{"id":56207,"date":"2024-07-17T12:16:20","date_gmt":"2024-07-17T06:46:20","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=56207"},"modified":"2026-10-09T13:28:11","modified_gmt":"2026-10-09T07:58:11","slug":"exploratory-data-analysis-eda-in-data-science","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/exploratory-data-analysis-eda-in-data-science\/","title":{"rendered":"Exploratory Data Analysis (EDA) in Data Science: Checklist, Steps and Common Mistakes"},"content":{"rendered":"\n<p><strong>Exploratory Data Analysis (EDA) <\/strong>is the process of examining a dataset using statistics and visuals before you build any model. It helps you spot missing values, outliers, and relationships between variables so your machine learning model isn&#8217;t built on shaky data.<\/p>\n\n\n\n<p>Here&#8217;s what EDA in data science typically involves:<\/p>\n\n\n\n<ul>\n<li>Checking data structure with <code>df.info()<\/code> and <code>df.describe()<\/code><\/li>\n\n\n\n<li>Finding and handling missing values<\/li>\n\n\n\n<li>Visualizing distributions with histograms and box plots<\/li>\n\n\n\n<li>Spotting correlations with a heatmap<\/li>\n\n\n\n<li>Detecting outliers before modeling<\/li>\n\n\n\n<li>Studying relationships between variables (bivariate and multivariate analysis)<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>TL;DR Summary<\/strong><\/h2>\n\n\n\n<ul>\n<li>This blog covers Exploratory Data Analysis (EDA) and how it helps you understand data before building a machine learning model.<\/li>\n\n\n\n<li>It guides you through a 10-step EDA checklist, including missing values, duplicates, distributions, outliers, and correlations.<\/li>\n\n\n\n<li>It also explores EDA using the Titanic dataset with practical Python steps and visualizations.<\/li>\n\n\n\n<li>It helps you avoid common EDA mistakes, like ignoring outliers, missing values, and class imbalance.<\/li>\n\n\n\n<li>It also explains why Exploratory Data Analysis (EDA) is important before every machine learning project.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why Exploratory Data Analysis Still Matters in 2026?<\/strong><\/h2>\n\n\n\n<p>Most data science tutorials jump straight to model building. That&#8217;s a mistake.<\/p>\n\n\n\n<p>A model trained on unexamined data will quietly learn from missing values, duplicate rows, and <a href=\"https:\/\/www.guvi.in\/blog\/outliers-in-data-science\/\" data-type=\"link\" data-id=\"https:\/\/www.guvi.in\/blog\/outliers-in-data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">outliers<\/a> you never caught. Exploratory Data Analysis (EDA) is where you catch these problems while they&#8217;re still cheap to fix.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><strong><em>Ready to take your EDA skills beyond the Titanic dataset? Join HCL GUVI&#8217;s Advanced Data Science &amp; Generative AI Course and learn Python, machine learning, deep learning, and Generative AI through live online classes, 10+ real-world projects, and 1:1 doubt sessions. Earn an IITM Pravartak certification, prepare with mock interviews and placement assistance, and start from scratch with no prior coding experience needed. Enroll today and build a career in data science!<\/em><\/strong><\/p>\n\n\n<div class=\"wp-block-guvi-cta-button\" style=\"justify-content:center;\"><a href=\"https:\/\/www.guvi.in\/zen-class\/data-science-course\/?utm_source=blog&#038;utm_medium=hyperlink&#038;utm_campaign=exploratory-data-analysis-eda-in-data-science\" class=\"guvi-btn-enquire\" target=\"_blank\" rel=\"noopener noreferrer\">Enroll in Data Science Course<\/a><\/div>\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>EDA Checklist: 10 Steps Before Every ML Project<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1200\" height=\"628\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/ChatGPT-Image-Jul-6-2026-09_01_10-PM-1200x628.webp\" alt=\"EDA Checklist: 10 Steps Before Every ML Project\" class=\"wp-image-121363\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/ChatGPT-Image-Jul-6-2026-09_01_10-PM-1200x628.webp 1200w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/ChatGPT-Image-Jul-6-2026-09_01_10-PM-300x157.webp 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/ChatGPT-Image-Jul-6-2026-09_01_10-PM-768x402.webp 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/ChatGPT-Image-Jul-6-2026-09_01_10-PM-1536x803.webp 1536w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/ChatGPT-Image-Jul-6-2026-09_01_10-PM-150x78.webp 150w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/ChatGPT-Image-Jul-6-2026-09_01_10-PM.webp 1734w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" title=\"\"><\/figure>\n\n\n\n<p>Run through this before you touch a single model:<\/p>\n\n\n\n<ol>\n<li><strong>Load the dataset and check its shape with <code>df.shape<\/code>:<\/strong> tells you how many rows and columns you have, so you know if the dataset is big enough to model.