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)
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, y_train = X[:num_training...