Machine Learning

In this article, we will show one example of Simple Linear Regression Program.

The formula is as follow:

y = b0 + b1 * x1

y = Dependent Variable (DV)
b0 = Constant
b1 = Coefficient
x1 = Independent Variable (IV)

Here is one example of the program:

Import Libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

Import the dataset

dataset = pd.read_csv(‘dataset-file.csv’)

x = dataset.iloc[:, :-1].values

x = dataset.iloc[:, -1].values

Replace the dataset-file.csv file with your file and mention full path of the file.

Splitting the dataset into the Training set and Test set

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)

Training the Simple Linear Regression model on the Training set

from sklearn.linear_model import LinearRegression

regressor = LinearRegression()

regressor.fit(x_train, y_train)

Prediction the Test set result

y_pred = regressor.predict(x_test)

Visualizing the Training set results

plt.scatter(x_train, y_train, color = ‘red’)

plt.plot(x_train, regressor.predit(x_train), color = ‘blue’)

plt.title(‘Name of the graph’)

plt.xlabel(‘Name of the x label’)

plt.ylabel(‘name of the y label’)

plt.show()

Visualizing the Test set results

plt.scatter(x_test, y_test, color = ‘red’)

plt.plot(x_train, regressor.predit(x_train), color = ‘blue’)

plt.title(‘Name of the graph’)

plt.xlabel(‘Name of the x label’)

plt.ylabel(‘name of the y label’)

plt.show()

We have shown Simple Linear Regression Program.

[Need assistance to fix this error or install tools? We’ll help you.]