<\/li>\n\n\n\n<li><strong>Preview rows using <code>df.head()<\/code> and <code>df.tail()<\/code>:<\/strong> catches obvious problems early, like wrong headers, odd values or messy formatting.<\/li>\n\n\n\n<li><strong>Check <\/strong><a href=\"https:\/\/www.guvi.in\/blog\/types-of-data-in-data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">data types<\/a> <strong>with <code>df.info()<\/code>:<\/strong> shows columns stored as the wrong type, like dates or numbers saved as text.<\/li>\n\n\n\n<li><strong>Get summary statistics with <code>df.describe()<\/code> and <code>df['col'].value_counts()<\/code>:<\/strong> <code>describe()<\/code> shows the mean, spread and min\/max of numeric columns, while <code>value_counts()<\/code> checks categorical columns for class imbalance.<\/li>\n\n\n\n<li><strong>Count missing values with <code>df.isnull().sum()<\/code>:<\/strong> shows which columns need imputing or dropping.<\/li>\n\n\n\n<li><strong>Check for duplicate rows with <code>df.duplicated().sum()<\/code>:<\/strong> duplicates can inflate accuracy and leak data between train and test sets.<\/li>\n\n\n\n<li><strong>Plot distributions for numeric columns using <code>plt.hist()<\/code>:<\/strong> shows whether columns are skewed, which decides if they need transforming.<\/li>\n\n\n\n<li><strong>Detect outliers using box plots or the IQR method:<\/strong> extreme values can distort the mean, correlations and the model itself.<\/li>\n\n\n\n<li><strong>Visualize <\/strong><a href=\"https:\/\/www.guvi.in\/blog\/correlation-in-data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">correlations<\/a><strong> using <code>sns.heatmap()<\/code>:<\/strong> shows which features move together and which relate most to the target.<\/li>\n\n\n\n<li><strong>Study relationships between key variables (bivariate analysis):<\/strong> confirms whether a feature really affects the target.<\/li>\n<\/ol>\n\n\n\n<p>Keep this list open in a second tab the first few times. It becomes muscle memory fast.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<div style=\"background-color: #099f4e; border: 3px solid #110053; border-radius: 12px; padding: 18px 22px; color: #ffffff; font-size: 18px; font-family: Montserrat, Helvetica, sans-serif; line-height: 1.6; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15); max-width: 750px;\"><strong style=\"font-size: 22px; color: #ffffff;\">\ud83d\udca1 Did You Know?<\/strong><br \/><br \/>Data scientists spend close to 80% of their project time on data preparation and exploration, not on building the actual model, according to widely cited industry surveys on data science workflows.<\/div>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>EDA Walkthrough Using the Titanic Dataset<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1200\" height=\"630\" src=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/ChatGPT-Image-Jul-6-2026-09_03_41-PM-1200x630.webp\" alt=\"EDA Walkthrough Using the Titanic Dataset\" class=\"wp-image-121364\" srcset=\"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/ChatGPT-Image-Jul-6-2026-09_03_41-PM-1200x630.webp 1200w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/ChatGPT-Image-Jul-6-2026-09_03_41-PM-300x158.webp 300w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/ChatGPT-Image-Jul-6-2026-09_03_41-PM-768x403.webp 768w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/ChatGPT-Image-Jul-6-2026-09_03_41-PM-1536x807.webp 1536w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/ChatGPT-Image-Jul-6-2026-09_03_41-PM-150x79.webp 150w, https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/07\/ChatGPT-Image-Jul-6-2026-09_03_41-PM.webp 1731w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" title=\"\"><\/figure>\n\n\n\n<p>Theory only takes you so far. Here&#8217;s Exploratory Data Analysis applied to the <a href=\"https:\/\/www.kaggle.com\/datasets\/yasserh\/titanic-dataset\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Titanic dataset<\/a>, a common beginner dataset for classification problems.