Scatter plots, a fundamental visualization technique, showcase the relationship between two continuous variables. They help in understanding the underlying data distribution by showing measures of central tendency and spread. Histograms provide a graphical representation of the distribution of a single continuous variable by dividing it into bins and displaying the count of observations within each bin. ![]() ![]() ![]() In this article, we'll delve into various data visualization techniques that significantly aid in efficient exploratory data analysis. The insights obtained from EDA, including features extracted, are pivotal not only for further data analysis and modelling but also for enhancing machine learning applications.ĭata visualization is a cornerstone of EDA, enabling the representation of complex data in an easily understandable visual format. The primary objective of EDA is to summarize the data, uncover patterns, generate hypotheses, and test assumptions, setting the foundation for in-depth analytics.ĭata scientists leverage EDA to gain insights into datasets, ultimately influencing business strategies and outcomes. Heat map, which is a graphical representation of data where values are depicted by color.Exploratory Data Analysis (EDA) is a crucial step in the data analysis process, involving a thorough examination of data through statistical and visualization tools.Bubble chart, which is a data visualization that displays multiple circles (bubbles) in a two-dimensional plot.Run chart, which is a line graph of data plotted over time.Multivariate chart, which is a graphical representation of the relationships between factors and a response.Scatter plot, which is used to plot data points on a horizontal and a vertical axis to show how much one variable is affected by another.Other common types of multivariate graphics include: The most used graphic is a grouped bar plot or bar chart with each group representing one level of one of the variables and each bar within a group representing the levels of the other variable. Multivariate graphical: Multivariate data uses graphics to display relationships between two or more sets of data.Multivariate non-graphical EDA techniques generally show the relationship between two or more variables of the data through cross-tabulation or statistics. Multivariate nongraphical: Multivariate data arises from more than one variable.Box plots, which graphically depict the five-number summary of minimum, first quartile, median, third quartile, and maximum.Histograms, a bar plot in which each bar represents the frequency (count) or proportion (count/total count) of cases for a range of values.Stem-and-leaf plots, which show all data values and the shape of the distribution.Common types of univariate graphics include: Graphical methods are therefore required. Non-graphical methods don’t provide a full picture of the data. The main purpose of univariate analysis is to describe the data and find patterns that exist within it. Since it’s a single variable, it doesn’t deal with causes or relationships. This is simplest form of data analysis, where the data being analyzed consists of just one variable. Predictive models, such as linear regression, use statistics and data to predict outcomes.K-means Clustering is commonly used in market segmentation, pattern recognition, and image compression. The data points closest to a particular centroid will be clustered under the same category. the number of clusters, based on the distance from each group’s centroid. K-means Clustering is a clustering method in unsupervised learning where data points are assigned into K groups, i.e.Multivariate visualizations, for mapping and understanding interactions between different fields in the data. ![]()
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