Decision Tree

Decision tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.Decision trees can handle high dimensional data. In general decision tree classifier has good accuracy. Decision tree induction is a typical inductive approach to learn knowledge on classification.

Problem :

For the given ‘Iris’ dataset, create the Decision Tree classifier and visualize it graphically. The purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.

Dataset:

Data can be found at : https://drive.google.com/file/d/11Iq7YvbWZbt8VXjfm06brx66b10YiwK-/view
EDA : The dataset contains 150 rows and 6 columns the columns are SepalLenght,SepalWidth,PetalLenght,PetalWidth all the scales are in centemeter(cm) and the last row is Species which determine weather the flower is Setosa, Versicolor or Virginica. The data distribution among setosa, virginica, versicolor are equal so iris dataset is a Balanced dataset (as the number of data points for every class is 50).
Sepal length,Sepal width, Petal length, Petal width are called feature/Input-variable/Independent-variable Species are called Labels/Dependent-variable/Output-variable/class-label/Response label

Import necessary libraries

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

Visual Exploratory Data

From the pairplot, we can see that the Iris-setosa species is separataed from the other two across all feature combination

Preparing the data

Independent variable or input variable = Feature variable = x
Dependent variable or output variable = Target variable = y

Split the datset

Split the data into training and test datasets, we'll do this by using Scikit-Learn's built-in train_test_split() method The model learn the relationships from the training data and predict on the test data.i have split the data in 70:30 ratio

Traning the Algorithm

I have split the data into training and testing sets, and now i ll train the algorithm using DecisionTreeeClassifier method,then instaniate the Classifier and fit it on the traning set using the fit() method In this step the model learned the relationships between the training data ie,x_train and y_train.

Accuracy is 97%

Predict the data

Now the model is ready to make predictionson the test data using the predict() method

Comparing actual vs predicted

df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  
df.head()

    	Actual	           Predicted
Id		
115	 Iris-virginica	  Iris-virginica
63	  Iris-versicolor	 Iris-versicolor
34	  Iris-setosa	     Iris-setosa
108	 Iris-virginica	  Iris-virginica
8	   Iris-setosa     	Iris-setosa

Plot the Decision Tree

Plotting the tree obtained using sklearn tree module

Conclusion

We made a model that predicts the species from the iris dataset using Decision tree calssifier where the accuracy was found to be 97% overall.And made a comparission between the actual and the predicted values which was found to be same in all the cases.And finally visualized the Decision tree classifier.