2 Train-Test Split in Python: Step-by-Step Guide for Machine Learning Beginners скачать в хорошем качестве

2 Train-Test Split in Python: Step-by-Step Guide for Machine Learning Beginners

Learn how to split data for machine learning in Python using clear, step-by-step examples. This beginner-friendly guide covers the essentials of dividing your data into training and testing sets, using both simple lists and real-world datasets like the Titanic. You will understand why splitting is important, how to use train_test_split, and how to ensure your splits are balanced and reproducible. Follow along as we demonstrate practical tips for handling imbalanced targets, shuffling data, and cleaning datasets before splitting. By the end, you will be confident in preparing your own data for machine learning projects and ready to apply these techniques to any dataset. 00:00 Introduction 00:11 Silencing Warnings in Python 00:50 Why Split Data 01:10 Importing Libraries 01:41 Simple List Splitting Example 02:21 Creating and Printing a Sample List 02:40 Splitting a List with train_test_split 03:08 Splitting Real-World Data: Titanic Dataset 03:56 Exploring the Target Variable 04:19 Separating Features and Labels 04:50 Splitting Titanic Data into Train and Test Sets 05:41 Checking Label Balance 06:16 Interactive Test Size Selection 06:50 Practice: Changing Random State 07:05 Challenge: Splitting Without Random State 07:36 Ensuring Reproducible Splits 08:04 Stratified Splitting for Imbalanced Data 08:57 Shuffling Data Before Splitting 09:21 Mini Project: Titanic Survival Prediction 09:48 Cleaning Data by Removing Missing Values 10:12 Splitting Cleaned Data and Checking Balance 11:18 Challenge: Try with Another Dataset 11:33 Recap and Next Steps 11:56 Conclusion and Practice Prompt #Python #MachineLearning #DataScience