This project demonstrates how to use a Decision Tree Classifier from the scikit-learn library to classify the Iris flower dataset — one of the most famous datasets in machine learning.
###What the Code Does:### 1.Loads the dataset from scikit-learn.
2.Splits it into training and testing sets (80/20).
3.Trains a Decision Tree Classifier on the training data.
4.Predicts flower classes for the test data.
5.Evaluates the model's accuracy using accuracy_score.
6.Displays the predicted and actual labels, along with accuracy.
###How the model works:### Is petal length < 2.5? ├── Yes → Predict: Setosa └── No ├── Is petal width < 1.8? │ ├── Yes → Predict: Versicolor │ └── No → Predict: Virginica