2 - Train-Test Split in Python
Learn how to use Python's train test split to prepare your data for machine learning. This step is essential for building reliable models and evaluating how well they perform on new, unseen data. In this tutorial, you will see both manual and automated ways to split your data using popular libraries like pandas, numpy, and scikit-learn.
Follow along as we demonstrate how to divide your dataset into training and testing sets, adjust split sizes, use random state for reproducibility, and apply stratification for balanced categories. By the end, you will know how to avoid common mistakes and use these techniques in your own projects.
00:00 Introduction to train test split
00:18 Why split your data
00:38 Importing numpy and pandas
01:12 Creating a sample dataset
01:58 What is a train test split
02:41 Manual splitting of data
03:19 Using scikit-learn for splitting
04:54 Understanding random state
05:49 Splitting features and labels
06:25 Splitting both features and labels
07:01 Practice with different split sizes
07:30 Extreme test size examples
08:27 Interactive split with user input
09:15 Why randomness matters in splitting
09:54 Stratified splitting for balanced labels
10:48 Simple prediction example
11:46 Checking model accuracy
12:16 Common mistakes to avoid
12:52 Creating a reusable split function
13:46 Challenge: build your own split
14:16 Lesson recap and next steps
14:54 Thank you and closing
#Python #MachineLearning #DataScience
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