Practical - 5

Aim: What is the effect of discretization, continuization, Normalization, Randomization on the data w.r.t. Orange?

How to work with Orange in Python and vice-versa?

Text / Data Preprocessing with Orange tool.

Dataset Description:

Name: Red Wine Quality

Link: https://www.kaggle.com/uciml/red-wine-quality-cortez-et-al-2009

This datasets is related to red variants of the Portuguese "Vinho Verde" wine.

Columns:

1 - fixed acidity

2 - volatile acidity

3 - citric acid

4 - residual sugar

5 - chlorides

6 - free sulfur dioxide

7 - total sulfur dioxide

8 - density

9 - pH

10 - sulphates

11 - alcohol

12 - quality (score between 0 and 10)

Now we will perform different pre-processing techniques using orange tool.

1. Discretization

Output of discretization.

2. Continuization

3. Randomization.

4. Normalization using python script.

from Orange.data import Table
from Orange.preprocess import Normalize
data = in_data
normalizer = Normalize(norm_type=Normalize.NormalizeBySpan)
out_data = normalizer(data)

Output of python script

Drive link with orange files for reference