text: # -*- coding: utf-8 -*-
  """
  Created on Mon Apr 27 21:57:18 2020
  
  @author: Prasenjit
  """
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  # Importing the dataset
  dataset = pd.read_csv(r"C:\Users\Prasenjit\Desktop\Python_Files\SVR\Position_Salaries.csv")
  X = dataset.iloc[:, 1:2].values
  y = dataset.iloc[:, 2].values
  
  # 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 = 1/3, random_state = 0)"""
  
  # Feature Scaling
  """from sklearn.preprocessing import StandardScaler
  sc_X = StandardScaler()
  X_train = sc_X.fit_transform(X_train)
  X_test = sc_X.transform(X_test)
  sc_y = StandardScaler()
  y_train = sc_y.fit_transform(y_train)"""
  
  #fitting SVR to Training set
  from sklearn.svm import SVR
  regressor = SVR(kernel = 'rbf')
  regressor.fit(X, y)
  
  
  
  #Predicting the Test Set results
  y_pred = regressor.predict(6.5)
  
  # Visulising the training SVR results 
  plt.scatter(X, y, color = 'red')
  plt.plot(X,regressor.predict(X), color = 'blue' )
  plt.title('Salary vs Experience')
  plt.xlabel('Year of Experience')
  plt.ylabel('Salary')
  plt.show()
  
  