COMPANY:CODTECH IT SOLUTIONS
NAME: KURUVA SHASHI KIRAN
INTERN ID:CT06DM211
DOMAIN: MACHINE LEARNING
DURATION:6 WEEKS
MENTOR:NEELA SANTHOSH KUMAR
This Python file implements a Decision Tree Classifier for the famous Iris dataset, which appears to be part of a CODTECH internship task. Here's a comprehensive breakdown of what the code does: Purpose and Dataset The script creates a machine learning model to classify iris flowers into three species (setosa, versicolor, virginica) based on four flower measurements: sepal length, sepal width, petal length, and petal width. Key Components Data Preparation:
Loads the built-in Iris dataset from scikit-learn Converts data to pandas DataFrame for easier manipulation Checks for missing values (none found in Iris dataset) Splits data into training (70%) and testing (30%) sets with stratification
Model Configuration:
Uses DecisionTreeClassifier with entropy criterion for splitting Sets maximum depth to 4 to prevent overfitting Applies random state for reproducible results
Model Training and Evaluation:
Trains the decision tree on the training data Makes predictions on the test set Calculates accuracy score Generates detailed classification report with precision, recall, and F1-scores Creates a confusion matrix visualization using seaborn heatmap
Visualization Features:
Decision Tree Visualization: Shows the complete tree structure with splitting conditions, feature names, and class predictions Confusion Matrix Heatmap: Visual representation of prediction accuracy across classes Feature Importance Plot: Bar chart showing which features are most important for classification
Analysis Output:
Feature importance rankings to understand which measurements are most decisive Comprehensive performance metrics for model evaluation
Technical Highlights The code is well-structured with clear step-by-step comments, uses best practices like stratified splitting to maintain class distribution, and provides multiple visualization methods to understand both the model's decision-making process and its performance. The entropy criterion and depth limitation help create an interpretable model that balances accuracy with simplicity.