Estimating housing prices using a Support Vector Regressor

Let's see how to use the SVM concept to build a regressor to estimate housing prices. We will use the dataset available in sklearn where each datapoint is defined by 13 attributes.

Our goal is to estimate 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)

Split the dataset into training and testing in an 80/20 format:

# Split the data into training and testing datasets 
num_training = int(0.8 * len(X))
X_train...