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## You're reading fromPython Machine Learning (Wiley)

Product type Book
Published in Apr 2019
Publisher Wiley
ISBN-13 9781119545637
Pages 320 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Wei-Meng Lee

1. Cover
2. Introduction
3. CHAPTER 1: Introduction to Machine Learning 4. CHAPTER 2: Extending Python Using NumPy 5. CHAPTER 3: Manipulating Tabular Data Using Pandas 6. CHAPTER 4: Data Visualization Using matplotlib 7. CHAPTER 5: Getting Started with Scikit‐learn for Machine Learning 8. CHAPTER 6: Supervised Learning—Linear Regression 9. CHAPTER 7: Supervised Learning—Classification Using Logistic Regression 10. CHAPTER 8: Supervised Learning—Classification Using Support Vector Machines 11. CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN) 12. CHAPTER 10: Unsupervised Learning—Clustering Using K‐Means 13. CHAPTER 11: Using Azure Machine Learning Studio 14. CHAPTER 12: Deploying Machine Learning Models 15. Index

# Getting Started with Scikit‐learn

The easiest way to get started with machine learning with Scikit‐learn is to start with linear regression. Linear regression is a linear approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variables). For example, imagine that you have a set of data comprising the heights (in meters) of a group of people and their corresponding weights (in kg):

````%matplotlib inline`
`import matplotlib.pyplot as plt`
` `
`# represents the heights of a group of people in meters`
`heights = [[1.6], [1.65], [1.7], [1.73], [1.8]]`
` `
`# represents the weights of a group of people in kgs`
`weights = [[60], [65], [72.3], [75], [80]]`
` `
`plt.title('Weights plotted against heights')`
`plt.xlabel('Heights in meters')`
`plt.ylabel('Weights in kilograms')`
` `
`plt.plot(heights, weights, 'k.')`
` `
`# axis range for x and y`
`plt.axis([1.5, 1.85, 50, 90])`
`plt.grid(True)` ```

When you...

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