SVR Technique

 # -*- coding: utf-8 -*-

"""

Created on Mon Oct 30 13:27:39 2023

@author: Syed Kamran Bukhari

"""

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

#Import CSV File

dataset = pd.read_csv('Position_Salaries.csv')

X= dataset.iloc[:,1:2].values

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

from sklearn.preprocessing import StandardScaler

sc_x = StandardScaler()

X= sc_x.fit_transform(X)

sc_y = StandardScaler()

Y=Y.reshape(len(Y),1)

Y= sc_y.fit_transform(Y)

from sklearn.svm import SVR

regressor= SVR(kernel='rbf')

Y=Y.reshape(len(Y),)

regressor.fit(X,Y)

Y_pred=regressor.predict(sc_x.transform([[6.5]]))

print('The Predicted value of Y is = ',Y_pred)

Y_pred=Y_pred.reshape(len(Y_pred), 1)

Y_pred= sc_y.inverse_transform(Y_pred)

print('The New Predicted value of Y is = ',Y_pred)

Y=Y.reshape(len(Y),1)

ya = regressor.predict(X)

ya = ya.reshape(len(ya),1)

plt.scatter(sc_x.inverse_transform(X), sc_y.inverse_transform(Y), color='red')

plt.plot(sc_x.inverse_transform(X), sc_y.inverse_transform(ya), color='blue')

plt.title('Position vs Salary')

plt.xlabel('Position')

plt.ylabel('Salary')

plt.show()

X_grid = np.arange(min(X),max(X), 0.1)

X_grid= X_grid.reshape(len(X_grid),1)

yaa = regressor.predict(X_grid)

yaa=yaa.reshape(len(yaa),1)

plt.scatter(sc_x.inverse_transform(X), sc_y.inverse_transform(Y), color='red')

plt.plot(sc_x.inverse_transform(X), sc_y.inverse_transform(ya), color='blue')

plt.title('Position vs Salary')

plt.xlabel('Position')

plt.ylabel('Salary')

plt.show()