Data Science in 30 Days | Data Science Full Course Free | #datascience #fullcourse - Train-Test Split & Model Training Explained | Day 18/30 (Part 1) - Data Science in 30 Days| #course

Welcome to Day 18 (Part 1) of the "Data Science in 30 Days" series by The Data Key! In the previous videos, we mastered Data Cleaning, Feature Engineering, and Feature Selection. Now, we finally move to the core of Machine Learning: Model Building. In this video (Part 1), we cover the most critical first step of any ML project: The Train-Test Split. You will learn why "testing on the data you trained on" is a major mistake and how to avoid it. We will also write our first lines of code to initialize and train a model using Scikit-Learn. 📘 What You Will Learn in This Video: 🔹The Golden Rule: Why we never evaluate a model on training data. 🔹Train-Test Split: How to divide your dataset (80/20 rule) using sklearn.model_selection. 🔹Overfitting vs. Underfitting: A simple explanation of these key concepts. 🔹Model Selection: How to choose between Regression and Classification models. 🔹The .fit() Method: How to actually "train" your algorithm on the data. 🚀 Next Up (Part 2): Once the model is trained, how do we know if it's good? In Part 2, we will cover Model Evaluation Metrics (Confusion Matrix, Accuracy, Precision, Recall, F1-Score). ---------------------------------------------------------------------------------------------------- 🔗 Important Resources & Links: 1. Official Scikit-Learn Documentation (The Bible of Python ML) : 🔹Train-Test Split: The official guide on how the function works and what parameters like random_state actually do. 👉 https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html 🔹Choosing the Right Model: An interactive "Cheat Sheet" flow chart by Scikit-Learn to help you decide which algorithm to use. 👉 https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html 2. Deep Dive Tutorials : 🔹Understanding Train-Test Split: A beginner-friendly guide that explains why we split data and the consequences of not doing it. 👉 https://builtin.com/data-science/train-test-split 🔹Overfitting vs. Underfitting: A great conceptual article by IBM explaining the "Goldilocks" problem of Machine Learning (Too hot, too cold, or just right). 👉 https://www.ibm.com/topics/overfitting 3. Visual Guides : 🔹Bias-Variance Tradeoff: If you want to understand the math behind why models fail (High Bias vs. High Variance), this is the next step. 👉 https://towardsdatascience.com/understanding-the-bias-variance-tradeoff-165e6942b229 -------------------------------------------------------------------------------------------------------------------------------------------- OUTLINE: 00:00:00 From Raw Data to Ready Data 00:02:56 Why We Don't Cheat in Data Science 00:03:33 Understanding Overfitting 00:06:12 Dividing Our Data for Honest Results 00:09:08 Regression or Classification? 00:09:58 Hello, Logistic Regression! 00:10:51 Creating an Instance 00:11:30 Teaching Our Model with .fit() 00:12:15 What's Next? Preparing for the Final Exam Don't forget to Like, Comment, and Subscribe to stay updated with the course! #datasciencecourse #datascience #datascienceforbeginners #datasciencetutorial #datasciencebasics #model #modeltrains #trainmodels #modelevaluation #machinelearning #machinelearningfullcourse #machinelearningwithpython #programming #thedatakey #technology #pythontutorial #modeling #aivideo #aiagents #datacleaning #featureselection #featureengineering #foryou #newvideo #trending #logisticregression #generativeai