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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

# Polynomial Regression

In the previous section, you saw how to apply linear regression to predict the prices of houses in the Boston area. While the result is somewhat acceptable, it is not very accurate. This is because sometimes a linear regression line might not be the best solution to capture the relationships between the features and label accurately. In some cases, a curved line might do better.

Consider the series of points shown in Figure 6.10.

The series of points are stored in a file named `polynomial.csv`:

````x,y`
`1.5,1.5`
`2,2.5`
`3,4`
`4,4`
`5,4.5`
`6,5` ```

And plotted using a scatter plot:

````df = pd.read_csv('polynomial.csv')`
`plt.scatter(df.x,df.y)` ```

Using linear regression, you can try to plot a straight line cutting through most of the points:

````model = LinearRegression()`
` `
`x = df.x[0:6, np.newaxis] #---convert to 2D array---`
`y = df.y[0:6, np.newaxis] #---convert to 2D array---`
` `
`model.fit(x,y)`
` `
`#---perform prediction---`
`y_pred = model.predict...````
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