**What Is EDA In Machine Learning? A Comprehensive Guide**

Exploratory Data Analysis (EDA) in machine learning is a crucial initial step that involves delving into and visualizing data to grasp its core characteristics, pinpoint patterns, and uncover relationships between different elements. LEARNS.EDU.VN champions a thorough understanding of EDA as it allows for the identification of anomalies and outliers before advancing to sophisticated statistical modeling or algorithm development. Master the art of EDA and unlock the power of data-driven insights, improve data quality, and refine feature engineering.

1. Why Exploratory Data Analysis (EDA) Matters in Machine Learning

Exploratory Data Analysis (EDA) is indispensable in machine learning for a multitude of reasons. It’s the compass that guides data scientists through the uncharted territories of datasets. Think of EDA as the detective work that uncovers the hidden narratives within the data, setting the stage for building robust and reliable machine learning models.

1.1. Understanding Data Structure

EDA provides a comprehensive understanding of a dataset’s structure, which includes the number of features, the type of data contained within each feature (e.g., numerical, categorical), and the data distribution. This foundational knowledge helps in selecting appropriate analytical techniques. For example, understanding the data type informs whether to use a histogram (for numerical data) or a bar chart (for categorical data).

1.2. Uncovering Hidden Patterns and Relationships

One of the primary goals of EDA is to reveal latent patterns and relationships between different data points. These insights are crucial for feature engineering and model building. Correlation analysis, a key component of EDA, can highlight which variables are strongly related, enabling data scientists to focus on the most influential features.

1.3. Identifying Outliers and Anomalies

EDA enables the detection of errors, outliers, and anomalies that could potentially skew results. Outliers, if not properly addressed, can lead to biased models. Techniques like box plots and scatter plots are invaluable for spotting these unusual data points.

1.4. Guiding Feature Selection and Engineering

The insights gleaned from EDA play a pivotal role in deciding which features are most relevant for building models. It also guides how these features should be prepared to enhance performance. Feature importance rankings, derived from EDA, help prioritize the most impactful variables.

1.5. Enhancing Model Selection and Adjustment

By providing a deep understanding of the data, EDA aids in selecting the most appropriate modeling techniques and fine-tuning them for optimal results. Different algorithms perform differently based on the characteristics of the data; EDA helps match the right algorithm to the right dataset.

2. Types of Exploratory Data Analysis (EDA)

The nature of the data determines the type of EDA strategies employed. EDA can be broadly categorized into three types based on the number of variables being analyzed: univariate, bivariate, and multivariate. Each type serves a unique purpose and offers different insights.

2.1. Univariate Analysis

Univariate analysis focuses on examining one variable at a time to understand its distribution, central tendency, and spread. It helps to describe the data and identify patterns within a single feature. Common methods include:

Histograms: Display the distribution of numerical data.
Box Plots: Identify outliers and understand data spread.
Bar Charts: Represent categorical data.
Summary Statistics: Include mean, median, mode, variance, and standard deviation.

2.2. Bivariate Analysis

Bivariate analysis explores the relationship between two variables to uncover connections, correlations, and dependencies. This analysis is crucial for understanding how two variables interact. Key techniques include:

Scatter Plots: Visualize the relationship between two continuous variables.
Correlation Coefficient: Measures the strength and direction of the linear relationship between two variables.
Cross-Tabulation (Contingency Tables): Show the frequency distribution of two categorical variables.
Line Graphs: Compare two variables over time, especially useful in time series data.
Covariance: Measures how two variables change together.

2.3. Multivariate Analysis

Multivariate analysis examines relationships among three or more variables. This approach is essential for understanding complex interactions within the dataset. Techniques include:

Pair Plots: Display relationships between multiple variables at once.
Principal Component Analysis (PCA): Reduces the complexity of large datasets by simplifying them while retaining the most important information.

Alt Text: Pair plots showing relationships between multiple variables at once, helping to see how they interact

2.4. Specialized EDA Techniques

Beyond the standard univariate, bivariate, and multivariate analyses, several specialized EDA techniques cater to specific data types and analysis needs.

Spatial Analysis: Used for geographical data to understand the spatial distribution of variables using maps and spatial plotting.
Text Analysis: Involves techniques like word clouds, frequency distributions, and sentiment analysis to explore text data.
Time Series Analysis: Applied to datasets with a temporal component to inspect and model trends, patterns, and seasonality. Techniques include line plots, autocorrelation analysis, moving averages, and ARIMA models.

