Data
Drug-Classification

Drug-Classification

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Onur Yildirim
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Context Since as a beginner in machine learning it would be a great opportunity to try some techniques to predict the outcome of the drugs that might be accurate for the patient. Content The target feature is Drug type The feature sets are: Age Sex Blood Pressure Levels (BP) Cholesterol Levels Na to Potassium Ration Inspiration The main problem here in not just the feature sets and target sets but also the approach that is taken in solving these types of problems as a beginner. So best of luck.

6 features

Agenumeric57 unique values
0 missing
Sexstring2 unique values
0 missing
BPstring3 unique values
0 missing
Cholesterolstring2 unique values
0 missing
Na_to_Knumeric198 unique values
0 missing
Drugstring5 unique values
0 missing

19 properties

200
Number of instances (rows) of the dataset.
6
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.
2
Number of numeric attributes.
0
Number of nominal attributes.
0.03
Number of attributes divided by the number of instances.
33.33
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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