International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Analysis Study Research Paper | Computer Science and Information Technology | India | Volume 12 Issue 6, June 2023


Exploring the Paradigm Shift: Harnessing Data Analytics for Real - World Applications

D. Diana Julie M. E | Abinesh Mathivanan [2]


Abstract: Data handling and extraction are essential steps in any data science project. In Python, there are a variety of libraries that can be used to perform these tasks. This paper reviews the most popular libraries for handling and extracting datasets, including Pandas, NumPy, SciPy, Matplotlib, and scikit - learn. Pandas is a powerful library for data analysis and manipulation. It provides high - level data structures and tools for working with structured and time series data. NumPy is a library for scientific computing. It provides high - performance multidimensional arrays and a wide range of mathematical functions. SciPy is a collection of scientific computing tools that build on top of NumPy. It provides functions for linear algebra, numerical integration, and signal processing. Matplotlib is a library for creating visualizations. It provides a wide range of plotting functions for 2D and 3D data. The paper discusses the implementation of these libraries into a real world application and provides examples of how they can be used to handle and extract datasets. The paper also discusses how these libraries can be used to run simulation models of the data using ML Models. The paper concludes by discussing the future of data handling and extraction in Python, and how the development of new libraries is likely to continue to improve the tools available to data scientists.


Keywords: Python, data handling, data extraction, Pandas, NumPy, SciPy, Matplotlib, scikit - learn, machine learning, simulation models


Edition: Volume 12 Issue 6, June 2023,


Pages: 1467 - 1480


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