Data Science in 30 Days | Data Science Full Course Free | #datascience #fullcourse - Feature Selection : Wrapper & Embedded Methods - RFE, Lasso & Random Forest | Day 17/30 Part-2 |

📘 Day 17 (Part 2) | Advanced Feature Selection: RFE, Lasso & Random Forest Welcome to Part 2 of Day 17 in the Data Science in 30 Days series by The Data Key 🚀 In this video, we go beyond basic correlation methods and dive into advanced feature selection techniques that are actually used in real-world machine learning projects. --------------------------------------------------------------------------------------------------------------------- If you’ve ever wondered: Which features truly matter? How do ML models decide feature importance? How to remove useless features without hurting accuracy? 👉 This video answers all of that step by step. 🧠 What You’ll Learn in This Video In Part 2, we focus on Wrapper & Embedded Methods, which use machine learning models themselves to select the best features. --------------------------------------------------------------------------------------------------------------------- 🔹 Topics Covered: • What are Wrapper Methods and how they work • Recursive Feature Elimination (RFE) explained intuitively • Why RFE is accurate but computationally expensive • What are Embedded Methods in feature selection • Lasso Regression (L1 Regularization) and how it removes useless features • Random Forest Feature Importance explained visually • Practical Python implementation using Scikit-Learn • How to decide which method to use in real projects ---------------------------------------------------------------------------------------------------------------------- 📚 Recommended Resources (Official & Trusted): 🔹 Feature Selection – Scikit-Learn https://scikit-learn.org/stable/modules/feature_selection.html 🔹 Recursive Feature Elimination (RFE) https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html 🔹 Lasso Regression (L1 Regularization) https://scikit-learn.org/stable/modules/linear_model.html#lasso 🔹 Random Forest Feature Importance https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html 🔹 Feature Selection Explained (Article) https://machinelearningmastery.com/feature-selection-machine-learning/ ---------------------------------------------------------------------------------------------------------------------- 🧪 Day 17 Assignment 📌 Take the dataset from Day 16 📌 Train a Random Forest model 📌 Plot and analyze the feature importance graph 📌 Share your insights in the comments 👇 ---------------------------------------------------------------------------------------------------------------------- 🚀 What’s Coming Next? 👉 Day 18: Building & Evaluating Your First Complete Machine Learning Models (Train–Test Split, Evaluation Metrics, and Model Comparison) 🔔 Subscribe & Stay Consistent This is part of the Data Science Full Course in 30 Days — designed to take you from beginner to job-ready. 👉 Subscribe to The Data Key 👉 Like 👍 | Comment 💬 | Share 📤 Keep learning. Keep building. 🚀 ------------------------------------------------------------------------------------------------------------------------ OUTLINE: 00:00:00 Why Filter Methods Aren't Always Enough 00:02:42 The Brute-Force Approach 00:03:38 A Deep Dive into RFE (Recursive Feature Elimination) 00:04:11 The Good and the Bad of Wrapper Methods 00:06:45 The Best of Both Worlds 00:07:41 The Feature Shrinker 00:08:20 Finding Importance in the Woods 00:09:04 Filter vs. Wrapper vs. Embedded 00:11:40 A Practical Assignment #featureselection #wrapper #embedding #datasciencecourse #datasciencein30days #machinelearningfullcourse #foryou #lasso #rfe #randomforest #mlmodels #datasciencebasics #datasciencetutorial #youtubevideo #newvideo #subscribe #thedatakey #ai #popularvideo #scikitlearn #aivideo #technology #tech #coding #dataanalytics #data #database #programming #python #machinelearningwithpython #machinelearning