# 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

) containing the size of houses and their asking prices (in thousands) for a particular area:`house_sizes_prices_svm.csv`

`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)`

`data = pd.read_csv('house_sizes_prices_svm.csv')`

`sns.lmplot('size', 'price',`

`data=data,`

`hue='sold',`

`palette='Set2',`

`fit_reg...`