Data
tamilnadu-electricity

tamilnadu-electricity

deactivated ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
0 likes downloaded by 23 people , 29 total downloads 0 issues 0 downvotes
  • OpenML100 study_123 study_14 study_34 study_7
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: [K.Kalyani](kkalyanims@gmail.com)T.U.K Arts College,Karanthai,Thanjavur Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Tamilnadu+Electricity+Board+Hourly+Readings) - 2013 Please cite: None ### Description Tamilnadu Electricity Board Hourly Readings dataset. ### Source ``` K.Kalyani ,kkalyanims '@' gmail.com, T.U.K Arts College,Karanthai,Thanjavur. ``` ### Data Set Information Real-time readings were collected from residential, commercial, industrial and agriculture to find the accuracy consumption in Tamil Nadu, around Thanajvur. Note: the attribute Sector was removed from original source since it was constant to all instances. ### Attribute Information: ``` 1 - ForkVA (V1) : real 2 - ForkW (V2) : real 3 - ServiceID (V3): factor 4 - Type (Class): - Bank - AutomobileIndustry - BpoIndustry - CementIndustry - Farmers1 - Farmers2 - HealthCareResources - TextileIndustry - PoultryIndustry - Residential(individual) - Residential(Apartments) - FoodIndustry - ChemicalIndustry - Handlooms - FertilizerIndustry - Hostel - Hospital - Supermarket - Theatre - University

4 features

Class (target)nominal20 unique values
0 missing
V1numeric44778 unique values
0 missing
V2numeric44777 unique values
0 missing
V3nominal31 unique values
0 missing

62 properties

45781
Number of instances (rows) of the dataset.
4
Number of attributes (columns) of the dataset.
20
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.
2
Number of numeric attributes.
2
Number of nominal attributes.
-0.01
First quartile of skewness among attributes of the numeric type.
0.5
Mean of means among attributes of the numeric type.
0.29
First quartile of standard deviation of attributes of the numeric type.
1
Average class difference between consecutive instances.
4.25
Average mutual information between the nominal attributes and the target attribute.
4.94
Second quartile (Median) of entropy among attributes.
4.25
Entropy of the target attribute values.
0.16
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
-1.2
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
25.5
Average number of distinct values among the attributes of the nominal type.
0.5
Second quartile (Median) of means among attributes of the numeric type.
1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
-0
Mean skewness among attributes of the numeric type.
4.25
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
6.35
Percentage of instances belonging to the most frequent class.
0.29
Mean standard deviation of attributes of the numeric type.
-0
Second quartile (Median) of skewness among attributes of the numeric type.
2906
Number of instances belonging to the most frequent class.
4.94
Minimal entropy among attributes.
0
Percentage of binary attributes.
0.29
Second quartile (Median) of standard deviation of attributes of the numeric type.
4.94
Maximum entropy among attributes.
-1.2
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
4.94
Third quartile of entropy among attributes.
-1.2
Maximum kurtosis among attributes of the numeric type.
0.5
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
-1.2
Third quartile of kurtosis among attributes of the numeric type.
0.5
Maximum of means among attributes of the numeric type.
4.25
Minimal mutual information between the nominal attributes and the target attribute.
50
Percentage of numeric attributes.
0.5
Third quartile of means among attributes of the numeric type.
4.25
Maximum mutual information between the nominal attributes and the target attribute.
20
The minimal number of distinct values among attributes of the nominal type.
50
Percentage of nominal attributes.
4.25
Third quartile of mutual information between the nominal attributes and the target attribute.
31
The maximum number of distinct values among attributes of the nominal type.
-0.01
Minimum skewness among attributes of the numeric type.
4.94
First quartile of entropy among attributes.
-0
Third quartile of skewness among attributes of the numeric type.
-0
Maximum skewness among attributes of the numeric type.
0.29
Minimum standard deviation of attributes of the numeric type.
-1.2
First quartile of kurtosis among attributes of the numeric type.
0.29
Third quartile of standard deviation of attributes of the numeric type.
0.29
Maximum standard deviation of attributes of the numeric type.
3.05
Percentage of instances belonging to the least frequent class.
1397
Number of instances belonging to the least frequent class.
0.5
First quartile of means among attributes of the numeric type.
7.78
Standard deviation of the number of distinct values among attributes of the nominal type.
4.94
Average entropy of the attributes.
0
Number of binary attributes.
4.25
First quartile of mutual information between the nominal attributes and the target attribute.
-1.2
Mean kurtosis among attributes of the numeric type.

88 tasks

10108 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
43 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
1311 runs - target_feature: Class
1307 runs - target_feature: Class
1307 runs - target_feature: Class
1306 runs - target_feature: Class
1305 runs - target_feature: Class
1305 runs - target_feature: Class
1304 runs - target_feature: Class
1304 runs - target_feature: Class
1304 runs - target_feature: Class
1303 runs - target_feature: Class
1302 runs - target_feature: Class
1302 runs - target_feature: Class
1302 runs - target_feature: Class
1302 runs - target_feature: Class
1302 runs - target_feature: Class
1302 runs - target_feature: Class
1302 runs - target_feature: Class
1301 runs - target_feature: Class
1301 runs - target_feature: Class
1300 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
Define a new task