https://github.com/marktheo/bike-sharing-demand

Jupyter Notebook - Predicting bike rental numbers based on climate and temporal data
https://github.com/marktheo/bike-sharing-demand

decision-tree-classifier decision-tree-regression jupyter-notebook machine-learning scikit-learn

Last synced: 1 day ago
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Jupyter Notebook - Predicting bike rental numbers based on climate and temporal data

• Host: GitHub
• URL: https://github.com/marktheo/bike-sharing-demand
• Owner: marktheo
• Created: 2022-12-17T00:10:03.000Z (about 2 years ago)
• Default Branch: master
• Last Pushed: 2023-02-23T18:15:24.000Z (almost 2 years ago)
• Last Synced: 2024-12-17T01:38:04.926Z (about 2 months ago)
• Topics: decision-tree-classifier, decision-tree-regression, jupyter-notebook, machine-learning, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 6.26 MB
• Stars: 0
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Bike Sharing Demand

> ## Description

> [EN]: The main purpose of this chalenge is to predict the total count of bikes rented at each hour based on the test dataset, using the information given at each especific time period.

> 



> [PT]: O principal objetivo desse desafio é prever o número total de bikes alugadas a cada hora baseado em um conjunto de dados teste, usando as informações correspondentes ao devido horário.






    







> ## Data Fields

> Field | DataType | Sample | Description

> ----- | ----- | ----- | ----- |

> Datetime | date | yyyy-mm-dd hh | Horário

> Season | int | 1 - 4 | Estações do Ano

> Holiday | bool | 0 or 1 | Feriado

> Working Day | bool | 0 or 1 | Dia Útil

> Weather | int | 1 - 4 | Clima

> Temp | float | 36.50 | Temperatura

> Atemp | float | 39.75 | Sensação Térmica

> Humidity | int | 65 | Umidade do Ar

> Windspeed | float | 6.00 | Velocidade do Vento

> Casual | int | 5 | Nº Aluguéis s/ Registro

> Registered | int | 15 | Nº Aluguéis c/ Registro

> Count | int | 20 | Nº Total de Aluguéis




> - **Season**

>     - 1 - Spring [Primavera]

>     - 2 - Summer [Verão]

>     - 3 - Fall [Outono]

>     - 4 - Winter [Inverno]

> 



> - **Weather**

>     - 1 - Clean [Limpo]

>     - 2 - Cloudy [Nublado]

>     - 3 - Rainy [Chuvoso]

>     - 4 - Stormy [Tempestuoso]