y = dataset.iloc[:2].values
#spliting the dataset
'''from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.5,random_state=0)'''
#feature scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X = sc_X.fit_transform(X)
y = sc_y.fit_transform(y)
#fitting svr into the dataset
from sklearn.svm import SVR
regressor = SVR(kernel='rbf')
regressor.fit(X, y)
#predicting the result(if you want to know what will the salary of a 6.5experience person should be )
y_pred = regressor.predict(sc_X.transform(np.array[[(6.5)]]))
y_pred = sc_y.inverse(y_pred) #inverse to get back the original values
# Visualising the SVR results (for higher resolution and smoother curve)
X_grid = np.arange(min(X), max(X), 0.01)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color='red')
plt.plot(X_grid, regressor.predict(X_grid), color='blue')
plt.title('Support Vector Regression')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()