3. Comprehensive Guide on How To Perform Exploratory Data Analysis (EDA)

Performing EDA involves a structured series of steps designed to provide a comprehensive understanding of the data. These steps help uncover underlying patterns, identify anomalies, test hypotheses, and ensure the data is clean and suitable for further analysis. Let’s break down each step.

3.1. Step 1: Understand the Problem and the Data

The first step in any data analysis project is to clearly understand the problem you’re trying to solve and the data you have. This involves asking key questions:

What is the business goal or research question?
What are the variables in the data and what do they represent?
What types of data (numerical, categorical, text, etc.) do you have?
Are there any known data quality issues or limitations?
Are there any domain-specific concerns or restrictions?

By thoroughly understanding the problem and the data, you can better plan your analysis, avoid incorrect assumptions, and ensure accurate conclusions. This step sets the direction for the entire EDA process.

3.2. Step 2: Import and Inspect the Data

After clearly understanding the problem and the data, the next step is to import the data into your analysis environment (like Python, R, or a spreadsheet tool). At this stage, it’s crucial to examine the data to get an initial understanding of its structure, variable types, and potential issues.

Here’s what you can do:

Load the data: Import the data into your environment carefully to avoid errors or truncations.
Examine the size: Check the number of rows and columns to understand the dataset’s complexity.
Check for missing values: Identify missing values and their distribution across variables, as this can impact the quality of your analysis.
Identify data types: Determine the data type for each variable (e.g., numerical, categorical) to inform subsequent data manipulation and analysis.
Look for errors: Spot errors or inconsistencies such as invalid values, mismatched units, or outliers, which could indicate deeper data issues.

By completing these tasks, you’ll be prepared to clean and analyze the data more effectively.

3.3. Step 3: Handle Missing Data

Missing data is a common issue in many datasets and can significantly affect the quality of your analysis. During EDA, it’s essential to identify and handle missing data properly to avoid biased or misleading results.

Here’s how to handle it:

Understand the patterns: Determine the reasons for missing data. Is it Missing Completely at Random (MCAR), Missing at Random (MAR), or Missing Not at Random (MNAR)? Understanding the nature of missingness helps decide how to handle the missing data.
Decide on a strategy: Determine whether to remove missing data (listwise deletion) or impute (fill in) the missing values. Removing data can lead to biased outcomes, especially if the missing data isn’t MCAR. Imputing values helps preserve data but should be done carefully.
Use imputation methods: Apply appropriate imputation methods such as mean/median imputation, regression imputation, or machine learning techniques like KNN or decision trees, based on the data’s characteristics.
Consider the impact: Even after imputing, missing data can introduce uncertainty and bias, so interpret the results with caution.

Properly handling missing data improves the accuracy of your analysis and prevents misleading conclusions.

3.4. Step 4: Explore Data Characteristics

After addressing missing data, the next step in EDA is to explore the characteristics of your data by examining the distribution, central tendency, and variability of your variables, as well as identifying any outliers or anomalies. This helps in selecting appropriate analysis methods and spotting potential data issues.

You should calculate summary statistics like mean, median, mode, standard deviation, skewness, and kurtosis for numerical variables. These provide an overview of the data’s distribution and help identify any irregular patterns or issues.

3.5. Step 5: Perform Data Transformation

Data transformation is an essential step in EDA because it prepares your data for accurate analysis and modeling. Depending on your data’s characteristics and analysis needs, you may need to transform it to ensure it’s in the right format.

Common transformation techniques include:

Scaling or normalizing numerical variables (e.g., min-max scaling or standardization).
Encoding categorical variables for machine learning (e.g., one-hot encoding or label encoding).
Applying mathematical transformations (e.g., logarithmic or square root) to correct skewness or non-linearity.
Creating new variables from existing ones (e.g., calculating ratios or combining variables).
Aggregating or grouping data based on specific variables or conditions.

3.6. Step 6: Visualize Data Relationships

Visualization is a powerful tool in the EDA process, helping to uncover relationships between variables and identify patterns or trends that may not be obvious from summary statistics alone.

