SalaryModel.py

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split 
from sklearn.linear_model import LinearRegression

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)



plt.scatter(X_train, y_train, color='red')
plt.plot(X_train, regressor.predict(X_train), color='blue')
plt.title('Salary vs Years of Experience')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()


plt.scatter(X_test, y_test, color='red')
plt.plot(X_train, regressor.predict(X_train), color='blue')
plt.title('Salary vs Years of Experience')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show()