Implementing a decision tree with scikit-learn

Now, when we are sufficiently aware of the mathematics behind decision trees, let us implement a simple decision tree using the methods in scikit-learn. The dataset we will be using for this is a commonly available dataset called the iris dataset that has information about flower species and their petal and sepal dimensions. The purpose of this exercise will be to create a classifier that can classify a flower as belonging to a certain species based on the flower petal and sepal dimensions.

To do this, let's first import the dataset and have a look at it:

import pandas as pd
data=pd.read_csv('E:/Personal/Learning/Predictive Modeling Book/My Work/Chapter 7/iris.csv')
data.head()

The datasheet looks as follows:

Fig. 8.7: The first few observations of the iris dataset

Sepal-length, Sepal-width, Petal-length, and Petal-width are the dimensions of the flower while the Species denotes the class the flower belongs to. There are actually three classes of...