Completion of Python files

Code/Day 3_Multiple_Linear_Regression.py

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+# Importing the libraries
+import pandas as pd
+import numpy as np
+
+# Importing the dataset
+dataset = pd.read_csv('../datasets/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 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)
+
+# Fitting Multiple Linear Regression 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)
+