Decision_Tree_Regression.py

#Decision Tree Regression: 

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

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

# We do not split the dataset because we have a small dataset

#3 Fitting the Decision Tree Regression Model to the dataset
# Create the Decision Tree regressor object here
from sklearn.tree import DecisionTreeRegressor 
#DecisionTreeRegressor class has many parameters. Input only random_state=0 or 42.
regressor = DecisionTreeRegressor(random_state=0)
#Fit the regressor object to the dataset. 
regressor.fit(X,y)


#4 Predicting a new result
y_pred = regressor.predict(5.5)

#5 Visualising the Decision Tree Regression results
plt.scatter(X, y, color = 'red')
plt.plot(X, regressor.predict(X), color = 'blue')
plt.title('Check It (Regression Model)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()

#6 Visualising the Regression 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('Example of Decision Regression Model')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()