{"id":145100,"date":"2026-11-02T15:41:59","date_gmt":"2026-11-02T10:11:59","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=145100"},"modified":"2026-11-02T15:42:01","modified_gmt":"2026-11-02T10:12:01","slug":"machine-learning-for-business-analysts","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/machine-learning-for-business-analysts\/","title":{"rendered":"Machine Learning for Business Analysts: Where to Start"},"content":{"rendered":"\n<p>Machine learning is becoming useful for business analysts who need to understand customer behaviour, forecast sales, and identify business opportunities. You do not need to become a machine learning engineer to get started. Focus on understanding the concepts, identifying suitable business problems, and interpreting model results.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>TL;DR<\/strong><\/h2>\n\n\n\n<ul>\n<li>Understand machine learning fundamentals and business applications.<\/li>\n\n\n\n<li>Learn supervised and unsupervised learning.<\/li>\n\n\n\n<li>Strengthen statistics, SQL, and basic Python.<\/li>\n\n\n\n<li>Explore regression, classification, and clustering.<\/li>\n\n\n\n<li>Build projects using real business datasets.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Quick Answer<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td>Business analysts should start with machine learning fundamentals, basic statistics, data preparation, and common algorithms such as regression, classification, and clustering. Practise with small datasets and learn to evaluate predictions before making recommendations. The<a href=\"https:\/\/scikit-learn.org\/stable\/getting_started.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"> scikit-learn getting-started guide<\/a> introduces model training, preprocessing, and evaluation.<\/td><\/tr><\/tbody><\/table><\/figure>\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=cta-button&#038;utm_campaign=machine-learning-for-business-analysts\" class=\"guvi-btn-enquire\" target=\"_blank\" rel=\"noopener noreferrer\">Explore HCL GUVI Data Science Course<svg class=\"guvi-btn-chevron\" style=\"transform:rotate(0deg);\" width=\"16\" height=\"16\" viewBox=\"0 0 24 24\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-hidden=\"true\"><path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M9.39935 18.1007C9.0674 17.7687 9.0674 17.2305 9.39935 16.8986L13.6922 12.6057C13.7508 12.5471 13.7508 12.4521 13.6922 12.3935L9.39935 8.10065C9.0674 7.7687 9.0674 7.23051 9.39935 6.89857C9.7313 6.56662 10.2695 6.56662 10.6014 6.89857L14.8943 11.1915C15.6168 11.9139 15.6168 13.0853 14.8943 13.8078L10.6014 18.1007C10.2695 18.4326 9.7313 18.4326 9.39935 18.1007Z\" fill=\"currentColor\"\/><\/svg><\/a><\/div>\n\n\n<h2 class=\"wp-block-heading\"><strong>Understand Machine Learning Fundamentals<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Learn what machine learning means<\/strong><\/h3>\n\n\n\n<p><a href=\"https:\/\/www.guvi.in\/blog\/introduction-to-machine-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">Machine learning<\/a> enables computers to identify patterns in data and use them to make predictions or group similar observations. Unlike traditional reporting, which primarily describes past performance, machine learning can help estimate future outcomes.<\/p>\n\n\n\n<p>For example, a <a href=\"https:\/\/www.guvi.in\/blog\/a-complete-business-analyst-career-roadmap\/\" target=\"_blank\" rel=\"noreferrer noopener\">business analyst<\/a> might use historical sales data to forecast demand. Another application could identify customers who are more likely to cancel their subscriptions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Explore the main types<\/strong><\/h3>\n\n\n\n<p>Start by understanding the three common machine learning techniques. Each approach serves different analytical purposes.<\/p>\n\n\n\n<ul>\n<li><strong>Supervised learning:<\/strong> Learns from labelled examples to predict outcomes.<\/li>\n\n\n\n<li><strong>Unsupervised learning:<\/strong> Finds patterns in data without predefined labels.<\/li>\n\n\n\n<li><strong>Reinforcement learning:<\/strong> Learns through actions, feedback, and rewards.<\/li>\n<\/ul>\n\n\n\n<p>To understand these approaches in more detail, explore this guide to<a href=\"https:\/\/www.guvi.in\/hub\/machine-learning-tutorial\/supervised-vs-unsupervised-vs-reinforcement-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\"> supervised, unsupervised, and reinforcement learning<\/a>.