Decision Tree Regression

#reading the data
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

dataset=pd.read_csv('Position_Salaries.csv')
X=dataset.iloc[:,1:2].values
Y=dataset.iloc[:,2].values


#fitting desicion tree
from sklearn.tree import DecisionTreeRegressor
regressor=DecisionTreeRegressor(random_state=0)
regressor.fit(X,Y)

#predict
Y_predict=regressor.predict(6.5)

#plot decision tree
plt.scatter(X,Y,color='red')
plt.plot(X,regressor.predict(X),color='blue')
plt.title('Truth or Bluff(Decision Tree Regression)')
plt.xlabel('level')
plt.ylabel('salary')
plt.show()

#plot decision tree for more resolution
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('Truth or Bluff(Decision Tree Regression)')
plt.xlabel('position level')
plt.ylabel('salary')
plt.show()