Swayanshu Shanti Pragnya
Bachelor of Engineering, specializing in Computer Science LOGIC DESIGN, PROGRAMMING TECHNIQUES, SOFTWARE ENGINEERING, PROGRAMMING LANGUAGES, OPERATING SYSTEMS, NUMERICAL ANALYSIS, DISCRETE MATHEMATICS, PROBABILITY AND STATISTICS, MATHEMATICAL SOFTWARE, ARTIFICIAL INTELLIGENCE, IMAGE PROCESSING AND COMPUTER VISION, PATTERN RECOGNITION, DOCUMENT AND TEXT PROCESSING, ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY, MATHEMATICAL LOGIC AND FORMAL LANGUAGES Computer Science LOGIC DESIGN PROGRAMMING TECHNIQUES SOFTWARE ENGINEERING PROGRAMMING LANGUAGES OPERATING SYSTEMS NUMERICAL ANALYSIS DISCRETE MATHEMATICS PROBABILITY AND STATISTICS Data Mining and Machine Learning Applications Multinomial logistic regression Random forest Logistic regression Machine learning Advanced Scientific and Engineering Studies Artificial Intelligence in Healthcare Theoretical computer science Computational Science and Engineering Health Information Management Information Systems Management Science and Operations Research Statistics and Probability

Bio

Swayanshu Shanti Pragnya is a data scientist and researcher affiliated with the University of Colorado Denver and Centurion University of Technology and Management. Holding a Bachelor of Engineering in Computer Science and qualifications as a FCSRC and Data Scientist, Swayanshu has contributed to the field of computing through a publication titled "Accuracy Analysis of Continuance by using Classification and Regression Algorithms in Python." With a keen interest in artificial intelligence, image processing, and software engineering, Swayanshu also serves as a reviewer and fellow member for the Global Journal of Computer Science and Technology, actively participating in the academic community.

Educational Journey

Centurion University of Technology and Management

Data Scientist • Bachelor of Engineering, specializing in Computer Science

University of Colorado, Denver

FCSRC and Data scientist

Experience

0 - 0

Research

Accuracy Analysis of Continuance by using Classification and Regression Algorithms in Python

Article May 25, 2018

- Reinforcement rate of technics and appositeness towards the convenience of the human being is a perennial mechanism. Mathematics has always been in the root towards the implementation of any algorithm or analysis regarding statistics or language. Extracting more about the data and analyzing them to solve a particular problem is the reason behind any analysis. Scrutiny itself has the different number of outcome which can be predictive or descriptive. Now prediction is how far accurate is tested by using various techniques. The enhancement in problem-solving capability leads to come up with a new aptitude concerning machine learning algorithms. But before prediction of data set collection, exploration, feature extraction, model building, accuracy testing are primarily required to invent. So for explaining all these processes, concept learning is essential. In this paper different algorithms like SVM, Linear and Logistic Regression, Decision tree, and Random forest algorithms will be used to demonstrate the accuracy in titanic data from Kaggle Website with all the required steps by using Python language.