How to create TRAIN and TEST dataset using sklearn and Python

When working with machine learning, it’s important to split the data into a training and testing set to evaluate the performance of your model. This is known as a train-test split, and it’s a common practice in machine learning.

In Python, the library scikit-learn provides an easy way to perform this split using the train_test_split function.

The first step is to import the library and load the dataset into a pandas dataframe. Then, the data is split into two sets: a training set and a testing set. The train_test_split function takes the dataset and splits it into the desired ratio, typically 80% for training and 20% for testing.

You can also pass the variable ‘random_state’ which make the split deterministic .

The function returns the dataframes for training and testing set, which can be used to train the model and evaluate its performance.

It’s worth noting that the ratio of data split may vary depending on the dataset size, you should use the split ratio that best suits your dataset.

In summary, train-test split is a crucial step in machine learning to evaluate the performance of your model. Using the train_test_split function in scikit-learn, it’s easy to split your data into a training and testing set in Python, making it a powerful tool for data scientists and machine learning practitioners.

 

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