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Jupyter Notebook - Predicting Bike Rental Numbers Based on Climate and Temporal Data

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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]

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Jupyter Notebook - Predicting Bike Rental Numbers Based on Climate and Temporal Data

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