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The Unsupervised Learning Workshop

You're reading from   The Unsupervised Learning Workshop Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800200708
Length 550 pages
Edition 1st Edition
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Authors (3):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Christopher Kruger Christopher Kruger
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Christopher Kruger
Aaron Jones Aaron Jones
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Aaron Jones
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Toc

Table of Contents (11) Chapters Close

Preface
1. Introduction to Clustering 2. Hierarchical Clustering FREE CHAPTER 3. Neighborhood Approaches and DBSCAN 4. Dimensionality Reduction Techniques and PCA 5. Autoencoders 6. t-Distributed Stochastic Neighbor Embedding 7. Topic Modeling 8. Market Basket Analysis 9. Hotspot Analysis Appendix

4. Dimensionality Reduction Techniques and PCA

Activity 4.01: Manual PCA versus scikit-learn

Solution:

  1. Import the pandas, numpy, and matplotlib plotting libraries and the scikit-learn PCA model:
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.decomposition import PCA
  2. Load the dataset and select only the sepal features as per the previous exercises. Display the first five rows of the data:
    df = pd.read_csv('../Seed_Data.csv')
    df = df[['A', 'LK']]
    df.head()

    The output is as follows:

    Figure 4.36: The first five rows of the data

  3. Compute the covariance matrix for the data:
    cov = np.cov(df.values.T)
    cov

    The output is as follows:

    array([[8.46635078, 1.22470367],
           [1.22470367, 0.19630525]])
  4. Transform the data using the scikit-learn API and only the first principal component. Store the transformed data in the sklearn_pca variable:
    model = PCA(n_components=1)
    sklearn_pca...
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