In the previous section, we discussed how to build a regression model for a single variable. In this section, we will deal with multidimensional data. Create a new Python file and import the following packages:

import numpy as np 
from sklearn import linear_model 
import sklearn.metrics as sm 
from sklearn.preprocessing import PolynomialFeatures 

We will use the file data_multivar_regr.txt provided to you.

# Input file containing data 
input_file = 'data_multivar_regr.txt' 

This is a comma-separated file, so we can load it easily with a one-line function call:

# Load the data from the input file 
data = np.loadtxt(input_file, delimiter=',') 
X, y = data[:, :-1], data[:, -1] 

Split the data into training and testing:

# Split data into training and testing  
num_training = int(0.8 * len(X)) 
num_test = len(X) - num_training 
 
# Training data 
X_train, y_train = X[:num_training], y[:num_training...