linear_regression.py

from turtle import color
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset = pd.read_csv('Salary_Data.csv')
x = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values

x_train, x_test, y_train, y_test = train_test_split(
    x, y, test_size=1/3, random_state=0)

regressor = LinearRegression()
regressor.fit(x_train, y_train)


y_pred = regressor.predict(x_test)

# Visualising the training set
plt.scatter(x_train, y_train, color='red')
plt.plot(x_train, regressor.predict(x_train), color='blue')
plt.title('salary vs Experience (Training set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()

# Visualising the test set
plt.scatter(x_test, y_test, color='red')
plt.plot(x_train, regressor.predict(x_train), color='blue')
plt.title('salary vs Experience (Training set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()