Role of Exploratory Data Analysis in Data Science

Rao, Arjun; Vardhan, B. Vishnu; Shaik, Hafeezuddin

doi:10.1109/icces51350.2021.9488986

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…The next step is exploratory data analysis (EDA) [70][71][72]. This approach is useful for visualizing data and finding answers to a specific task.…”

Section: Exploratory Data Analysismentioning

confidence: 99%

Research of Machine Learning Algorithms for the Development of Intrusion Detection Systems in 5G Mobile Networks and Beyond

Imanbayev

Tynymbayev

Одарченко

et al. 2022

Sensors

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The introduction of fifth generation mobile networks is underway all over the world which makes many people think about the security of the network from any hacking. Over the past few years, researchers from around the world have raised this issue intensively as new technologies seek to integrate into many areas of business and human infrastructure. This paper proposes to implement an IDS (Intrusion Detection System) machine learning approach into the 5G core architecture to serve as part of the security architecture. This paper gives a brief overview of intrusion detection datasets and compares machine learning and deep learning algorithms for intrusion detection. The models are built on the basis of two network data CICIDS2017 and CSE-CIC-IDS-2018. After testing, the ML and DL models are compared to find the best fit with a high level of accuracy. Gradient Boost emerged as the top method when we compared the best results based on metrics, displaying 99.3% for a secure dataset and 96.4% for attacks on the test set.

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“…The next step is exploratory data analysis (EDA) [70][71][72]. This approach is useful for visualizing data and finding answers to a specific task.…”

Section: Exploratory Data Analysismentioning

confidence: 99%

Research of Machine Learning Algorithms for the Development of Intrusion Detection Systems in 5G Mobile Networks and Beyond

Imanbayev

Tynymbayev

Одарченко

et al. 2022

Sensors

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“…Data Analysis is the analysis of various data likes cleaning the data, transforming it into understandable form, and then modeling data to extract some useful information for business use or an organizational use [2]. The analyzed data are more insightful for identifying and improving extremely critical insights across the Organization therefore make an Data Analysis an important factor in business development [3].…”

Section: Introductionmentioning

confidence: 99%

Exploratory Data Analysis (EDA): A Study of Olympic Medallist

Sagala

Aryatama²

2022

SISTEMASI

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Olympic games are one of the most popular international sports events in the world where thousands of athletes participate in different types of sports categories. The winner has rewarded a medal (Bronze, Silver, Gold) according to the rank. An analysis can be carried out on the Olympic data to understand the changes in medalists over time. Furthermore, it helps to determine the progress of participating countries and strategies that can be used in the future. Exploratory Data Analysis (EDA) is a method for analyzing and summarizing the properties of data, either in graphical or non-graphical, to get insights from the dataset being studied. The approaches can be classified as univariate, bivariate, or multivariate. EDA is widely used in various domains including sport. The main purpose of this study is to analyze the changes of Olympic Medalist data throughout the provided years in the form of univariate, bivariate, and multivariate analysis. This analysis provides detailed, statistical, and interesting information about the changes in medal winners from time to time.

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