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Machine Learning - Data preprocessing

hi_iam_chris profile image Kristijan Pajtasev Updated on ・2 min read

As part of my trying to complete Machine Learning A-Z Udemy course, this series of posts, starting with this one, will contain note I gather from it.

Dependent vs independent variables

  • Dependent – variable being tested and measured – predicted result
  • Independent – variable being changed or controlled – features

Used libraries(python):

  • numpy, a library containing mathematical tools
  • matplotlib.pyplot, plotting library
  • pandas – importing datasets
  • sklearn.preprocessing – library for processing data

Importing dataset with pandas:

import pandas as pd
pd.read_csv(FILE_NAME)
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Missing data

Option 1:

  • remove rows with missing data
  • dangerous because we might be losing valuable information Option 2:
  • set missing values to mean of that feature

Library used:

sklearn.preprocessing.Imputer
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Categorical data

Labels need to be converted into numbers - Euclidean distance can’t be calculated on labels
Library:

sklearn.preprocessing.LabelEncoder
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Problem with LabelEncoder: converting labels into numbers can lead to problems as numbers can be ordered. Labels not necessary
Solution: Creating feature per label
Library: sklearn.preprocessing.OneHotEncoder

Splitting data

For creating a model, data needs to be split into two sets, train and test. The train set is the one we use for creating a model, and the test is one we use to evaluate that mode's correctness.
Library: sklearn.model_selection.train_test_split
Usual ration: 70-80% for train data

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Feature scaling

One feature, because of large values, can dominate the smaller number value feature. This is why all features should be scaled to the same scale.
Option 1, standardization:
Each value is reduced by the mean and divided by the standard deviation.

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Option 2, normalization:
Reduce each x by minimal x value. Ather that, divide by the difference of the maximum and minimum value of x.

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Library:

sklearn.preprocessing.StandardScaler
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