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The Data Science Workshop

You're reading from   The Data Science Workshop A New, Interactive Approach to Learning Data Science

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Product type Paperback
Published in Jan 2020
Publisher
ISBN-13 9781838981266
Length 818 pages
Edition 1st Edition
Languages
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Authors (5):
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Thomas Joseph Thomas Joseph
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Thomas Joseph
Andrew Worsley Andrew Worsley
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Andrew Worsley
Robert Thas John Robert Thas John
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Robert Thas John
Anthony So Anthony So
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Anthony So
Dr. Samuel Asare Dr. Samuel Asare
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Dr. Samuel Asare
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Toc

Table of Contents (18) Chapters Close

Preface 1. Introduction to Data Science in Python 2. Regression FREE CHAPTER 3. Binary Classification 4. Multiclass Classification with RandomForest 5. Performing Your First Cluster Analysis 6. How to Assess Performance 7. The Generalization of Machine Learning Models 8. Hyperparameter Tuning 9. Interpreting a Machine Learning Model 10. Analyzing a Dataset 11. Data Preparation 12. Feature Engineering 13. Imbalanced Datasets 14. Dimensionality Reduction 15. Ensemble Learning 16. Machine Learning Pipelines 17. Automated Feature Engineering

LogisticRegressionCV

LogisticRegressionCV is a class that implements cross-validation inside it. This class will train multiple LogisticRegression models and return the best one.

Exercise 7.06: Training a Logistic Regression Model Using Cross-Validation

The goal of this exercise is to train a logistic regression model using cross-validation and get the optimal R2 result. We will be making use of the Cars dataset that you worked with previously.

The following steps will help you complete the exercise:

  1. Open a new Colab notebook.
  2. Import the necessary libraries:
    # import libraries
    import pandas as pd

    In this step, you import pandas and alias it as pd. You will make use of pandas to read in the file you will be working with.

  3. Create headers for the data:
    # data doesn't have headers, so let's create headers
    _headers = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'car']

    In...

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