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Exploratory data analysis (EDA) is a critical first step in any data science project. It involves getting to know your data by summarizing its main characteristics, identifying patterns and outliers, and exploring relationships between variables. EDA can help you to understand your data better and to identify potential problems with your data
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What is Exploratory Data Analysis ?
Exploratory data analysis is a statistical way of analyzing data sets to summarize their essential characteristics. Exploratory data analysis generally used data visualization and graphical representation techniques. While performing exploratory data analysis we can see what the data can tell us beyond the formal modeling and thereby contrast traditional hypothesis testing.
It helps find how best to manipulate data sources to get the answers you need, make it easier for data scientists to identify the patterns, spot anomalies, test a hypothesis, or check assumptions.
Exploratory data analysis is different from the initial data analysis, which is focused on checking the assumption required for model fitting and hypothesis testing, handling missing values and making transformation of variables as needed. EDA encompasses IDA
The objectives of EDA are to:
Allow for unexpected data findings.
Suggest to hypotheses about the causes of observed phenomena
Assess assumptions on which statistical inference will be based
Support the selection of appropriate statistical tools and techniques
Provide a basis for further data collection through surveys or experiments
Type of Exploratory Data analysis
Univariate non-graphical : This is the simplest form of data analysis, where the data being analyzed consists of just one variable.
Multivariate non graphical : It has more than one variable. In this EDA techniques generally show the relationship between two or more variables of the data through cross-tabulation or statistics.
Multivariate graphical: It uses graphics to display relationships between two or more sets of data.
Univariate graphical : It is a graphical method. Represent the data in graphics.
Technique and tools for Data analysis
There are lot of tools available for Data analysis
Box plot
Histogram
Heat map
Bar chart
Scatter plot
Dimensionality Reduction :
Principal component analysis (PCA)
Multilinear PCA
Dive into Exploratory Data Analysis (EDA) using Python: An Essential Step in Machine Learning
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EDA is an important part of any data science project. If you are struggling with an EDA assignment, there are a number of resources available to help you. I encourage you to check out the resources listed above.
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