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 1: Load and inspect the data<\/strong><\/h3>\n\n\n\n<p>python<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\n\ndf = pd.read_csv('titanic.csv')\ndf.head()\ndf.info()<\/code><\/pre>\n\n\n\n<p>This tells you column names, data types, and how many non-null values each column has.<\/p>\n\n\n\n<p><strong><em>Code Explanation:<\/em><\/strong><\/p>\n\n\n\n<ul>\n<li><code>import pandas as pd<\/code> loads pandas, the library used to handle table-like data.<\/li>\n\n\n\n<li><code>pd.read_csv('titanic.csv')<\/code> reads the CSV file into a DataFrame called <code>df<\/code>.<\/li>\n\n\n\n<li><code>df.head()<\/code> shows the first 5 rows so you can see what the data looks like.<\/li>\n\n\n\n<li><code>df.info()<\/code> lists every column with its data type and non-null count.<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 2: Summary statistics with df.describe()<\/strong><\/h3>\n\n\n\n<p>python<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>df.describe()<\/code><\/pre>\n\n\n\n<p>For the Titanic dataset, this instantly shows you that the average passenger age is around 29 to 30, fares are heavily right-skewed, and <code>Age<\/code> has missing values that need handling.<\/p>\n\n\n\n<p><strong><em>Code Explanation:<\/em><\/strong><\/p>\n\n\n\n<p><code>df.describe()<\/code> gives the count, mean, standard deviation, min, quartiles and max for every numeric column.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 3: Check missing data<\/strong><\/h3>\n\n\n\n<p>python<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>df.isnull().sum()<\/code><\/pre>\n\n\n\n<p>The <code>Age<\/code> and <code>Cabin<\/code> columns usually show the most missing values in this dataset. This decides whether you impute, drop, or flag them as a separate category.<\/p>\n\n\n\n<p><strong><em>Code Explanation:<\/em><\/strong><\/p>\n\n\n\n<ul>\n<li><code>df.isnull()<\/code> marks each cell as <code>True<\/code> if the value is missing.<\/li>\n\n\n\n<li><code>.sum()<\/code> adds up those <code>True<\/code> values, giving the missing count for each column.<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 4: Visualize distributions with plt.hist()<\/strong><\/h3>\n\n\n\n<p>python<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import matplotlib.pyplot as plt\n\ndf&#91;'Fare'].hist(bins=30)\nplt.title('Fare Distribution')\nplt.xlabel('Fare')\nplt.ylabel('Passengers')\nplt.show()<\/code><\/pre>\n\n\n\n<p>This shows most passengers paid low fares, with a long tail of expensive first-class tickets, a classic sign you&#8217;ll need to handle skew before modeling.<\/p>\n\n\n\n<p><strong><em>Code Explanation:<\/em><\/strong><\/p>\n\n\n\n<ul>\n<li><code>import matplotlib.pyplot as plt<\/code> loads the plotting library (imported once and reused in later steps).<\/li>\n\n\n\n<li><code>df['Fare'].hist(bins=30)<\/code> draws a histogram of the <code>Fare<\/code> column, split into 30 equal-width ranges called bins.<\/li>\n\n\n\n<li><code>plt.title()<\/code>, <code>plt.xlabel()<\/code> and <code>plt.ylabel()<\/code> add the chart title and the axis labels.<\/li>\n\n\n\n<li><code>plt.show()<\/code> displays the chart.<\/li>\n<\/ul>\n\n\n\n<p><strong>Read More: <a href=\"https:\/\/www.guvi.in\/blog\/data-visualization-in-data-science\/\" data-type=\"link\" data-id=\"https:\/\/www.guvi.in\/blog\/data-visualization-in-data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">Data Visualization in Data Science<\/a><\/strong><\/p>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 5: Correlation heatmap with sns.heatmap()<\/strong><\/h3>\n\n\n\n<p>python<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>import seaborn as sns\n\nsns.heatmap(df.corr(numeric_only=True), annot=True, cmap='coolwarm')\nplt.title('Correlation Heatmap')\nplt.show()<\/code><\/pre>\n\n\n\n<p>On Titanic, this usually reveals that <code>Pclass<\/code> and <code>Fare<\/code> are moderately correlated with <code>Survived<\/code>, which is a strong early signal for feature selection.<\/p>\n\n\n\n<p><strong><em>Code Explanation:<\/em><\/strong><\/p>\n\n\n\n<ul>\n<li><code>import seaborn as sns<\/code> loads seaborn, a chart library built on matplotlib.