Exploratory Data Analysis (EDA) is an important step in the data analysis process. It is a data exploration approach used to understand the many aspects of a dataset. It entails summarizing significant parts of data, sometimes with visual aids, in order to understand its structure, uncover anomalies, and test hypotheses.
This technique enables analysts to get relevant insights and develop data-driven conclusions.

Objectives of EDA

EDAis required for understanding the data and preparing it for further analysis or modeling purposes. EDA ensures that the data is clear, comprehensible, and appropriate for creating reliable and accurate models. EDA gives you a better grasp of the data, which is necessary for effective feature engineering.

The main goals of EDA are as follows:

Steps for Exploratory Data Analysis (EDA)

1. Understand the Data:
   Load and get basic information about the dataset.
2. Distinguish Attributes:
   Clean data by removing missing values, duplicates, and outliers.
3. Univariate Analysis:
   Analyze individual variables.
4. Bivariate Analysis:
   Analyze relationships between two variables.
5. Multivariate Analysis:
   Analyze relationships among multiple variables.
6. Detect Outliers/Feature Engineering:
   Analyze relationships, create meaningful data from raw data respectively.
7. Insights and Conclusions:
   Summarize and document findings, including insights and conclusions.
8. By going through these steps, you will be able to carefully examine and understand your dataset, which will enable you to make well-informed decisions while modeling.

Exploratory Data Analysis (EDA) is an iterative process that involves several steps to gain insights from a data set. The specific steps may vary depending on the data set and the problem being addressed. 

Summary

Exploratory Data Analysis (EDA) is an iterative process that includes understanding the data structure, cleaning the data, performing descriptive statistics, visualizing data distributions, identifying relationships, dealing with missing values, detecting outliers, and gathering insights for feature engineering. These procedures aid in the preparation of data for subsequent analysis and modeling, ensuring that the features utilized in machine learning models are well understood and useful.