Decision Tree Demo

Start with sample datasets or upload your own CSV/Excel files.

📁 Upload Your Data

🗂️ Sample Datasets

🎯 Target Column

🔄 Loading sample data...

📋 Data Preview (First 5 Rows)

🌳 Decision Tree Parameters

Max Depth

Set to 0 for unlimited depth

Min Samples Split

Minimum samples required to split an internal node

Min Samples Leaf

Minimum samples required to be at a leaf node

🎯 Criterion

Objective to measure split quality (auto-switched for regression)

Decision Tree (Interactive)

🎯 Prediction Result

Run prediction to see the result.

📝 Prediction Details

Detailed prediction information will appear here.

📋 Algorithm Summary

Algorithm details will appear here after prediction.

💡 Tips:

Tree visualization shows the complete decision tree structure with decision paths.
Feature importance shows which features matter most.
Try different max depth and criterion to see structure changes!
Min samples split/leaf control complexity and reduce overfitting.