Steps of EDA.

EDA (Exploratory Data Analysis)?is an essential step in the data analysis process that involves understanding the characteristics of the data set to extract insights and identify patterns. The following are the common steps involved in EDA:

Define the problem and ask relevant questions: Before starting with EDA, it's important to have a clear understanding of the problem you're trying to solve and the questions you're trying to answer with the data. This helps to guide the analysis and ensures that the insights gained are relevant to the problem.

Collect the data: Gather the data from various sources, such as databases, files, APIs, or web scraping.

Explore the data: This step involves getting familiar with the data by examining its structure, contents, and quality. It involves:

a. Checking the size of the data set, the number of columns, and rows.

b. Looking at the data types of each column and checking if they are the expected data types.

c. Checking for missing values and understanding the distribution of data.

d. Visualizing the data using graphs, histograms, and box plots to identify patterns and outliers.

Preprocess the data: This step involves cleaning, transforming, and restructuring the data to make it ready for analysis. It involves:

a. Removing or filling missing values.

b. Removing duplicates and irrelevant columns.

c. Converting data types, scaling, and normalizing the data.

d. Encoding categorical variables.

Analyze the data: In this step, we apply statistical and machine learning techniques to extract insights and identify patterns in the data. It involves:

a. Applying descriptive statistics to understand the central tendency, variability, and distribution of the data.

b. Applying inferential statistics to make inferences about the population based on the sample data.

c. Applying machine learning algorithms to build predictive models and classify data.

Visualize the data: Visualization is a powerful tool to communicate the findings of the analysis. It involves creating plots, graphs, and charts to illustrate the patterns and insights found in the data.

Draw conclusions and make recommendations: In this step, we interpret the results of the analysis and draw conclusions that are relevant to the problem at hand. It involves:

a. Summarizing the findings in a clear and concise manner.

b. Identifying key insights and trends.

c. Making recommendations based on the insights and trends identified in the analysis.

Document the process: It's important to document the EDA process to ensure that the analysis can be reproduced and the findings can be communicated effectively. It involves:

a. Creating a report that includes the problem statement, data collection process, EDA steps, findings, and recommendations.

b. Sharing the report with stakeholders and team members.