John Kyalo

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Exploratory Data Analysis using Data Visualization Techniques

Exploratory Data Analysis involves initial investigation and examination of datasets to summarize the main characteristics often with the help of graphical representations.
Basically, EDA helps you get a feel of the data you are working with. This is by identifying the structure and patterns involved. EDA helps generate hypotheses about relationships and trends in data which guide further analysis.
EDA with data visualization involves creating various plots and charts, such as histograms, box plots, scatter plots, bar charts and heatmaps, to visualize the distribution and relationship within the data. With this, you are able to uncover styles, pick out relationships, and gain insights. In case of anomalies, you also get to identify them.
Mostly Matplotlib and Seaborn are Python libraries used for visualization. There exist various sorts of EDA strategies hired depending on the nature of records and desires of evaluation. This includes:

1. Univariate analysis-makes a specialty of analyzing character variables inside the records set. It involves visualizing an unmarried variable at a time to understand its distribution. Examples include: Histogram -displays distribution of a single numerical variable. Useful for understanding data's central tendency.
2. Bivariate analysis- from the name bi which means two, you explore two variables by finding their correlation, association and dependencies. Examples include: Scatter plot - which explores relationship between two variables.
3. Multivariate analysis- extends bivariate evaluation to encompass greater variables. It ambitions to apprehend the complex interactions and dependencies among the many variables in a record set.

More different plots which are also considered as techniques include:

• Box plots (Box and Whisker) which provides a summary of visual distribution of data. Good for identifying outliers.

• Line plots used for time-series data. They show how a variable change over time identifying trends.

• Bar charts mainly used for comparison of categorical data.

• Heatmaps which visualize correlation matrix of numerical variables. They use color intensity to represent the strength of correlations.

• Pie charts which shows the composition of categorical variable.
Many others include: pair plots, violin plots, density plots, word cloud ...

All these visualization techniques can be done in both Python, and other visualization tools such as Excel, PowerBi and Tableau. Choice of a tools depends on the user's and interest and what they would like to achieve.

Therefore, EDA through visualization is a key step in the data analysis process that helps leverage insight into data understanding that results to appropriate business decision making.

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