Mathematics and Computer Science

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.