https://github.com/divakarkumarp/bike-sharing-demand-prediction
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.
https://github.com/divakarkumarp/bike-sharing-demand-prediction
Last synced: 2 months ago
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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.
- Host: GitHub
- URL: https://github.com/divakarkumarp/bike-sharing-demand-prediction
- Owner: divakarkumarp
- Created: 2021-10-10T14:11:31.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-02-14T15:56:27.000Z (over 2 years ago)
- Last Synced: 2025-01-22T08:13:26.366Z (4 months ago)
- Language: Jupyter Notebook
- Size: 4.02 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Bike Sharing Demand Prediction
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.
### Data Description
##### 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)
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### Overview:
This is a Colab Notebook,We selected to analyse a dataset relevant to Rental Bike Demand from Seoul, South Korea, which included climatic variables such as Temperature, Humidity, Rainfall, Snowfall, and Dew Point are all factors to consider. Temperature, among other factors. For the raw data that is available, First, a thorough pre-processing was carried out, followed by the regress and is the hourly rental bike count. in response to an Our model was able to explain the factors to some extent. Coordinating the hourly rental bike demand.
Technology and tools wise this project covers,
1.Python
2.Numpy and Pandas for data cleaning
3.Data visualization
4.Sklearn for model building
5.Google Colab Notebook
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### Technologies Used:
[
](https://numpy.org) [
](https://pandas.pydata.org) [
](https://seaborn.pydata.org) [
](https://matplotlib.org) [
](https://colab.research.google.com/)