import numpy as np

import pandas as pd

from sklearn.preprocessing import LabelEncoder,OneHotEncoder

dataset = pd.read_csv("HR_comma_sep.csv")

x = dataset.iloc[:,:-1].values ##Independent variable

y = dataset.iloc[:,9].values ##Dependent variable

##Encoding the categorical variables

le_x1 = LabelEncoder()

x[:,7] = le_x1.fit_transform(x[:,7])

le_x2 = LabelEncoder()

x[:,8] = le_x1.fit_transform(x[:,8])

ohe = OneHotEncoder(categorical_features = [7,8])

x = ohe.fit_transform(x).toarray()

##splitting the dataset in training and testing data

from sklearn.cross_validation import train_test_split

y = pd.factorize(dataset['left'].values)[0].reshape(-1, 1)

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 0)

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)

from sklearn.ensemble import RandomForestRegressor

regressor = RandomForestRegressor(n_estimators = 10, random_state = 0)

regressor.fit(x_train, y_train)

y_pred = regressor.predict(x_test)

print(y_pred)

from sklearn.metrics import r2_score

r2_score(y_test , y_pred)

from sklearn.model_selection import cross_val_score

accuracies = cross_val_score(estimator = regressor, X = x_train, y = y_train, cv = 10)

accuracies.mean()

accuracies.std()