text: #!/usr/bin/env python3
  # -*- coding: utf-8 -*-
  """
  Created on Wed Apr  4 21:01:58 2018
  
  @author: sajjadsalaria
  """
  
  # Importing the libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  # Importing the dataset
  dataset = pd.read_csv('50_Startups.csv')
  X = dataset.iloc[:, :-1].values
  y = dataset.iloc[:, 4].values
                             
  # Encoding categorical data
  from sklearn.preprocessing import LabelEncoder, OneHotEncoder
  labelencoder = LabelEncoder()
  X[:, 3] = labelencoder.fit_transform(X[:, 3])
  onehotencoder = OneHotEncoder(categorical_features = [3])
  X = onehotencoder.fit_transform(X).toarray()
  
  # Avoiding the Dummy Variable Trap
  X = X[:, 1:]
  
  # Splitting the dataset into the Training set and Test set
  from sklearn.cross_validation import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, 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 Multi Linear Regreaaion to the Training set
  from sklearn.linear_model import LinearRegression
  regressor = LinearRegression()
  regressor.fit(X_train, y_train)
  
  # Predicting the Test set results
  y_pred = regressor.predict(X_test)                      
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  