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Logistic Regression is widely used for binary and multiclass classification tasks.
The train-test split divides data into training and testing sets to evaluate how well a model generalizes.
Accuracy Score measures the proportion of correct predictions in a classification task.
Overfitting occurs when a model learns the noise and details of the training data, reducing its ability to generalize.
zip() aggregates elements from multiple iterables into tuples, useful for parallel iteration.
The upper() method converts all characters in the string to uppercase, so ‘Python’ becomes ‘PYTHON’.
NumPy provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions.
The // operator performs floor division, returning the largest integer less than or equal to the division result, so 5 // 2 is 2.
The join() method combines all items in a list into a single string, using a specified delimiter.
The in operator checks if 1 is present in the list x, returning True since 1 is an element.