Building a single-variable regressor

Let's see how to build a single-variable regression model. Create a new Python file and import the following packages:

import pickle

import numpy as np
from sklearn import linear_model 
import sklearn.metrics as sm 
import matplotlib.pyplot as plt

We will use the file data_singlevar_regr.txt provided to you. This is our source of data:

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

It's a comma-separated file, so we can easily load it using a one-line function call:

# Read data
data = np.loadtxt(input_file, delimiter=',') 
X, y = data[:, :-1], data[:, -1]

Split it into training and testing:

# Train and test split 
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]

# Test data
X_test, y_test = X[num_training:], y[num_trai...