Exploratory Data Analysis using Data Visualization Techniques.
Tags: dataanalysis, visualization, python, datascience
Published: 2023-10-10 18:19:16

## What is Exploratory Data Analysis?
Exploratory data analysis is a process used to analyze and summarize datasets.
Using various statistical and graphical tools, EDA tries to find patterns, detect anomalies, test hypotheses, and validate assumptions. It is an important part of the data science process because it allows analysts to obtain a thorough grasp of their data before going on to more advanced modeling and machine learning tasks.

## Steps involved in Exploratory Data analysis
1.  Data Collection
The data that will be evaluated is collected as the first step in the EDA process. Data can be obtained from a variety of sources, including structured databases, APIs, and even web scraping. To ensure compatibility with the analysis tools you intend to employ, it is critical to understand the many types of data sources, as well as their formats and architectures.

2. Data Cleaning
Once the data has been acquired, it must be cleaned and preprocessed to assure its quality and dependability. Handling missing numbers, deleting duplicates, changing data types, and detecting and addressing outliers are all examples of this process.

3. Data Exploration
Data exploration is the process of studying a dataset using various statistical and graphical approaches in order to discover the underlying structure, relationships, and trends in the data. This process is divided into three parts: univariate, bivariate, and multivariate analysis.

## Data Visualization

Data visualization facilitates the efficient communication of insights and patterns identified throughout the exploratory data analysis process. Choosing the correct style of visualization, following best practices, and utilizing popular visualization libraries can all help to increase the impact of your study.

## Choosing the Right Type of Visualization
The suitable visualization is determined by the nature of the data and the insights you wish to express. Bar charts, line charts, pie charts, scatter plots, and heatmaps are examples of common visualization types. Each of these visualizations has a specific function, such as comparing categories, exhibiting trends through time, or demonstrating relationships between variables.

## Visualization Techniques Used for Exploratory Data Analysis.

Several visualization tools and techniques are in use. Here are the mostly used for the same.

## **Univariate Analysis**
Univariate analysis examines the distribution, central tendency, and dispersion of a single variable. This study aids in comprehending the unique properties of each variable in the dataset.

- **Histograms**
Histograms are graphical representations of a variable's distribution that divide data points into bins based on their values. Histograms aid in the identification of the distribution's form, any gaps or clusters, and probable outliers. 

- **Box plots**
Box plots show the quartile distribution of a variable, showing the median, interquartile range (IQR), and any outliers. Box plots show the dispersion of a variable in a compact manner.

## **Bivariate Analysis**
Involves examining the relationship between two variables, exploring potential correlations, trends, or patterns between them.
Below are bivalents plots used for EDA;

- **Correlation plots or Heatmaps**
Heatmaps use color intensity to depict the strength of a relationship between many variables in a matrix format. This visualization aids in quickly recognizing groups of similar variables.

- **Bar Graphs**
They are used to compare nominal or ordinal data. They are helpful for recognizing trends.

## **Multivariate Analysis**
Multivariate analysis examines the relationships among three or more variables simultaneously, providing a more holistic view of the data.
Below are multivariate analysis techniques

- **Multiple linear regression**
A dependence method that examines the relationship between one dependent variable and two or more independent variables. A multiple regression model will tell you how well each independent variable correlates with the dependent variable. This is useful because it allows you to forecast future outcomes by understanding which elements are likely to impact a specific event.

- **Multiple logistic regression**
The chance of a binary event occurring is calculated (and predicted) using logistic regression analysis. A binary result has only two possible outcomes: the event occurs (1) or it does not occur (0). So, logistic regression can forecast the likelihood of a given situation based on a set of independent factors. It is also employed in classification. 

- **Multivariate analysis of variance (MANOVA)**
MANOVA is a statistical method for determining the effect of many independent factors on two or more dependent variables. It's vital to remember that the independent variables in MANOVA are categorical, whereas the dependent variables are metric.
In MANOVA analysis, you look at different combinations of independent variables to see how they differ in their influence on the dependent variable.

- **Factor analysis**
Factor analysis is an interdependent technique for reducing the number of variables in a dataset. Finding patterns in data might be tough if you have too many variables. Simultaneously, models built with too many variables are vulnerable to overfitting. Overfitting is a modeling error that occurs when a model fits a dataset too closely and specifically, making it less generalizable to future datasets and potentially less accurate in its predictions.
Factor analysis works by identifying groups of variables that have a high correlation with one another. These variables can then be merged to form a single variable. Factor analysis is frequently used by data analysts to prepare data for further examination.

- **Cluster analysis**
Cluster analysis is used to group similar items within a dataset into clusters. When grouping data into clusters, the aim is for the variables in one cluster to be more similar to each other than they are to variables in other clusters. 
Cluster analysis helps you to understand how data in your sample is distributed, and to find patterns.

## Exploratory Data Analysis tools and libraries





## Python Libraries
Commonly used python libraries are:

- **Pandas**
Referred as pd. It used for data analysis and manipulation

- **Numpy**
Referred as np. It is used for numerical computing, offering support for arrays and mathematical functions.

- **Matplotlib**
Referred as plt. It used for creating static, interactive, and animated visualizations.

- **Seaborn**
Referred as sns. It is a statistical data visualization library based on Matplotlib, providing a high-level interface for creating informative and attractive visualizations.

## R Libraries
R is another popular programming language for data analysis that has a robust ecosystem of EDA modules. R libraries that are regularly used include:

- **dplyr**
A data manipulation package that provides a consistent collection of methods for filtering, sorting, and aggregating data.

- **ggplot2**
A sophisticated and versatile data visualization library based on the Grammar of Graphics, allowing complex visualizations to be created with minimal code.

## Data Visualization Tools
Aside from programming languages and libraries, EDA use a variety of data visualization tools. These tools provide an easier-to-use interface for producing and customizing visualizations. Among the most popular data visualization tools are:

- **Tableau**
A popular data visualization software that enables users to create interactive and shareable dashboards.

- **Power BI**
A Microsoft business analytics service that provides data visualization and reporting capabilities.

In conclusion, EDA is an important step in the data analytics process because it allows data scientists to understand the features of the data, uncover patterns and relationships, and make educated judgments in following analysis or modeling stages. Data scientists may extract useful insights from their data and construct more effective and interpretable machine learning models by utilizing various EDA approaches and tools.

Always keep in mind, EDA is an iterative and exploratory process, and that constantly improving your research will result in a greater knowledge and more robust findings. You will develop a deeper understanding for the data and its underlying patterns as you gain expertise in EDA, making you a more effective data scientist and analyst.