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Identify Business Problems Worth Solving<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Connect machine learning to business goals<\/strong><\/h3>\n\n\n\n<p>A machine learning <a href=\"https:\/\/www.guvi.in\/blog\/best-machine-learning-project-ideas\/\" target=\"_blank\" rel=\"noreferrer noopener\">project<\/a> should begin with a business question rather than an algorithm. Define the problem, identify the available data, and determine how the result could improve a decision.<\/p>\n\n\n\n<p>Common business applications include:<\/p>\n\n\n\n<ul>\n<li><a href=\"https:\/\/www.guvi.in\/hub\/customer-churn-prediction-project-using-classification-techniques\/introduction\/\" target=\"_blank\" rel=\"noreferrer noopener\">Predicting customer churn<\/a>.<\/li>\n\n\n\n<li>Forecasting product demand.<\/li>\n\n\n\n<li><a href=\"https:\/\/www.guvi.in\/blog\/product-for-multiple-customer-segmentation\/\" target=\"_blank\" rel=\"noreferrer noopener\">Segmenting customers<\/a> by behaviour.<\/li>\n\n\n\n<li>Identifying <a href=\"https:\/\/www.guvi.in\/blog\/ai-in-fraud-detection\/\" target=\"_blank\" rel=\"noreferrer noopener\">fraud detection<\/a> in transactions.<\/li>\n\n\n\n<li>Estimating future revenue.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Define measurable outcomes<\/strong><\/h3>\n\n\n\n<p>Choose a metric that connects the model to the business objective. For example, a customer retention project could measure how accurately the model identifies customers who are likely to leave.<\/p>\n\n\n\n<p>Also consider the cost of incorrect predictions and the actions the business can take. A model is useful only when its results support a meaningful decision.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Strengthen Your Statistics and Data Skills<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Learn essential statistics<\/strong><\/h3>\n\n\n\n<p><a href=\"https:\/\/www.guvi.in\/blog\/statistics-fundamentals-for-machine-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">Statistics<\/a> helps you understand patterns, uncertainty, and relationships within datasets. Start with descriptive statistics, probability, distributions, correlation, and regression.<\/p>\n\n\n\n<p>You should also understand sampling, hypothesis testing, and confidence intervals. These concepts help you evaluate evidence and avoid drawing unsupported conclusions from model outputs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Improve your data handling<\/strong><\/h3>\n\n\n\n<p>Business analysts frequently work with spreadsheets, databases, dashboards, and reporting tools. Strong data preparation skills make machine learning projects more reliable.<\/p>\n\n\n\n<p>Prioritise these skills:<\/p>\n\n\n\n<ul>\n<li><a href=\"https:\/\/www.guvi.in\/blog\/work-smarter-with-microsoft-excel\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Excel<\/strong><\/a><strong>:<\/strong> Data cleaning, formulas, and summaries.<\/li>\n\n\n\n<li><a href=\"https:\/\/www.guvi.in\/hub\/dbms-and-sql-tutorial\/sql-intoduction\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>SQL<\/strong><\/a><strong>:<\/strong> Filtering, joining, and aggregating data.<\/li>\n\n\n\n<li><a href=\"https:\/\/www.guvi.in\/blog\/data-visualization-in-data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Data visualisation<\/strong><\/a><strong>:<\/strong> Communicating patterns and findings.<\/li>\n\n\n\n<li><a href=\"https:\/\/www.guvi.in\/hub\/machine-learning-tutorial\/python-basics-for-machine-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Python<\/strong><\/a><strong>:<\/strong> Automating analysis and building models.<\/li>\n<\/ul>\n\n\n\n<p>You do not need to master every tool immediately. Begin with the tools you already use and gradually add Python when you are ready to build predictive models.<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube\"><div class=\"wp-block-embed__wrapper\">\n<div class=\"container-lazyload preview-lazyload container-youtube js-lazyload--not-loaded\"><a href=\"https:\/\/www.youtube.com\/watch?v=iXU7R9ICJno\" class=\"lazy-load-youtube preview-lazyload preview-youtube\" data-video-title=\"Business Analyst Interview Prep - What Actually Helped Me | HCL GUVI\" title=\"Play video &quot;Business Analyst Interview Prep - What Actually Helped Me | HCL GUVI&quot;\" target=\"_blank\" rel=\"noopener\">https:\/\/www.youtube.com\/watch?v=iXU7R9ICJno<\/a><noscript>Video can&#8217;t be loaded because JavaScript is disabled: <a href=\"https:\/\/www.youtube.com\/watch?v=iXU7R9ICJno\" title=\"Business Analyst Interview Prep - What Actually Helped Me | HCL GUVI\" target=\"_blank\" rel=\"noopener\">Business Analyst Interview Prep &#8211; What Actually Helped Me | HCL GUVI (https:\/\/www.youtube.com\/watch?v=iXU7R9ICJno)<\/a><\/noscript><\/div>\n<\/div><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Learn the Most Useful Algorithms First<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Start with regression and classification<\/strong><\/h3>\n\n\n\n<p><a href=\"https:\/\/www.guvi.in\/blog\/regression-in-machine-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">Regression<\/a> predicts numerical values, such as revenue, demand, or delivery time. Classification predicts categories, such as whether a customer will churn or whether a transaction may be fraudulent.