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Contraceptive Method Choice Data Set
Download: Data Folder, Data Set Description

Abstract: Dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey.

Data Set Characteristics:  

Multivariate

Number of Instances:

1473

Area:

Life

Attribute Characteristics:

Categorical, Integer

Number of Attributes:

9

Date Donated

1997-07-07

Associated Tasks:

Classification

Missing Values?

No

Number of Web Hits:

82632


Source:

Origin:

This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey

Creator:

Tjen-Sien Lim (limt '@' stat.wisc.edu)

Donor:

Tjen-Sien Lim (limt '@' stat.wisc.edu)


Data Set 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.


Attribute Information:

1. Wife's age (numerical)
2. Wife's education (categorical) 1=low, 2, 3, 4=high
3. Husband's education (categorical) 1=low, 2, 3, 4=high
4. Number of children ever born (numerical)
5. Wife's religion (binary) 0=Non-Islam, 1=Islam
6. Wife's now working? (binary) 0=Yes, 1=No
7. Husband's occupation (categorical) 1, 2, 3, 4
8. Standard-of-living index (categorical) 1=low, 2, 3, 4=high
9. Media exposure (binary) 0=Good, 1=Not good
10. Contraceptive method used (class attribute) 1=No-use, 2=Long-term, 3=Short-term


Relevant Papers:

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. ([Web Link] or [Web Link])
[Web Link]


Papers That Cite This Data Set1:

Earl Harris Jr. Information Gain Versus Gain Ratio: A Study of Split Method Biases. The MITRE Corporation/Washington C. 2001. [View Context].

Soumya Ray and David Page. Generalized Skewing for Functions with Continuous and Nominal Attributes. Department of Computer Sciences and Department of Biostatistics and Medical Informatics, University of Wis. [View Context].

Jos'e L. Balc'azar. Rules with Bounded Negations and the Coverage Inference Scheme. Dept. LSI, UPC. [View Context].


Citation Request:

Please refer to the Machine Learning Repository's citation policy


[1] Papers were automatically harvested and associated with this data set, in collaboration with Rexa.info

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