Decision Tree Regression in Python – Step 4.) Predicting 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(),y)

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


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