For categorical variables, create frequency tables, bar plots, and pie charts to understand the distribution of categories and identify imbalances or unusual patterns.
For numerical variables, generate histograms, box plots, violin plots, and density plots to visualize distribution, shape, spread, and potential outliers.
To explore relationships between variables, use scatter plots, correlation matrices, or statistical tests like Pearson’s correlation coefficient or Spearman’s rank correlation.

3.7. Step 7: Handling Outliers

Outliers are data points that significantly differ from the rest of the data, often caused by errors in measurement or data entry. Detecting and handling outliers is important because they can skew your analysis and affect model performance.

You can identify outliers using methods like interquartile range (IQR), Z-scores, or domain-specific rules. Once identified, outliers can be removed or adjusted depending on the context. Properly managing outliers ensures your analysis is accurate and reliable.

3.8. Step 8: Communicate Findings and Insights

The final step in EDA is to communicate your findings clearly. This involves summarizing your analysis, pointing out key discoveries, and presenting your results in a clear and engaging way.

Clearly state the goals and scope of your analysis.
Provide context and background to help others understand your approach.
Use visualizations to support your findings and make them easier to understand.
Highlight key insights, patterns, or anomalies discovered.
Mention any limitations or challenges faced during the analysis.
Suggest next steps or areas that need further investigation.

Effective communication is critical for ensuring that your EDA efforts have a meaningful impact and that your insights are understood and acted upon by stakeholders.

4. Tools and Software for Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) can be performed using a variety of tools and software, each offering features that cater to different data types and analysis needs. Selecting the right tool can significantly enhance the efficiency and effectiveness of your EDA process.

4.1. Python Libraries

Python, with its rich ecosystem of data science libraries, is a popular choice for EDA. Several key libraries stand out:

Pandas: Essential for data manipulation, providing functions to clean, filter, and transform data. It offers data structures like DataFrames that simplify data handling and analysis.
Matplotlib: Used for creating basic static, interactive, and animated visualizations. It’s a foundational library for plotting data in Python.
Seaborn: Built on top of Matplotlib, Seaborn allows for the creation of more attractive and informative statistical plots. It simplifies the creation of complex visualizations with minimal code.
Plotly: An excellent choice for interactive and advanced visualizations. Plotly allows users to create dynamic plots that can be easily shared and embedded in web applications.

4.2. R Packages

R, another leading programming language for statistical computing, offers several powerful packages for EDA:

ggplot2: A powerful package for creating complex and visually appealing plots from data frames. It follows the grammar of graphics, allowing for highly customizable and aesthetically pleasing visualizations.
dplyr: Helps in data manipulation, making tasks like filtering and summarizing easier. Dplyr provides a consistent and intuitive syntax for data manipulation.
tidyr: Ensures your data is in a tidy format, making it easier to work with. Tidyr focuses on reshaping data to make it analysis-ready.

4.3. Spreadsheet Software

Spreadsheet software like Microsoft Excel and Google Sheets are also useful for basic EDA, especially for smaller datasets. These tools offer features for sorting, filtering, and creating simple charts.

Microsoft Excel: Widely used for data entry and basic analysis. It includes features for creating pivot tables and charts.
Google Sheets: A cloud-based spreadsheet application that allows for real-time collaboration. It offers similar functionalities to Excel and integrates well with other Google services.

4.4. Specialized EDA Tools

For more advanced EDA, specialized tools offer advanced features and capabilities:

Tableau: A data visualization tool that allows you to create interactive dashboards and reports. Tableau is known for its ease of use and powerful visualization capabilities.
Power BI: Microsoft’s business analytics service that provides interactive visualizations and business intelligence capabilities. Power BI integrates well with other Microsoft products and services.

5. Real-World Applications of EDA in Machine Learning

Exploratory Data Analysis (EDA) isn’t just a theoretical exercise; it’s a practical necessity with wide-ranging applications across various domains. Let’s explore some real-world examples to understand the impact and importance of EDA in machine learning.

5.1. Healthcare: Predicting Patient Readmission

In healthcare, EDA can be used to analyze patient data to predict readmission rates. By exploring variables such as age, medical history, lab results, and length of stay, analysts can identify patterns that contribute to readmissions. For example, EDA might reveal that patients with specific chronic conditions and a history of frequent hospital visits are more likely to be readmitted. This information can then be used to develop predictive models and implement targeted interventions to reduce readmission rates.

