Название: Mathematics and Computer Science, Volume 1Автор: Sharmistha Ghosh, M. Niranjanamurthy, Krishanu Deyasi
Издательство: Wiley-Scrivener
Год: 2023
Страниц: 557
Язык: английский
Формат: pdf (true), epub
Размер: 107.0 MB, 33.0 MB
This first volume in a new multi-volume set gives readers the basic concepts and applications for diverse ideas and innovations in the field of computing together with its growing interactions with mathematics.
This new edited volume from Wiley-Scrivener is the first of its kind to present scientific and technological innovations by leading academicians, eminent researchers, and experts around the world in the areas of mathematical sciences and computing. The chapters focus on recent advances in Computer Science, and mathematics, and where the two intersect to create value for end users through practical applications of the theory.
The chapters herein cover scientific advancements across a diversified spectrum that includes differential as well as integral equations with applications, computational fluid dynamics, nanofluids, network theory and optimization, control theory, Machine Learning and Artificial Intelligence, Big Data analytics, Internet of Things, cryptography, fuzzy automata, statistics, and many more. Readers of this book will get access to diverse ideas and innovations in the field of computing together with its growing interactions in various fields of mathematics. Whether for the engineer, scientist, student, academic, or other industry professional, this is a must-have for any library.
Scikit-learn, a tool for developing Machine Learning algorithms, is a standard library of Python. Through Scikit-learn, a trained model for predictive analysis can be developed. Such models aim to provide accurate predictions. Stock predictions are based on changes and patterns identified in the historical dataset. Following the trends and patterns of the historical changes of stocks, Machine Learning algorithms can be developed for achieving accurate outcomes. An effective model is developed, which enhance the working pattern or performance of the machine that further helps to draw a precise analysis of stocks.
Python provides a rich data structure library called Pandas, which provides fast and efficient data transformation and analysis. The word Pandas is an abbreviation of Python Data Analysis Library. Pandas facilitate optimized and dynamic data structure designs work with “relational” or “labeled” data. Python’s approach is meant to provide a high-level, high-performance building block that can be used to do real-world analysis of data. Pandas Library is allowing users to import data from different file formats, such as CSV, SQL, Microsoft Excel etc. It helps in data preparation, as well as in data modeling, for those projects, which aims data analysis for the extraction of information. Python’s future will be built on this layer for statistical computing. In addition to discussing future areas of work and growth opportunities for statistics and data analytics applications built on Python, the study provides details about the language’s design and features. In this research paper, we intend to solve the problem of missing values in a dataset using the DROPNA function in Python using Pandas library.
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