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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

# Using SVM for Real‐Life Problems

We will end this chapter by applying SVM to a common problem in our daily lives. Consider the following dataset (saved in a file named house_sizes_prices_svm.csv) containing the size of houses and their asking prices (in thousands) for a particular area:

size,price,sold
550,50,y
1000,100,y
1200,123,y
1500,350,n
3000,200,y
2500,300,y
750, 45,y
1500,280,n
780,400,n
1200, 450,n
2750, 500,n

The third column indicates if the house was sold. Using this dataset, you want to know if a house with a specific asking price would be able to sell.

First, let's plot out the points:

%matplotlib inline

import pandas as pd
import numpy as np
from sklearn import svm
import matplotlib.pyplot as plt
import seaborn as sns; sns.set(font_scale=1.2)