Decision Tree Regression - Cosmos

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#importing the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline

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

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#fitting the decision tree regression model to the dataset
from sklearn.tree import DecisionTreeRegressor
regressor=DecisionTreeRegressor(random_state=0)
regressor.fit(X,y)

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y_pred = regressor.predict(array([[6.5]]))

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y_pred

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#visualising the decision tree regression result(for higher resolution and smoother curves)
X_grid=np.arange(min(X),max(X),0.01)
X_grid.shape

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#reshaping X_grid from 1-D array to 2-D array
X_grid=X_grid.reshape(len(X_grid),1)

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plt.scatter(X,y,color='red')
plt.plot(X_grid,regressor.predict(X_grid),color='blue')
plt.title('Truth vs Bluff(Decision tree regression)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()