Decision Tree Algorithm in Machine Learning Explained with Example | Urdu Zero to AI Pro with Malik

Assalamualaikum and Welcome to Zero to AI Pro with Malik! 🌟 In this video, we’ll explore one of the most popular and powerful algorithms in Machine Learning — the Decision Tree Algorithm. 🌳 Decision Tree is a Supervised Learning Algorithm that works using a series of Yes/No (If–Else) questions about data features to make predictions. You’ll learn step-by-step how Decision Trees make decisions, how splitting works, what Gini Impurity and Entropy mean, and how to implement it in Python using Scikit-learn (sklearn). 📘 In This Tutorial You’ll Learn: ✅ What is a Decision Tree Algorithm ✅ How Decision Tree works in Machine Learning ✅ Components of a Decision Tree: Root Node, Internal Node, Leaf Node, and Branches ✅ How Decision Tree splits data using Gini Impurity and Entropy ✅ How to prevent overfitting using pruning and depth control ✅ Step-by-step implementation in Python using Scikit-learn:  🔹 Importing DecisionTreeClassifier  🔹 Using make_classification dataset  🔹 Training a model on Age & Income example (Loan Approval system)  🔹 Predicting results using the trained Decision Tree model ✅ Real-life examples like Loan Approval Prediction and Spam Detection ✅ Visualization of the Decision Tree flowchart ✅ Understanding how decision trees handle categorical and numerical data ⚡ Technical Topics Covered: Gini Impurity vs Entropy Information Gain Feature Splitting Criteria Overfitting and Pruning Depth Control (max_depth parameter) Classification Example using sklearn.tree.DecisionTreeClassifier How Decision Tree forms a flowchart of decisions 💡 Advantages of Decision Tree: ✔ Easy to Understand & Interpret ✔ Works well for both Classification & Regression ✔ Handles both Categorical and Numerical Data ✔ Visual representation for clear understanding Disadvantages: ⚠ Can overfit if not pruned properly ⚠ Sensitive to small changes in data ⚠ Sometimes biased towards features with more levels 🧩 Python Libraries Used: Scikit-learn (sklearn.tree) NumPy Pandas Matplotlib (optional for visualization) 🎯 Why You Should Watch This Video By the end of this video, you’ll be able to: ✅ Understand the working of Decision Trees ✅ Implement a Decision Tree model in Python ✅ Interpret the flow of decisions and predictions ✅ Build smarter ML models that can classify real-world data 📚 Keywords for YouTube SEO Decision Tree Algorithm, Decision Tree in Machine Learning, Decision Tree Python, Decision Tree Urdu, Machine Learning Urdu Tutorial, AI Course in Hindi, Zero to AI Pro with Malik, Gini Impurity, Entropy Information Gain, Scikit-learn Decision Tree, Loan Approval Example, Spam Detection Example, Data Science in Urdu, ML Algorithms Explained, Supervised Learning Urdu, Classification Algorithms Python, Tree Based Models, Machine Learning Basics. 📅 Watch Next in the Series: 👉 Logistic Regression Explained 👉 Feature Selection Methods 👉 Evaluation Metrics in ML 👉 Random Forest Algorithm 🔔 Subscribe to “Zero to AI Pro with Malik” for more tutorials on AI, Machine Learning, and Data Science in simple Urdu/Hindi — from beginner to advanced. 📈 New videos every week to help you go from Zero to AI Pro! #DecisionTree #MachineLearning #ZeroToAIProWithMalik #AI #DataScience #SupervisedLearning #PythonML #ScikitLearn #DecisionTreePython #UrduTutorial #HindiTutorial