Data
seoul_bike_sharing_demand

seoul_bike_sharing_demand

active ARFF CC BY 4.0 Visibility: public Uploaded 23-07-2024 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.

14 features

13 (target)numeric2166 unique values
0 missing
0string365 unique values
0 missing
1numeric24 unique values
0 missing
2numeric546 unique values
0 missing
3numeric90 unique values
0 missing
4numeric65 unique values
0 missing
5numeric1789 unique values
0 missing
6numeric556 unique values
0 missing
7numeric345 unique values
0 missing
8numeric61 unique values
0 missing
9numeric51 unique values
0 missing
10nominal4 unique values
0 missing
11nominal2 unique values
0 missing
12nominal2 unique values
0 missing

19 properties

8760
Number of instances (rows) of the dataset.
14
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.
10
Number of numeric attributes.
3
Number of nominal attributes.
14.29
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
-177.43
Average class difference between consecutive instances.
71.43
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
21.43
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
2
Number of binary attributes.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: 13
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