OpenML
Household-monthly-electricity-bill

Household-monthly-electricity-bill

active ARFF Database: Open Database, Contents: Database Contents Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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  • Computer Systems Machine Learning
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Introduction The idea behind this dataset is to see how the number of people and the home size affects the monthly electricity consumption in the household. Column decription: Column Explanation num_rooms Number of room in the house num_people Number of people in the house housearea Area of the house is_ac Is AC present in the house? is_tv Is TV present in the house? is_flat Is house a flat? avemonthlyincome Average monthly income of the household num_children Number of children in the house is_urban Is the house present in an urban area amount_paid Amount paid as the monthly bill Acknowledgements This dataset was prepared as a mock up dataset for practice use

10 features

num_roomsnumeric7 unique values
0 missing
num_peoplenumeric13 unique values
0 missing
houseareanumeric990 unique values
0 missing
is_acnumeric2 unique values
0 missing
is_tvnumeric2 unique values
0 missing
is_flatnumeric2 unique values
0 missing
ave_monthly_incomenumeric1000 unique values
0 missing
num_childrennumeric5 unique values
0 missing
is_urbannumeric2 unique values
0 missing
amount_paidnumeric1000 unique values
0 missing

19 properties

1000
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
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.
0
Number of nominal attributes.
0.01
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
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.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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