/#Data preprocessing
#importing the libraries
import numpy as np
import matplotlib.pyplot
import pandas as pd
#importing the dataset
dataset=pd.read_csv('Data.csv')
X=dataset.iloc[:,:-1].values
Y=dataset.iloc[:,3].values
#taking care of the missing data
from sklearn.impute import SimpleImputer
imputer=SimpleImputer(missing_values=np.nan, strategy='mean')
#imputer=SimpleImputer()
imputer=imputer.fit(X[:,1:3])#the upperbound is excluded
X[:,1:3]=imputer.transform(X[:,1:3])
print(X)
# Encoding categorical data
# Encoding the Independent Variable Country
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
columnTransformer = ColumnTransformer([('encoder', OneHotEncoder(),
[0])],remainder='passthrough')
X=np.array(columnTransformer.fit_transform(X),dtype=np.str)