text: # -*- coding: utf-8 -*-
  """simple_linear_regression.ipynb
  
  Automatically generated by Colaboratory.
  
  Original file is located at
      https://colab.research.google.com/drive/19sVV5Qd7ciQ3z6MlswBo1Oag1PM0v0pi
  """
  
  ## Importing the Libraries
  import pandas as pd
  import matplotlib.pyplot as plt
  
  ## Step 2: Importing the dataset
  data = pd.read_csv("https://raw.githubusercontent.com/codePerfectPlus/DataAnalysisWithJupyter/master/SalaryVsExperinceModel/Salary.csv")
  X = data.iloc[:, :-1].values
  y = data.iloc[:, -1].values
  data.head()
  
  ## Step 3: Splitting the dataset into the Training set and Test set
  from sklearn.model_selection import train_test_split
  train_X, test_X, train_y, test_y = train_test_split(X, y, test_size = 0.3, random_state=12)
  
  ## Step 4: Training the Simple Linear Regression model on the Training set
  from sklearn.linear_model import LinearRegression
  lr = LinearRegression()
  lr.fit(train_X, train_y)
  
  ## Step 5: Predicting the Test set results
  predicted_y = lr.predict(test_X)
  
  ## Step 6: Comparing the Test Set with Predicted Values
  df = pd.DataFrame({'Real Values':test_y,"Predict Value":predicted_y})
  df.head()
  
  ## Step 7: Visualising the Test set results
  
  plt.scatter(test_X, test_y, color = 'red')
  plt.scatter(test_X, predicted_y, color = 'green')
  plt.plot(train_X, lr.predict(train_X), color = 'black')
  plt.title('Salary vs Experience (Result)')
  plt.xlabel('YearsExperience')
  plt.ylabel('Salary')
  plt.savefig("slr.jpeg")
  plt.show()