Data Science in 30 Days | Data Science Full Course Free | #datascience #fullcourse - Model Selection & Evaluation | Day 13/30 of Data Science in 30 Days | Data Science Course | #ml #ai

Welcome to Day 13 of our Data Science in 30 Days course! In today’s session, we’ll dive deep into one of the most critical stages of any machine learning project — Model Selection & Evaluation. Choosing the right model and evaluating its performance correctly can make or break your data science project. In this video, we’ll walk step-by-step through key concepts, techniques, and practical implementations using Python and Scikit-learn. 🧠 What You’ll Learn in This Video: ✅ Understanding the importance of model selection ✅ Different types of evaluation metrics (Accuracy, Precision, Recall, F1-score, AUC-ROC, etc.) ✅ Cross-validation and why it matters ✅ Bias-Variance tradeoff explained ✅ Comparing multiple models (Logistic Regression, Random Forest, SVM, KNN, etc.) ✅ Model tuning and selection using GridSearchCV ✅ Practical coding demo with the Iris dataset 💻 Code & Resources: 🔗 Scikit-learn Documentation: https://scikit-learn.org/stable/ 🔗 Dataset (Iris): https://archive.ics.uci.edu/ml/datasets/iris 🔗 Cross-validation guide: https://scikit-learn.org/stable/modules/cross_validation.html 🔗 GridSearchCV Tutorial: https://scikit-learn.org/stable/modules/grid_search.html 🧩 Watch the Complete Playlist: https://youtube.com/playlist?list=PL07H6FsxVmmwoYzMdi2TEcb2Q6hQ0g-EV&si=qiFqk106GBJJ4Dzz 🧠 Perfect For: Beginner to Intermediate Data Science & Machine Learning learners who want to master real-world techniques and build strong model evaluation intuition. 🎬 Stay Connected: 💡 Subscribe to The Data Key for daily data science lessons, projects, and tutorials! 📢 Don’t forget to like, share, and comment if you found this video useful. ------------------------------------------------------------------------------------------------------------------------ OUTLINE: 00:00:00 : Concept to Code – Gentle Introduction 00:02:47 : First Look – Explore Data, Visualize Patterns 00:05:48 : Data Preparation + Quartet of Models 00:09:03 : Meet the Models – Core Intuitions 00:10:58 : Train, Evaluate, and Compare 00:12:39 : The Verdict – Metrics and Boundaries 00:14:19 : Boundaries, Interpretation, and Final Choice -------------------------------------------------------------------------------------------------------------------------- #coding #modelevaluation #ml #mlmodels #foryou #technology #education #modelselection #scikitlearn #scikit #python #pythontutorial #newvideo #subscribe #chatgpt #supervisedlearning #crossvalidation #randomforest #machinelearningfullcourse #mlcourse #datasciencetutorial #datascience #dataanalytics #machinelearning #bigdata #deeplearning #artificialintelligence #ai #datavisualization #thedatakey #datasciencewiththedatakey #learnwiththedatakey