Machine Learning Decision Trees | Healthcare Prediction with Python

Decision trees work like flowcharts - breaking complex decisions into simple yes/no questions. In this tutorial, you'll learn how to build an interpretable diabetes prediction model that doctors can actually understand and trust. This video, part of the *Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate on Coursera,* digs into interpretable machine learning with real healthcare data. You'll discover: *Why decision trees are perfect for medical AI (interpretability matters!) *Complete healthcare ML workflow with diabetes prediction dataset *Feature selection and data preprocessing for medical data *Decision tree training with hyperparameter tuning (max_depth=5) *Model visualization: Understanding the decision-making process *Tree pruning to prevent overfitting and improve generalization *Identifying key medical thresholds automatically Part 1 of complete decision tree series (evaluation coming next!) 📌 Enroll in the complete *Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate* → https://bit.ly/3Khv1zd ➡️ Watch Part 2: Model Evaluation & Overfitting: https://youtu.be/d3Cws0NULok ✅ Subscribe for more tutorials → https://www.youtube.com/@coursera 💬 Comment: What medical decisions would benefit most from decision tree transparency? 🩺 00:00 Introduction – Decision trees as medical flowcharts 00:26 Library Setup – Pandas, NumPy, Sklearn, Matplotlib imports 00:42 Dataset Loading – Diabetes prediction data from Kaggle 00:57 Data Exploration – Features like age, BMI, glucose levels 01:16 Dataset Analysis – Structure, data types, and statistical summary 01:32 Data Preparation – Feature selection and target variable separation 02:00 Train/Test Split – 70-30 split with reproducibility 02:27 Model Building – Decision tree classifier initialization 02:42 Hyperparameter Tuning – Max depth optimization for complexity control 03:04 Model Visualization – Interactive decision tree interpretation 03:27 Simplified