EDA is important because it helps you gain a better understanding of the data you are working with, identify patterns and insights, detect errors and outliers, and communicate your results effectively Exploratory Data Analysis (EDA) is an essential step in any data science project. It helps in understanding the data, identifying patterns, trends, relationships, and anomalies in the data. Here are some reasons why EDA is important:
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Identify data quality issues: EDA helps to identify missing values, outliers, and inconsistencies in the data.
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Understand the data: EDA helps to understand the distribution, shape, and range of variables. It also helps to understand the relationship between variables.
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Feature engineering: EDA can help to identify new variables that may be useful in the analysis.
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Model selection: EDA helps in the selection of the appropriate model for the data. It can also help to identify the most important variables for the model.
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Communicate results: EDA can help to communicate the results of the analysis to stakeholders in an understandable and actionable way.
Overall, EDA helps in making data-driven decisions and is a critical step in any data science project.