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Contraceptive-Method-Choice

Contraceptive-Method-Choice

active ARFF CC0: Public Domain Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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1. Title: Contraceptive Method Choice 2. Sources: (a) Origin: This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey (b) Creator: Tjen-Sien Lim (limtstat.wisc.edu) (c) Donor: Tjen-Sien Lim (limtstat.wisc.edu) (d) Date: June 7, 1997 3. Past Usage: Lim, T.-S., Loh, W.-Y. Shih, Y.-S. (1999). A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-three Old and New Classification Algorithms. Machine Learning. Forthcoming. (ftp://ftp.stat.wisc.edu/pub/loh/treeprogs/quest1.7/mach1317.pdf or (http://www.stat.wisc.edu/limt/mach1317.pdf) 4. Relevant Information: This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of interview. The problem is to predict the current contraceptive method choice (no use, long-term methods, or short-term methods) of a woman based on her demographic and socio-economic characteristics. 5. Number of Instances: 1473 6. Number of Attributes: 10 (including the class attribute) 7. Attribute Information: Wife's age (numerical) Wife's education (categorical) 1=low, 2, 3, 4=high Husband's education (categorical) 1=low, 2, 3, 4=high Number of children ever born (numerical) Wife's religion (binary) 0=Non-Islam, 1=Islam Wife's now working? (binary) 0=Yes, 1=No Husband's occupation (categorical) 1, 2, 3, 4 Standard-of-living index (categorical) 1=low, 2, 3, 4=high Media exposure (binary) 0=Good, 1=Not good Contraceptive method used (class attribute) 1=No-use ,2=Long-term,3=Short-term 8. Missing Attribute Values: None

10 features

24numeric34 unique values
0 missing
2numeric4 unique values
0 missing
3numeric4 unique values
0 missing
3.1numeric15 unique values
0 missing
1numeric2 unique values
0 missing
1.1numeric2 unique values
0 missing
2.1numeric4 unique values
0 missing
3.2numeric4 unique values
0 missing
0numeric2 unique values
0 missing
1.2numeric3 unique values
0 missing

19 properties

1472
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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