Artificial Intelligence with Python

Estimating housing prices using a Support Vector Regressor

Let's see how to use the SVM concept to build a regressor to estimate the housing prices. We will use the dataset available in sklearn where each data point is define, by 13 attributes. Our goal is to estimate the housing prices based on these attributes.

Create a new Python file and import the following packages:

import numpy as np 
from sklearn import datasets 
from sklearn.svm import SVR 
from sklearn.metrics import mean_squared_error, explained_variance_score 
from sklearn.utils import shuffle 

Load the housing dataset:

# Load housing data 
data = datasets.load_boston()  

Let's shuffle the data so that we don't bias our analysis:

# Shuffle the data X, y = shuffle(data.data, data.target, random_state=7) ...