Role of Exploratory Analytics and Visualization in Heart Disease Prediction
How to Cite?
Lijetha.C. Jaffrin, Dr.J. Visumathi, Dr.G.Umarani Srikanth, "Role of Exploratory Analytics and Visualization in Heart Disease Prediction," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 22-28, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I1P203
Exploratory Data Analysis, Visualization, Spyder IDE, python, Seaborn.
Data Analysis is carried out to discover useful knowledge from the dataset and to drive quick and better decisions. It is also used to increase the efficiency of the work. Exploratory Data analysis is the first phase in Data Analysis. It is a method to understand the data and summarize the main features in the dataset by analyzing the data. It is also used for the visual representation of data. Visualization includes line plot, subplot, pair plot, violin plot, joint plot, swarm plot, Histograms, Box plot, Scatter plot. In this paper, Exploratory Data Analysis is done using python and implemented in Spyder IDE. Univariate analysis, bivariate analysis, multivariate analysis, and dimensionality reduction have been done on variables in the heart dataset. Different types of graphs have been plotted using the python Seaborn library to analyze the heart dataset. The primary objective is to get a more explained sight for which sort of traits might be a more critical sign of approaching heart disease.
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