Understanding Simple Linear Regression, Multiple Linear Regression and Polynomial Regression - ML

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd                                                                                      

dataset = pd.read_csv('D:\SEM 7\ML\data.csv')

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

Y = dataset.iloc[:, 1].values

fromsklearn.model_selection import train_test_split

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 1/3, random_state = 0)

fromsklearn.preprocessing import StandardScaler

sc_X = StandardScaler()

X_train = sc_X.fit_transform(X_train)

X_test = sc_X.transform(X_test)

fromsklearn.linear_model import LinearRegression

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 Experience (Training set)')

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 Experience (Test set)')

plt.xlabel('Years of Experience')

plt.ylabel('Salary')

plt.show()

Dataset:-

Y_test and Y_train :-

Variable Explorer:- 

Graph:-