5.2. Finance: Fraud Detection

In the finance industry, EDA is crucial for detecting fraudulent transactions. By analyzing transaction data, including transaction amount, location, time, and user information, analysts can identify unusual patterns that may indicate fraud. EDA can help uncover that transactions occurring in unusual locations, at odd hours, or involving unusually large amounts are more likely to be fraudulent. These insights can be used to build fraud detection systems that flag suspicious transactions for further investigation.

5.3. Marketing: Customer Segmentation

In marketing, EDA can be used to segment customers based on their purchasing behavior, demographics, and other relevant data. By exploring variables such as purchase frequency, average transaction value, product preferences, and demographic information, marketers can identify distinct customer segments. For example, EDA might reveal segments of high-value customers who frequently purchase premium products, or price-sensitive customers who primarily buy discounted items. This information can be used to develop targeted marketing campaigns and personalized product recommendations.

5.4. Manufacturing: Quality Control

In manufacturing, EDA is used to monitor and improve product quality. By analyzing data from sensors and quality control checks, manufacturers can identify factors that contribute to defects. For example, EDA might reveal that specific machine settings or raw material batches are associated with a higher defect rate. This information can be used to optimize manufacturing processes and reduce defects, leading to improved product quality and reduced costs.

Alt Text: Quality control in manufacturing involves monitoring and improving product quality by analyzing data from sensors and quality control checks, helping to identify factors that contribute to defects

5.5. Environmental Science: Climate Change Analysis

In environmental science, EDA can be used to analyze climate data and understand the impact of climate change. By exploring variables such as temperature, precipitation, sea level, and carbon emissions, scientists can identify trends and patterns that indicate the effects of climate change. For example, EDA might reveal that average temperatures are increasing over time, sea levels are rising, and extreme weather events are becoming more frequent. These insights can be used to inform policy decisions and develop strategies to mitigate the impacts of climate change.

6. Common Mistakes to Avoid During Exploratory Data Analysis (EDA)

While Exploratory Data Analysis (EDA) is a crucial step in data science, it’s easy to fall into common traps that can lead to inaccurate insights or wasted time. Avoiding these mistakes can significantly improve the quality and efficiency of your analysis.

6.1. Jumping to Conclusions Too Quickly

One of the most common mistakes is drawing conclusions before thoroughly exploring the data. It’s tempting to form hypotheses and seek confirmation, but this can lead to biased analysis. Instead, approach the data with an open mind and let the data speak for itself. Spend sufficient time exploring different variables and relationships before forming any conclusions.

6.2. Ignoring Missing Data

Missing data can significantly impact your analysis if not handled properly. Ignoring missing data or simply removing rows with missing values without understanding why they are missing can lead to biased results. Always investigate the patterns of missing data and consider appropriate imputation techniques before proceeding with your analysis.

6.3. Misinterpreting Correlation

Correlation measures the strength and direction of a linear relationship between two variables, but it does not imply causation. Mistaking correlation for causation is a common error that can lead to incorrect conclusions. Always consider other factors and potential confounding variables when interpreting correlations.

6.4. Overlooking Outliers

Outliers can skew your analysis and affect the performance of machine learning models. Ignoring outliers or treating them as errors without proper investigation can lead to inaccurate results. Always identify and analyze outliers to determine whether they are genuine data points or errors that need to be addressed.

6.5. Neglecting Data Types

Failing to recognize and handle different data types appropriately can lead to errors in your analysis. Treating categorical variables as numerical variables, or vice versa, can produce meaningless results. Always verify the data types of your variables and use appropriate analysis techniques for each type.

6.6. Over-Reliance on Default Settings

Most EDA tools come with default settings for visualizations and statistical tests. Relying solely on these defaults without understanding their implications can lead to suboptimal or misleading results. Always customize your visualizations and statistical tests to suit the specific characteristics of your data and analysis goals.

6.7. Inadequate Documentation

Failing to document your EDA process can make it difficult to reproduce your analysis or communicate your findings to others. Always keep detailed records of your steps, decisions, and findings throughout the EDA process. This will help ensure transparency and reproducibility.

6.8. Not Communicating Findings Effectively

EDA is not complete until you have effectively communicated your findings to stakeholders. Failing to present your results in a clear and engaging way can diminish the impact of your analysis. Always use visualizations and concise explanations to communicate your insights and recommendations.

