Data
seoul_bike_sharing_demand_cat

seoul_bike_sharing_demand_cat

active ARFF CC BY 4.0 Visibility: public Uploaded 04-09-2024 by Bruno Belucci Teixeira
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From original source: ----- The dataset contains count of public bicycles rented per hour in the Seoul Bike Sharing System, with corresponding weather data and holiday information Additional Information 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. Has Missing Values? No ----- Columns with index [0] are dates and were dates and they were converted to colums ('day', 'month', 'year', 'week_day', 'timestamp').

18 features

rented_bike_count (target)numeric2166 unique values
0 missing
Hournumeric24 unique values
0 missing
Temperature(C)numeric546 unique values
0 missing
Humidity(%)numeric90 unique values
0 missing
Wind speed (m/s)numeric65 unique values
0 missing
Visibility (10m)numeric1789 unique values
0 missing
Dew point temperature(C)numeric556 unique values
0 missing
Solar Radiation (MJ/m2)numeric345 unique values
0 missing
Rainfall(mm)numeric61 unique values
0 missing
Snowfall (cm)numeric51 unique values
0 missing
Seasonsnominal4 unique values
0 missing
Holidaynominal2 unique values
0 missing
Functioning Daynominal2 unique values
0 missing
daynumeric31 unique values
0 missing
monthnumeric12 unique values
0 missing
yearnumeric2 unique values
0 missing
week_daynominal7 unique values
0 missing
timestampnumeric365 unique values
0 missing

19 properties

8760
Number of instances (rows) of the dataset.
18
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
14
Number of numeric attributes.
4
Number of nominal attributes.
Number of instances belonging to the least frequent class.
2
Number of binary attributes.
11.11
Percentage of binary attributes.
0
Percentage of instances having missing values.
-177.43
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
77.78
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
22.22
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: rented_bike_count
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