About EDA

This is an article about EDA.

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Today we will learn about EDA.

Exploratory Data Analysis (EDA) is a process for exploring and understanding data, primarily using visualization and statistical tools to identify data characteristics and develop hypotheses.

Below is information about EDA.

Understand the structure of data

The first step in EDA is to determine the structure of the data.

Understand the characteristics of the data by checking the variables, format, distribution, missing values, and outliers of the data.

Data exploration through visualization

Visualizations are very useful in understanding the characteristics of data.

Use various visualization techniques such as histograms, box plots, scatter plots, and heat maps to visually understand the distribution, correlation, and outliers of data.

Basic statistical analysis

Understand the characteristics of data through basic statistical analysis such as central tendency, variance, correlation, and frequency of data.

Hypothesis formulation

Through EDA, we identify data characteristics and develop hypotheses.

This provides directions for further analysis of the data.

Identify patterns and trends

Through EDA, patterns, trends, and clustering of data are identified to understand the inherent characteristics of the data and extract meaningful information.

Visualization and interpretation of results

Visualize and interpret EDA results to improve your understanding of the data and derive insights that can be used for further analysis or decision-making.

Conclusion

EDA is the initial stage of data analysis and plays an important role in determining the direction of data analysis by performing tasks such as identifying data characteristics and establishing hypotheses.

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