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

# Importing the dataset
dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values

""" Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
"""

# Feature Scaling
"""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)"""

# Fitting Decision Tree regression model
from sklearn.tree import DecisionTreeRegressor
regressor= DecisionTreeRegressor(random_state=0)
regressor.fit(X,y)

# Predicting the results
y_pred= regressor.predict(6.5)

# Plotting regression
X_grid= np.arange(min(X), max(X), 0.01)
X_grid= X_grid.reshape(len(X_grid),1)
plt.scatter(X,y, c='red')
plt.plot(X_grid, regressor.predict(X_grid), color='green')
print("Finish")

