmer weather.
- **summer (0.0623)** and **winter (0.0969)**: These seasons have a positive effect on demand, possibly due to seasonal preferences.
### Negative Coefficients:
- **holiday (-0.1043)**: On holidays, the demand for bikes may decrease, possibly due to fewer people commuting.
- **windspeed (-0.1480)**: Higher wind speeds seem to decrease bike demand, likely due to unfavorable cycling conditions.
- **Light_snowrain (-0.2904)**: Snow and rain significantly reduce demand, indicating that people avoid cycling in bad weather.
- **sun (-0.0495)**, **Misty (-0.0809)**, **spring (-0.0544)**: These have smaller negative impacts, which could reflect variations in weather conditions influencing demand.
## Acknowledgements
- This project was inspired by IIT-B AI-ML program at Upgrad
## Contact
Created by [@in/omkaramale](https://github.com/coder5omkar) - feel free to contact me!
Developed as part of the ML-1 Module assignment required for Post Graduate Diploma in Machine Learning and AI - IIIT,Bangalore.
This project is open source and available under the [MIT License](https://github.com/coder5omkar/Linear-Regression-Bike-Sharing-Assignment/blob/main/licence.txt).