Data Description

This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature.

This data set includes 201 instances of one class and 85 instances of another class. The instances are described by 9 attributes, some of which are linear and some are nominal.

Data Pre-processing

  1. Missing value handling

In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data

Select dataset in orange and add preprocess -> select impute missing values

2. Normalization

Normalization is the process of organizing data in a database. This includes creating tables and establishing relationships between those tables according to rules designed both to protect the data and to make the database more flexible by eliminating redundancy and inconsistent dependency

add preprocess -> select normalize features.

3. select columns

select target column

4 . Feature selection

Feature Selection is the process where you automatically or manually select those features which contribute most to your prediction variable or output in which you are interested in. Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features

connect data table with rank

5. encoding

Encoding is the process of converting data from one form to another. it refers to a specific type of encoded data. There are several types of encoding, including image encoding, audio and video encoding, and character encoding.

with pre-processing

without pre-processing


chart — 1

chart -2






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