Exploratory data analysis is a simple classification technique usually done by visual methods. It is an approach to analyzing data sets to summarize their main characteristics. When you are trying to build a machine learning model you need to be pretty sure whether your data is making sense or not.
Every machine learning problem solving starts with EDA. It is probably one of the most important part of a machine learning project. With the growing market, the size of data is also growing. It becomes harder for companies to make decision without proper analyzing it.
The Significance of Exploratory Data Analysis
Exploratory Data Analysis is the preliminary step in data analysis where you get to know your data before diving into more complex modeling or hypothesis testing. Its primary objectives are as follows:

1. Data Cleaning: Identifying and rectifying missing values, outliers, and other data quality issues.
2. Data Exploration: Understanding the distribution, summary statistics, and characteristics of the data.
3. Pattern Recognition: Discovering relationships, trends, and correlations among variables.
4. Assumption Checking: Assessing if the data meets the assumptions required for further statistical analysis.
5. Feature Selection: Identifying which features (variables) are most relevant for your analysis or modeling. To achieve these objectives effectively, data visualization plays a pivotal role. Visualization helps transform raw data into understandable patterns and trends, making it easier to draw meaningful conclusions

With the use of charts and certain graphs, one can make sense out of the data and check whether there is any relationship or not.
Various plots are used to determine any conclusions. This helps the company to make a firm and profitable decisions. Once Exploratory Data Analysis is complete and insights are drawn, its feature can be used for supervised and unsupervised machine learning modelling.

Data scientists can use exploratory analysis to ensure the results they produce are valid and applicable to any desired business outcomes and goals. EDA also helps stakeholders by confirming they are asking the right questions. EDA can help answer questions about standard deviations, categorical variables, and confidence intervals. Once EDA is complete and insights are drawn, its features can then be used for more sophisticated data analysis or modeling, including machine learning.
Exploratory data analysis tools
Specific statistical functions and techniques you can perform with EDA tools include:
• Clustering and dimension reduction techniques, which help create graphical displays of high-dimensional data containing many variables.
• Univariate visualization of each field in the raw dataset, with summary statistics.
• Bivariate visualizations and summary statistics that allow you to assess the relationship between each variable in the dataset and the target variable you’re looking at.
• Multivariate visualizations, for mapping and understanding interactions between different fields in the data.
• K-means Clustering is a clustering method in unsupervised learning where data points are assigned into K groups, i.e. the number of clusters, based on the distance from each group’s centroid. The data points closest to a particular centroid will be clustered under the same category. K-means Clustering is commonly used in market segmentation, pattern recognition, and image compression.
• Predictive models, such as linear regression, use statistics and data to predict outcomes.
There are four primary types of EDA:
• Univariate non-graphical. This is the simplest form of data analysis, where the data being analyzed consists of just one variable. Since it’s a single variable, it doesn’t deal with causes or relationships. The main purpose of univariate analysis is to describe the data and find patterns that exist within it.
• Univariate graphical. Non-graphical methods don’t provide a full picture of the data. Graphical methods are therefore required. Common types of univariate graphics include:
o Stem-and-leaf plots, which show all data values and the shape of the distribution.
o Histograms, a bar plot in which each bar represents the frequency (count) or proportion (count/total count) of cases for a range of values.
o Box plots, which graphically depict the five-number summary of minimum, first quartile, median, third quartile, and maximum.
• Multivariate nongraphical: Multivariate data arises from more than one variable. Multivariate non-graphical EDA techniques generally show the relationship between two or more variables of the data through cross-tabulation or statistics.
• Multivariate graphical: Multivariate data uses graphics to display relationships between two or more sets of data. 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.
When performing EDA, we can have the following types of variables:
• Numerical — a variable that can be quantified. It can be either discrete or continuous.
• Categorical — a variable that can assume only a limited number of values.
• Ordinal — a numeric variable that can be sorted
Other common types of multivariate graphics include:
• 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.
• Multivariate chart, which is a graphical representation of the relationships between factors and a response.
• Run chart, which is a line graph of data plotted over time.
• Bubble chart, which is a data visualization that displays multiple circles (bubbles) in a two-dimensional plot.
• Heat map, which is a graphical representation of data where values are depicted by color.

In summary, Exploratory Data Analysis (EDA) stands as an indispensable initial phase in the data analysis journey, and it's firmly rooted in the practice of data visualization. Through the application of diverse data visualization techniques, you can delve deeper into your dataset, revealing hidden patterns, outliers, and critical insights. This holds true whether you're a data scientist, analyst, or a business professional. Proficiency in the art of data visualization equips you with the ability to unearth valuable knowledge concealed within your data, leading to more informed decision-making and more effective problem-solving.