Computer scienceData scienceInstrumentsScikit-learnData preprocessing with scikit-learn

Feature scaling in scikit-learn

Normalizing the samples

Report a typo

Suppose you have a fragment of the wine dataset:

+----+-----------+--------------+-------+---------------------+-------------+
|    |   alcohol |   malic_acid |   ash |   alcalinity_of_ash |   magnesium |
|----+-----------+--------------+-------+---------------------+-------------|
|  0 |     14.23 |         1.71 |  2.43 |                15.6 |         127 |
|  1 |     13.2  |         1.78 |  2.14 |                11.2 |         100 |
|  2 |     13.16 |         2.36 |  2.67 |                18.6 |         101 |
|  3 |     14.37 |         1.95 |  2.5  |                16.8 |         113 |
|  4 |     13.24 |         2.59 |  2.87 |                21   |         118 |
+----+-----------+--------------+-------+---------------------+-------------+

Normalize each sample individually to a unit norm. Use the default norm. Print out the transformed values for the 4th sample of the dataframe.

Write a program in Python 3

import pandas as pd

data = {'alcohol': [14.23, 13.2, 13.16, 14.37, 13.24, 14.2, 14.39, 14.06, 14.83, 13.86, 14.1, 14.12, 13.75, 14.75, 14.38, 13.63, 14.3, 13.83, 14.19, 13.64],

'malic_acid': [1.71, 1.78, 2.36, 1.95, 2.59, 1.76, 1.87, 2.15, 1.64, 1.35, 2.16, 1.48, 1.73, 1.73, 1.87, 1.81, 1.92, 1.57, 1.59, 3.1],

'ash': [2.43, 2.14, 2.67, 2.5, 2.87, 2.45, 2.45, 2.61, 2.17, 2.27, 2.3, 2.32, 2.41, 2.39, 2.38, 2.7, 2.72, 2.62, 2.48, 2.56],

'alcalinity_of_ash': [15.6, 11.2, 18.6, 16.8, 21.0, 15.2, 14.6, 17.6, 14.0, 16.0, 18.0, 16.8, 16.0, 11.4, 12.0, 17.2, 20.0, 20.0, 16.5, 15.2],

'magnesium': [127.0, 100.0, 101.0, 113.0, 118.0, 112.0, 96.0, 121.0, 97.0, 98.0, 105.0, 95.0, 89.0, 91.0, 102.0, 112.0, 120.0, 115.0, 108.0, 116.0]}

df = pd.DataFrame(data)

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