<\/li>\n\n\n\n<li><code>df.corr(numeric_only=True)<\/code> calculates the correlation between every pair of numeric columns and skips text columns.<\/li>\n\n\n\n<li><code>sns.heatmap()<\/code> draws that correlation table as a colored grid.<\/li>\n\n\n\n<li><code>annot=True<\/code> prints the correlation value inside each cell.<\/li>\n\n\n\n<li><code>cmap='coolwarm'<\/code> sets the colors: warm shades for positive and cool shades for negative correlation.<\/li>\n\n\n\n<li><code>plt.title()<\/code> adds the title and <code>plt.show()<\/code> displays the chart.<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 6: Bivariate check<\/strong><\/h3>\n\n\n\n<p>python<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>sns.boxplot(x='Survived', y='Age', data=df)\nplt.show()<\/code><\/pre>\n\n\n\n<p>A quick box plot like this can reveal whether age had any real relationship with survival, or whether it&#8217;s mostly noise.<\/p>\n\n\n\n<p>If you&#8217;re working with a housing price dataset instead, the exact same six steps apply. Swap <code>Survived<\/code> for <code>SalePrice<\/code>, and the heatmap step becomes even more important since housing data usually has many correlated numeric features.<\/p>\n\n\n\n<p><strong><em>Code Explanation:<\/em><\/strong><\/p>\n\n\n\n<ul>\n<li><code>sns.boxplot(x='Survived', y='Age', data=df)<\/code> draws one box of ages for each <code>Survived<\/code> value (0 = did not survive, 1 = survived). <code>data=df<\/code> tells seaborn where the columns come from.<\/li>\n\n\n\n<li>Each box shows the median and the middle 50% of ages, and dots beyond the whiskers are outliers.<\/li>\n\n\n\n<li><code>plt.show()<\/code> displays the plot.<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>G<em>reat models start with great data. HCL GUVI&#8217;s Data Science &amp; Analytics Certification Course teaches you Exploratory Data Analysis (EDA), data gathering, and cleaning, then moves into supervised and unsupervised machine learning and model interpretation with LIME and SHAP. Beginner-friendly, with recorded lessons and a certificate upon completion. Start learning today!<\/em><\/strong><\/p>\n\n\n<div class=\"wp-block-guvi-cta-button\" style=\"justify-content:center;\"><a href=\"https:\/\/www.guvi.in\/courses\/data-science\/data-science-and-analytics\/?utm_source=blog&#038;utm_medium=hyperlink&#038;utm_campaign=exploratory-data-analysis-eda-in-data-science\" class=\"guvi-btn-enquire\" target=\"_blank\" rel=\"noopener noreferrer\">Enroll in Data Science &amp; Analytics Certification Course<\/a><\/div>\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Common EDA Mistakes Data Scientists Make<\/strong><\/h2>\n\n\n\n<ol>\n<li><strong>Skipping the missing value check:<\/strong> Jumping straight to visualization without running <code>df.isnull().sum()<\/code> first often leads to silently biased charts and <a href=\"https:\/\/www.guvi.in\/blog\/data-science-models-types-and-techniques\/\" target=\"_blank\" data-type=\"link\" data-id=\"https:\/\/www.guvi.in\/blog\/data-science-models-types-and-techniques\/\" rel=\"noreferrer noopener\">models<\/a>.<\/li>\n\n\n\n<li><strong>Ignoring outliers:<\/strong> A handful of extreme values can distort your mean, correlation, and even your model&#8217;s decision boundary. Always look before you decide to keep or remove them.<\/li>\n\n\n\n<li><strong>Reading <code>df.describe()<\/code> only for numeric columns:<\/strong> Categorical columns need their own check with <code>df['col'].value_counts()<\/code>. Skipping this hides class imbalance problems.<\/li>\n\n\n\n<li><strong>Treating EDA as a one-time step:<\/strong> New features, joins, or cleaned versions of the data all deserve a fresh round of EDA. Don&#8217;t assume your first pass still applies.<\/li>\n\n\n\n<li><strong>Overplotting without a question:<\/strong> Making twenty charts without asking what you&#8217;re checking for wastes time. Every plot should answer something specific.<\/li>\n<\/ol>\n\n\n\n<p><strong>Also Read: <a href=\"https:\/\/www.guvi.in\/blog\/best-datasets-for-data-science-projects\/\" data-type=\"link\" data-id=\"https:\/\/www.guvi.in\/blog\/best-datasets-for-data-science-projects\/\" target=\"_blank\" rel=\"noreferrer noopener\">Best Datasets for Data Science Projects<\/a><\/strong><\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n\n\n\n<p>Exploratory Data Analysis is not a box to tick before modeling, it&#8217;s the step that decides whether your model is trustworthy. Running through <code>df.describe()<\/code>, missing value checks, histograms, and a correlation heatmap on every new dataset takes minutes but saves hours of debugging later. <\/p>\n\n\n\n<p>Start applying this checklist on a Kaggle dataset like Titanic or Housing Prices, and it will quickly become second nature.