https://github.com/kmohamedalie/seoul-bike-sharing-demand_regression

Predicting Seoul Bike sharing count /hr multiple regression 🤖🚲
https://github.com/kmohamedalie/seoul-bike-sharing-demand_regression

bike-sharing machine-learning regression seoul social-sciences

Last synced: about 19 hours ago
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Predicting Seoul Bike sharing count /hr multiple regression 🤖🚲

• Host: GitHub
• URL: https://github.com/kmohamedalie/seoul-bike-sharing-demand_regression
• Owner: Kmohamedalie
• License: mit
• Created: 2023-08-14T11:15:11.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2023-08-22T14:11:21.000Z (over 1 year ago)
• Last Synced: 2024-11-09T01:13:30.307Z (about 2 months ago)
• Topics: bike-sharing, machine-learning, regression, seoul, social-sciences
• Language: Jupyter Notebook
• Homepage: https://github.com/Kmohamedalie/Seoul-Bike-Sharing-Demand_Regression/tree/master
• Size: 275 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Seoul Bike Sharing Demand - Multiple Linear Regression



https://github.com/Kmohamedalie/Seoul-Bike-Sharing-Demand_Regression/assets/63104472/7b4420e9-5830-45f9-89a1-44f196657f28

**Source:** [Arirand](https://www.youtube.com/watch?v=zod2Nawmlwk)



**Task:** Predicting Count of bikes rented at each hour(Rent Bike Count)


**Dataset:** [UCI Machine Learning](https://archive.ics.uci.edu/dataset/560/seoul+bike+sharing+demand)

**Complete JupyterNotebook:** [Link](https://github.com/Kmohamedalie/Seoul-Bike-Sharing-Demand/blob/master/Seoul%2Bbike%2Bsharing%2Bdemand/Seoul%20Bike%20sharing-%20Regression%20(SKLearn)%20.ipynb) 


**Metrics:**

| Algorithm         |    MAE    |     MSE       |     RMSE   |    R2     |

|------------------ |-----------|---------------|------------|-----------|

| Linear Regression | 318.88402 |	178535.387537 |	422.534481 |	0.560321 |




### **Additional Information about the dataset**

Currently Rental bikes are introduced in many urban cities for the enhancement of mobility comfort. It is important to make the rental bike available and accessible to the public at the right time as it lessens the waiting time. Eventually, providing the city with a stable supply of rental bikes becomes a major concern. The crucial part is the prediction of bike count required at each hour for the stable supply of rental bikes. 

The dataset contains weather information (Temperature, Humidity, Windspeed, Visibility, Dewpoint, Solar radiation, Snowfall, Rainfall), the number of bikes rented per hour and date information. 




### **Attribute information**

Date : year-month-day 


Rented Bike count - Count of bikes rented at each hour 


Hour - Hour of he day 


Temperature-Temperature in Celsius 


Humidity - % 


Windspeed - m/s 


Visibility - 10m 


Dew point temperature - Celsius  


Solar radiation - MJ/m2 


Rainfall - mm 


Snowfall - cm 


Seasons - Winter, Spring, Summer, Autumn 


Holiday - Holiday/No holiday 
 

Functional Day - NoFunc(Non Functional Hours), Fun(Functional hours)