Train Test Split with Python Machine Learning (Scikit-Learn)
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In this Python Machine Learning Tutorial, we take a look at how you can split a data set through train test split in scikit learn.
This is a great method for prepping your data before you run a model.
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In this video, I walk you through implementing train test split in Python using sklearn, one of the most essential techniques in machine learning. Train test split allows you to divide your dataset into training and testing portions, typically using an 80-20 split. This ensures your machine learning model can be evaluated on unseen data, which is crucial for validating model performance.
We start by importing the necessary libraries including pandas and sklearn's train_test_split function. I demonstrate using a real baseball dataset with 500 players, showing you how to load the data and prepare it for splitting. We cover how to separate features (X) from the target variable (y), and I explain why proper data preparation matters before running any machine learning algorithm.
I walk through the exact syntax for train_test_split, including key parameters like test_size and random_state. The random_state parameter is particularly important because it ensures reproducibility - you'll get the same split every time you run the code. I show you how to verify your split worked correctly by checking the shape of your training and testing sets, and I demonstrate using describe() to compare statistics between them.
By the end of this tutorial, you'll understand exactly how to implement train test split, why it's essential for machine learning projects, and how to validate that your data has been properly divided for model training and testing.
TIMESTAMPS
00:00 Introduction to Train Test Split
00:36 Setting Up Python Environment
01:04 Importing Train Test Split
01:07 Loading the Dataset
01:57 Understanding the Data
02:21 Creating X and Y Variables
03:19 Examining the Data Shape
03:42 Implementing Train Test Split
04:11 Understanding Random State
04:56 Setting Test Size
05:23 Verifying the Data Split
06:23 Exploring Training Data
06:52 Comparing Train vs Test Statistics
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