Welcome to Bike Sharing Demand Prediction AI Challenge!

The aim of the challenge is to predict the hourly demand for rental bikes using the given dataset. The dataset consists of 3 csv files:

TRAIN.csv
TEST.csv
sample_submission.csv

TRAIN.csv consists of 15 attributes:

Index
Date – day/month/year
Hour - Hour of the day
Temperature - 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
Functioning Day - No(Non-Functional Hours), Yes(Functional hours)
Rented Bike count - Count of bikes rented at each hour (Target Attribute)

This data should be used to train the model, no additional data is allowed to be used for the training process.

TEST.csv consists of the testing data required for prediction and consists of 14 attributes:

Index
Date - day/month/year
Hour - Hour of the day
Temperature - 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
Functioning Day - No(Non-Functional Hours), Yes(Functional hours)

A "sample_submission".csv is also provided for ease of the participants. To know the exact submission format, please check out "Submission Guidelines".

Akash Gupta's Certificate

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