#Multiple Linear Regression

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset = pd.read_csv('50_Startups.csv')

X = dataset.iloc[:,:-1].values
y = dataset.iloc[:, 4].values

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 3] = labelencoder_X.fit_transform(X[:, 3])
onehotencoder = OneHotEncoder(categorical_features = [3])
X = onehotencoder.fit_transform(X).toarray()


#Avoiding dummy variable trap
X =X[:,1:]

from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state = 0)


from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train,y_train)

#predicting
y_pred = regressor.predict(X_test)