#Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#Importing the dataset
dataset = pd.read_csv('Lingard.csv')
X = dataset.iloc[:, :-2].values
y = dataset.iloc[:, 7:9].values
print(dataset.head())

#Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)

print(X_train)
print(X_test)
print(y_train)
print(y_test)

#Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X_train = sc_X.fit_transform(X_train)
y_train = sc_y.fit_transform(y_train)

#Training the SVR model on the Training set
from sklearn.svm import SVR
regressor = SVR(kernel = 'rbf')
regressor.fit(X_train, y_train)

#Predicting the Test set results
y_pred = sc_y.inverse_transform(regressor.predict(sc_X.transform(X_test)))
np.set_printoptions(precision=2)
print(np.concatenate((y_pred.reshape(len(y_pred),2), y_test.reshape(len(y_test),2)),1))

#Evaluating the Model Performance
from sklearn.metrics import r2_score
r2_score(y_test, y_pred)