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>FAQs<\/strong><\/h2>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1783051482727\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">1. What is Exploratory Data Analysis in data science?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>EDA, or Exploratory Data Analysis, is the process of examining a dataset using statistics and visualizations to understand its structure, quality, and patterns before building a model.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1783051484851\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">2. <strong>Which Python libraries are used for Exploratory Data Analysis?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Pandas for data handling, Matplotlib and Seaborn for visualization, and NumPy for numerical operations are the most commonly used libraries for EDA.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1783051493184\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">3. Is Exploratory Data Analysis necessary before every machine learning project?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes. Skipping EDA often means models are trained on missing values, outliers, or unbalanced classes that go unnoticed until the model underperforms in production.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1783051498908\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">4. What is the difference between EDA and data cleaning?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Data cleaning fixes issues like missing values and duplicates. EDA is a broader process of exploring the data and often uncovers issues that data cleaning then fixes.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1783051504028\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">5. Which dataset is best for practicing EDA as a beginner?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>The Titanic dataset and the Boston or Ames Housing dataset are common for beginners because they&#8217;re small, well documented, and include a mix of numeric and categorical features.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1783051508700\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \">6. How long does it take to learn Exploratory Data Analysis?<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>With consistent practice on two or three real datasets, most beginners get comfortable with core EDA steps like df.describe(), histograms, and correlation heatmaps within two to three weeks.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Exploratory Data Analysis (EDA) is the process of examining a dataset using statistics and visuals before you build any model. It helps you spot missing values, outliers, and relationships between variables so your machine learning model isn&#8217;t built on shaky data. Here&#8217;s what EDA in data science typically involves: TL;DR Summary Why Exploratory Data Analysis [&hellip;]<\/p>\n","protected":false},"author":64,"featured_media":121366,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"views":"32485","authorinfo":{"name":"Abhishek Pati","url":"https:\/\/www.guvi.in\/blog\/author\/abhishek-pati\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2024\/07\/ChatGPT-Image-Jul-6-2026-09_06_09-PM-300x116.webp","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/56207"}],"collection":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/users\/64"}],"replies":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/comments?post=56207"}],"version-history":[{"count":53,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/56207\/revisions"}],"predecessor-version":[{"id":142864,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/56207\/revisions\/142864"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/121366"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=56207"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=56207"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=56207"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}