Decision Tree Regression in Python – Step 5.) Visualize Results with Decision Tree Regression Model

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#Importing Libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#Importing data
dataset = pd.read_csv(‘Decision Tree Data.csv’)
x = dataset.iloc[:,1:2].values
y =dataset.iloc[:,2].values

#Split Training Set and Testing Set
from sklearn.cross_validation import train_test_split
xtrain, xtest, ytrain, ytest =train_test_split(x,y,test_size=0.2)

# Train Decision Tree Regression model
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor()
regressor.fit(x,y)

#Predict using Decision Tree Regression
y_pred = regressor.predict(6.5)

#Visual Decision Tree Regression Results
plt.scatter(x,y, color = ‘red’)
plt.plot(x, regressor.predict(x), color = ‘blue’)
plt.title(‘Truth or Bluff (Decision Tree Regression)’)
plt.xlabel(‘Position Level’)
plt.ylabel(‘Salary’)
plt.show

#Visual Decision Tree Regression Results for 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(‘Truth or Bluff (Decision Tree Regression)’)
plt.xlabel(‘Position Level’)
plt.ylabel(‘Salary’)
plt.show

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