Detailed Guide to Exploratory Data Analysis (EDA) in ML

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Every machine learning project must clearly understand the data to ensure it fits the problem well. However, this is done in a step called Exploratory Data Analysis (EDA). Here, we clean up the data, find any unusual values, and check if it's suitable for answering our questions. EDA is like exploring a new place to find clues and ideas for our project.

Explaining Exploratory Data Analysis

Exploratory Data Analysis (EDA) is just like exploring a new place. Before you start building something, you need to understand the area. In addition, EDA helps us look closely at our data, find interesting things, and see how different parts of the data are connected. It's a big part of data science projects, and it helps us make better decisions later on.
 

Data science exploratory analysis is like exploring a new place and finding hidden treasures. It helps us see things in the data that we might not have noticed before. We can understand the different parts and how they fit together by looking closely at the data. Additionally, this helps us choose the right tools to analyze the data and get the best results. EDA has been used for a long time, and it's still a very important part of data science today.

Types of Exploratory Data Analysis

EDA helps us look closely at our data, find interesting things, and see how different parts of the data are connected. Moreover, there are many types of EDA, and the best way depends on the kind of data you have and what you want to learn. You can divide exploratory data analysis into three types based on how many parts of the data you're looking at: Univariate (one part), Bivariate (two parts), and Multivariate (many parts).
 

1. Univariate Analysis
    

Univariate analysis helps us understand that piece better. We can look at how it looks, where it fits, and what it means. As a result, this is done by looking at things like histograms (showing how many times something happens), box plots (showing the spread of the data), bar charts (showing different groups), and summary statistics (like the average or how spread out the data is).
 

2. Bivariate Analysis
    

Bivariate analysis helps us understand how those pieces fit together and if there's a connection between them. Apart from that, we can use scatter plots to see how two things change together, correlation coefficients to see how strong the connection is, cross-tabulation to compare different groups, line graphs to see how things change over time, and covariance to see if two things move together.
 

3. Multivariate Analysis
    

Multivariate analysis helps us understand how those pieces fit together and how they affect each other. With the help of this, we can use pair plots to see how many things change together, and PCA to simplify the dilemma by focusing on the most important parts.

What is EDA Used For?

Automated Exploratory Data Analysis (EDA) helps us understand the data we're working with without making guesses or using complicated math. Moreover, we can find interesting things, like patterns and trends, and see if there's anything strange in the data. This helps us make better decisions later on. By looking at each part of the data and how they fit together, we can understand what the data is like and find any mistakes or weird things. As a result, this helps us figure out what's important in the data and make smarter choices.

Exploratory Data Analysis Steps

Exploratory Data Analysis (EDA) helps us understand the data we're working with, find interesting things, and make sure it's ready to use. We look for patterns, find any unusual things, and check if the data is clean and ready to use for further analysis.

Step 1: Comprehend the Data and the Problem
 

• What is the goal? What do you want to achieve?
• Understanding the data is crucial. What information do you possess?
• Ever wondered what the data is trying to tell us? What stories do the different parts of the data hold?
• What kind of data is it? Is it numbers, categories, text, or something else?
• Could you let me know if there are any issues with the data? Do you see anything that's not quite right or missing?
• Are there any special things to consider? Are there any rules or limits to follow?

Ask people who know about the problem and the data for their input. This will help you understand the situation better.

Step 2: Import and Review the Data
 

• After that, bring the data to your computer. Use a program like Python, R, or a spreadsheet to work with the data.
• Look at the data and check how big the data is and what kind of information it has.
• Next, find missing information. Check if there are any empty spaces in the data, as this can affect your analysis.
• Afterward, check the type of data and see if the data is numbers, words, or something else.
• Look for any mistakes. Check if there are any errors or strange things in the data that might be wrong.

Step 3: Dealing with Missing Data
 

• Then, figure out why the data is missing.
• Next, decide what to do. Remove the data with missing parts or fill in the missing parts.
• Further, opt for the different ways to fill in the missing parts, like using the average or using other data.
• Even after filling in the missing parts, the data may still have problems.
• Explain how you dealt with the missing data and why you chose that method.

