Decision Tree Implementation on Iris Dataset
├── Introduction
│   └── Overview of Decision Tree Algorithm
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Loading the Dataset
├── Implementing the Decision Tree
│   ├── Data Preparation
│   ├── Model Training
│   └── Model Evaluation
├── Visualization
│   └── Decision Tree Visualization
└── Conclusion
    └── Insights and Observations

1. Introduction

Overview of Decision Tree Algorithm

• The Decision Tree is a versatile machine learning algorithm used for both classification and regression. It's particularly known for its interpretability and simplicity.

2. Setting Up the Environment

Importing Libraries

import numpy as np
from sklearn.tree import Decision TreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
from sklearn import tree

Loading the Dataset

iris = load_iris()
X = iris.data
y = iris.targe

3. Implementing the Decision Tree

Data Preparation

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

Model Training

model = DecisionTreeClassifier()
model.fit(X_train, y_train)

Model Evaluation

y_pred = model.predict(X_test)
conf_matrix = confusion_matrix(y_test, y_pred)
class_report = classification_report(y_test, y_pred)

4. Visualization

Decision Tree Visualization