7. Advanced EDA Techniques for Machine Learning

Beyond the basic EDA techniques, there are several advanced methods that can provide deeper insights and improve the performance of machine learning models. These techniques often involve more complex statistical analysis and visualization.

7.1. Feature Engineering and Selection

Feature engineering involves creating new features from existing ones to improve the performance of machine learning models. EDA plays a crucial role in identifying potential features and evaluating their usefulness. Techniques include:

Polynomial Features: Creating polynomial combinations of existing features.
Interaction Features: Combining two or more features to capture interaction effects.
Domain-Specific Features: Creating features based on domain knowledge.

Feature selection involves selecting the most relevant features for your model. EDA can help identify redundant or irrelevant features that can be removed to simplify the model and improve its generalization performance. Techniques include:

Univariate Feature Selection: Selecting features based on univariate statistical tests.
Recursive Feature Elimination: Recursively removing features and evaluating model performance.
Feature Importance: Using model-based feature importance scores to select the most important features.

7.2. Dimensionality Reduction

Dimensionality reduction techniques are used to reduce the number of variables in a dataset while preserving its essential information. EDA can help identify appropriate dimensionality reduction techniques and evaluate their effectiveness. Common techniques include:

Principal Component Analysis (PCA): Transforming the data into a new set of uncorrelated variables called principal components.
t-Distributed Stochastic Neighbor Embedding (t-SNE): Reducing the dimensionality of the data while preserving its local structure.
Uniform Manifold Approximation and Projection (UMAP): A general-purpose dimensionality reduction technique that can preserve both local and global structure.

7.3. Time Series Analysis

For time series data, advanced EDA techniques can provide insights into trends, seasonality, and autocorrelation. These techniques include:

Decomposition: Decomposing the time series into its trend, seasonal, and residual components.
Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF): Identifying patterns of autocorrelation in the time series.
Spectral Analysis: Analyzing the frequency components of the time series.

7.4. Spatial Data Analysis

For spatial data, advanced EDA techniques can provide insights into spatial patterns and relationships. These techniques include:

Spatial Autocorrelation: Measuring the degree to which values are clustered or dispersed in space.
Hot Spot Analysis: Identifying clusters of high or low values.
Geostatistics: Modeling spatial variability and making predictions at unsampled locations.

8. Future Trends in Exploratory Data Analysis (EDA)

As data continues to grow in volume and complexity, Exploratory Data Analysis (EDA) is evolving to meet new challenges and opportunities. Several trends are shaping the future of EDA, making it more efficient, automated, and insightful.

8.1. Automated EDA (AutoEDA)

AutoEDA tools automate many of the routine tasks involved in EDA, such as data profiling, visualization, and summary statistics generation. These tools can quickly provide an overview of a dataset, identify potential issues, and suggest further areas of investigation. AutoEDA tools are becoming increasingly popular as they save time and effort, allowing data scientists to focus on more complex analysis and interpretation.

8.2. Interactive Visualization

Interactive visualization tools allow users to explore data in a dynamic and intuitive way. These tools provide features such as zooming, filtering, and drill-down capabilities, enabling users to uncover hidden patterns and relationships. Interactive visualizations are becoming more sophisticated, with features such as linked views, which allow users to explore multiple visualizations simultaneously, and dynamic queries, which allow users to filter and aggregate data on the fly.

8.3. Explainable AI (XAI)

Explainable AI (XAI) techniques are being integrated into EDA to provide insights into the behavior of machine learning models. XAI methods can help users understand which features are most important for making predictions, identify potential biases, and evaluate the fairness of models. These techniques are becoming increasingly important as machine learning models are used in more critical applications where transparency and accountability are essential.

8.4. Real-Time EDA

Real-time EDA involves analyzing streaming data in real-time to detect anomalies, identify trends, and make timely decisions. This requires specialized tools and techniques that can process large volumes of data with low latency. Real-time EDA is becoming increasingly important in industries such as finance, manufacturing, and healthcare, where timely insights can have a significant impact.

8.5. Data Storytelling

Data storytelling involves communicating insights from EDA in a compelling and engaging way. This requires not only technical skills but also strong communication and narrative skills. Data storytelling is becoming increasingly important as data scientists need to communicate their findings to a broader audience, including decision-makers and stakeholders.