Step 4: Analyze Data Characteristics

Now that you've fixed the missing parts of your data, the next step is to explore what the data looks like. In short, looking at how the data is spread out, where the middle point is, and how much it varies. Understanding these things will help you choose the right tools to analyze the data and find any problems.
 

You can calculate summary statistics like the average, median, mode, standard deviation, skewness, and kurtosis. Moreover, these numbers will give you a quick idea of how the data is distributed and where the middle point is, which can help you find any unusual things in the data.
 

Step 5: Perform Data Transformation
 

Data transformation is like changing the shape of a puzzle piece to make it fit better. It helps you prepare your data so it's ready to be analyzed and used to build models. You might need to change the data in different ways depending on what it looks like and what you want to do with it.
 

• Further, change the numbers so they're all in the same range.
• Then, turn words or groups into numbers that can be used in models.
• Now, use math to change numbers if they're not evenly spread out or if they follow a curve.
• Subsequently, combine or divide existing numbers to create new information.
• Put data together based on different parts or conditions.

By doing these things, you can make sure your analysis and models work well and give you good results.
 

Step 6: Envision Data Relationships
 

Pictures can help you see things in the data that numbers alone might miss. You can use different kinds of pictures to look at one part of the data, two parts together, or many parts at once.
 

• Create tables, bars, or pies to see how often things happen.
• Make histograms, box plots, violin plots, or density plots to see how the numbers are spread out.
• Make scatter plots, and correlation matrices, or use exceptional tests to see how two or more things are connected.

By looking at these pictures, you can learn more about the data and decide better what to do next.

Step 7: Dealing with Outliers
 

Outliers are like strange puzzle pieces that don't fit with the others. They can be caused by mistakes or unusual things. Finding and removing outliers is an important part of data analysis.
 

• Find outliers: Use methods like the interquartile range (IQR) or Z-scores.
• Check outliers: Look at them carefully to see if they're wrong.

Outliers can make your analysis look wrong, so it's important to deal with them correctly.
 

Step 8: Sharing Your Findings
 

• Tell people what you did. In other words, explain the goal of your analysis and what you found.
• Next, give background information. Further, it helps people understand what you did by giving them some context.
• Use charts and graphs to make your findings easier to understand.
• Point out the most interesting things you found.
• Mention any difficulties you faced or things that might be wrong.
• At last, say what you think should be done next.

Sharing your findings is important so that people understand what you did and what it means.

Exploratory Data Analysis Tools

There are many basic tools of eda in data science you can use to explore data, and the best one for you depends on how complicated your project is, how much you know about programming, and what you need to do. Here are some popular options:
 

• Python Libraries: Python has tools like Pandas, NumPy, Matplotlib, Seaborn, and Plotly that used by many data scientists.
• R Programming: R is another language for data analysis, and it has tools like dplyr, ggplot2, and shiny. You can use RStudio to work with R.
• Jupyter Notebooks: Jupyter Notebooks lets you make documents with code, pictures, and writing, and you can share them too. In addition, they work with many different programming languages.
• Tableau: Tableau is an exploratory data analysis in data science tool for making moving pictures of data. It's easy to use and helps you understand data quickly.
• Microsoft Excel: Excel is a good tool for making tables and doing simple things with data, but it's not as powerful as the other tools.

Choose the tool that's right for you and your project!

Exploratory Data Analysis Example

We have a list of students with information about them. We want to learn interesting things from this list.
 

First, we put the student information into a computer program. Then, we look at the list to see how many students there are and what kind of information is in each column. After that, we calculate simple numbers for things like age and test scores. As a result, this helps us understand the data better. For things like gender, we just count how many boys and girls there are.
 

We make pictures to understand the data better and can use histograms, bar charts, and scatter plots to see how the data is spread out and how different things are related. We try to find things that happen together. For example, we might see if students who study a lot get higher scores, or if boys and girls have different scores.

Our Learner Also Reads: Top 10 Data Analysis Tools and Software for Big Data Analytics
 

Conclusion
 

Exploratory Data Analysis (EDA) is a powerful tool for understanding data. Apart from that, it helps us understand the data we're working with, find interesting things, and make sure it's ready to use. In addition to that, we look for patterns, find any unusual things, and check if the data is clean and ready to use for further analysis.