Which Python library is commonly used for machine learning and predictive modeling?

Matplotlib
Pandas
Numpy
Scikit-learn

The correct answer is D. Scikit-learn.

Scikit-learn is a free and open-source machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and Pandas.

Matplotlib is a plotting library for the Python programming language and its numerical and scientific libraries NumPy and SciPy. It provides an object-oriented API for embedding plots into applications and the ability to save figures as various image formats.

Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language.

NumPy is a Python package that provides fast, efficient numerical computation. NumPy arrays are objects that can store numerical data. They are very fast to access and manipulate, and they can be used to perform a wide variety of mathematical operations.

In conclusion, Scikit-learn is the most commonly used Python library for machine learning and predictive modeling. It is a free and open-source library that features various classification, regression and clustering algorithms. It is designed to interoperate with the Python numerical and scientific libraries NumPy and Pandas.

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