9. EDA Checklist

To ensure a thorough and effective Exploratory Data Analysis (EDA), consider the following checklist:

9.1. Data Collection & Understanding

[ ] Gather comprehensive details about your dataset.
[ ] Clarify the objective of your analysis and its significance.
[ ] Document the data source and its potential biases.

9.2. Data Inspection & Cleaning

[ ] Verify the structure and format of your dataset.
[ ] Correct any inconsistencies in the data entries.
[ ] Handle missing values using suitable methods.
[ ] Detect and address any duplicate entries.

9.3. Data Exploration

[ ] Calculate basic statistical measures (mean, median, mode, standard deviation).
[ ] Create frequency distributions and histograms for each variable.
[ ] Develop visualizations like box plots and scatter plots to observe distributions and relationships.

9.4. Relationship Analysis

[ ] Investigate the interactions between variable pairs.
[ ] Employ correlation matrices and heatmaps to pinpoint dependencies.
[ ] Use cross-tabulations for categorical variable insights.

9.5. Advanced Analysis

[ ] Conduct time series analyses for trend detection.
[ ] Perform spatial data analysis using mapping tools.
[ ] Implement dimensionality reduction techniques to simplify the dataset.

9.6. Outlier Handling

[ ] Define criteria for identifying outliers.
[ ] Use IQR or Z-score methods to find outliers.
[ ] Determine whether to remove, adjust, or retain outliers based on their impact.

9.7. Documentation & Communication

[ ] Keep thorough records of all EDA stages and choices.
[ ] Document findings, graphics, and their interpretations.
[ ] Present outcomes through reports, presentations, or interactive dashboards.

9.8. Validation & Iteration

[ ] Ensure the accuracy and consistency of results.
[ ] Validate findings using alternative methods or data subsets.
[ ] Revise EDA steps as needed for deeper insights.

10. Frequently Asked Questions (FAQ) About EDA

Here are some frequently asked questions about Exploratory Data Analysis (EDA) to help you understand its importance and application in machine learning.

What is the primary goal of Exploratory Data Analysis (EDA)?

The primary goal of EDA is to understand the data, uncover patterns, identify anomalies, and test hypotheses. It helps to gain insights into the data structure, relationships between variables, and potential data quality issues.
Why is EDA considered a crucial step in machine learning?

EDA is crucial because it helps to identify relevant features, handle missing data, detect outliers, and validate assumptions before building machine learning models. It ensures that the data is clean, well-understood, and suitable for modeling.
What are the main types of EDA?

The main types of EDA include univariate analysis (examining one variable), bivariate analysis (examining relationships between two variables), and multivariate analysis (examining relationships among multiple variables).
What tools and techniques are commonly used in EDA?

Common EDA tools include Python libraries (Pandas, Matplotlib, Seaborn, Plotly), R packages (ggplot2, dplyr, tidyr), and spreadsheet software (Microsoft Excel, Google Sheets). Techniques include summary statistics, data visualization, correlation analysis, and outlier detection.
How does EDA help in feature engineering?

EDA helps in feature engineering by identifying potential features, understanding their distributions, and evaluating their relationships with the target variable. It guides the creation of new features and the selection of relevant features for the model.
What should you do with missing data during EDA?

During EDA, you should identify the patterns of missing data, determine the reasons for missingness, and decide whether to remove or impute the missing values. Use appropriate imputation methods based on the data’s characteristics and consider the impact of missing data on the analysis.
How can outliers be identified during EDA?

Outliers can be identified using methods like interquartile range (IQR), Z-scores, or domain-specific rules. Visualize the data using box plots or scatter plots to identify data points that significantly differ from the rest of the data.
What are some common mistakes to avoid during EDA?

Common mistakes include jumping to conclusions too quickly, ignoring missing data, misinterpreting correlation, overlooking outliers, neglecting data types, over-reliance on default settings, inadequate documentation, and not communicating findings effectively.
How does EDA contribute to model selection and evaluation?

EDA helps in model selection by providing insights into the data’s characteristics, such as its distribution, relationships between variables, and presence of outliers. It also helps in evaluating model performance by identifying potential biases, validating assumptions, and ensuring that the model generalizes well.
What are some future trends in EDA?

Future trends in EDA include automated EDA (AutoEDA), interactive visualization, explainable AI (XAI), real-time EDA, and data storytelling. These trends aim to make EDA more efficient, insightful, and accessible to a broader audience.

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