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Scikit-Learn

Exploring Scikit-Learn: A Data Science Powerhouse

Understanding Scikit-Learn

Scikit-Learn, also known as sklearn, is a prominent open-source machine learning library for the Python programming language. It is built on NumPy, SciPy, and matplotlib, and is designed to interoperate with the Python numerical and scientific libraries. Scikit-Learn provides simple and efficient tools for machine learning, statistical modeling, and data analysis. It features various classification, regression, and clustering algorithms including support vector machines, random forests, gradient boosting, and k-means.

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Application of Scikit-Learn

Scikit-Learn has been extensively used across diverse domains such as finance, healthcare, e-commerce, and more. One notable application is its utilization in healthcare to predict patient outcomes based on medical history and demographic data. In finance, Scikit-Learn has been employed for fraud detection and credit scoring. Furthermore, the e-commerce sector has leveraged Scikit-Learn for customer segmentation and prediction of purchasing patterns.​

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References
  1. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825-2830.

  2. Raschka, S., Mirjalili, V. (2019). Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2. Packt Publishing Ltd.

  3. Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, Inc.

  4. VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. O'Reilly Media, Inc.

  5. Brownlee, J. (2020). Machine Learning Mastery With Python: Understand Your Data, Create Accurate Models, and Work Projects End-to-End. Machine Learning Mastery.

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