How to split dataset into training, validation, and tasting sets in Python

To split a dataset into training, validation, and testing sets in Python, you can use the train_test_split() function from the Scikit-learn library.
In this video, you will learn everything, including tricks to split a dataset into training, validation, and testing sets using Python. Splitting a dataset into these sets is a crucial step in machine learning, as it helps to evaluate the performance of a model.
We will be using the Scikit-learn library in Python to perform this task. The train_test_split() function will be used to split the dataset into three sets.
First, we will import the necessary libraries and load the dataset. Next, we will split the dataset into two sets: the training-validation set and the test set. The test set will be set to 20% of the original dataset.
Then, we will further split the training-validation set into a training set and a validation set. The validation set will be set to 25% of the training-validation set (or 20% of the original dataset). This will allow us to fine-tune the hyperparameters of the model.
Finally, we will have three sets: the training set, the validation set, and the testing set. We will use the training set to train the model, the validation set to tune the hyperparameters, and the testing set to evaluate the final performance of the model.
The code will be demonstrated step-by-step, and you will be able to follow along easily. By the end of the video, you will clearly understand how to split a dataset into training, validation, and testing sets using Python.

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