train_test_split sklearn in python

To split your data into training and testing sets using scikit-learn's train_test_split function, you can follow these steps:

1. Import the necessary module:

main.py
from sklearn.model_selection import train_test_split
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2. Split your data into input variables (X) and target variable (y). X should contain all the input features, and y should contain the target variable you want to predict.

main.py
X = your_data[['feature1', 'feature2', ...]]
y = your_data['target']
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3. Use the train_test_split function to split your data into training and testing sets. Specify the test_size parameter to determine the ratio of the test set. For example, setting test_size=0.2 will split the data into an 80% training set and a 20% testing set.

main.py
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
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4. You can also specify a random_state parameter if you want to reproduce the same split for future runs.

main.py
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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Make sure to replace 'feature1', 'feature2', and 'target' with the actual column names from your dataset. X_train and y_train will contain the training data, while X_test and y_test will contain the testing data.

Remember to import the necessary modules and preprocess your data before using the train_test_split function.