Machine learning in business finance using Python / Kian Guan Lim

By:

Lim, Kian Guan

Material type:

TextSeries: World Scientific series on financial data analytics ; V.2Publication details: New Jersey : World Scientific, 2026Description: xxiii, 290 pISBN:

9789819811236

Subject(s):

DDC classification:

006.31 LIM-M

Summary: This book is an introduction to machine learning using Python programming language with applications in finance and business. Coverages include the prediction methods of logistic regression, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Random Forest, Gradient Boosting, and various types of Neural Networks. Performance measurements and assessments of feature importance are also explained. The book also contains detailed examples of the applications with data. Python codes are explained in a step-by-step manner using Jupyter Notebook so that the readers can practise on their own

Item type:

Books

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Current library	Call number	Status	Barcode
UMT Main Campus	006.31 LIM-M (Browse shelf(Opens below))	Available	153680

Browsing UMT Main Campus shelves Close shelf browser (Hides shelf browser)

Previous								Next
Previous	006.31 LAU-S Scable Fuzzy algorithms for data management and analysis	006.31 LEV- Leveraging artificial intelligence (AI) competencies for next-generation cybersecurity solutions /	006.31 LEV-J Just enough data science and machine learning : essential tools and techniques /	006.31 LIM-M Machine learning in business finance using Python /	006.31 LIU-P Python machine learning by example : unlock machine learning best practices with real-world use cases /	006.31 MAC- Machine learning paradigms :	006.31 MAC- machine learning and data mining in aerospace technology /	Next

Includes bibliographical references and index.

This book is an introduction to machine learning using Python programming language with applications in finance and business. Coverages include the prediction methods of logistic regression, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Random Forest, Gradient Boosting, and various types of Neural Networks. Performance measurements and assessments of feature importance are also explained. The book also contains detailed examples of the applications with data. Python codes are explained in a step-by-step manner using Jupyter Notebook so that the readers can practise on their own

Eng

There are no comments on this title.

to post a comment.