<\/p>\n\n\n\n<p>These methods are used in many business forecasting and decision-making tasks. Learn their purpose, assumptions, limitations, and evaluation metrics before attempting advanced algorithms.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Explore clustering<\/strong><\/h3>\n\n\n\n<p><a href=\"https:\/\/www.guvi.in\/blog\/what-is-clustering-in-machine-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">Clustering<\/a> groups observations based on similarities in their characteristics. Businesses can use it to identify customer segments, purchasing patterns, or groups of products with similar behaviour.<\/p>\n\n\n\n<p>Start by understanding the popular <a href=\"https:\/\/www.guvi.in\/blog\/k-means-clustering-algorithm-machine-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">K-means algorithm<\/a> and how its clusters can be interpreted. Remember that the resulting groups need business validation before being used to guide marketing or operational decisions.<\/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=cta-button&#038;utm_campaign=machine-learning-for-business-analysts\" class=\"guvi-btn-enquire\" target=\"_blank\" rel=\"noopener noreferrer\">Explore HCL GUVI Data Science Course<svg class=\"guvi-btn-chevron\" style=\"transform:rotate(0deg);\" width=\"16\" height=\"16\" viewBox=\"0 0 24 24\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" aria-hidden=\"true\"><path fill-rule=\"evenodd\" clip-rule=\"evenodd\" d=\"M9.39935 18.1007C9.0674 17.7687 9.0674 17.2305 9.39935 16.8986L13.6922 12.6057C13.7508 12.5471 13.7508 12.4521 13.6922 12.3935L9.39935 8.10065C9.0674 7.7687 9.0674 7.23051 9.39935 6.89857C9.7313 6.56662 10.2695 6.56662 10.6014 6.89857L14.8943 11.1915C15.6168 11.9139 15.6168 13.0853 14.8943 13.8078L10.6014 18.1007C10.2695 18.4326 9.7313 18.4326 9.39935 18.1007Z\" fill=\"currentColor\"\/><\/svg><\/a><\/div>\n\n\n<h2 class=\"wp-block-heading\"><strong>Learn Python for Machine Learning<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Focus on practical fundamentals<\/strong><\/h3>\n\n\n\n<p>Python is widely used for data analysis and machine learning because its libraries simplify common tasks. Start with variables, lists, dictionaries, <a href=\"https:\/\/www.guvi.in\/blog\/what-are-python-loops\/\" target=\"_blank\" rel=\"noreferrer noopener\">loops<\/a>, <a href=\"https:\/\/www.guvi.in\/hub\/python\/functions-in-python\/\" target=\"_blank\" rel=\"noreferrer noopener\">functions<\/a>, and importing libraries.<\/p>\n\n\n\n<p>You should also learn the basics of pandas and NumPy for handling data. These skills provide a foundation for preparing datasets and experimenting with machine learning models.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Explore essential libraries<\/strong><\/h3>\n\n\n\n<p>Several <a href=\"https:\/\/www.guvi.in\/blog\/useful-python-libraries-tools-for-data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">Python libraries<\/a> support different stages of a machine learning workflow. Learn their purposes before attempting complicated implementations.<\/p>\n\n\n\n<ul>\n<li><strong>pandas:<\/strong> Manipulates tabular datasets.<\/li>\n\n\n\n<li><strong>NumPy:<\/strong> Supports numerical operations.<\/li>\n\n\n\n<li><strong>Matplotlib:<\/strong> Creates data visualisations.<\/li>\n\n\n\n<li><strong>scikit-learn:<\/strong> Provides algorithms and evaluation tools.<\/li>\n<\/ul>\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;\"> \n  <strong style=\"font-size: 22px; color: #FFFFFF;\">\ud83d\udca1 Did You Know?<\/strong> \n  <br \/><br \/> \nMachine learning models can produce accurate predictions without establishing why an outcome occurs. Business analysts must interpret results carefully and consider whether the findings support the decisions being proposed.\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Understand Model Evaluation<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Learn how to measure performance<\/strong><\/h3>\n\n\n\n<p>Evaluate a model on data it hasn&#8217;t seen during training. This helps you estimate how well the model may perform on new observations.<\/p>\n\n\n\n<p>Common evaluation measures include accuracy, <a href=\"https:\/\/www.guvi.in\/hub\/fake-review-detection-system-using-machine-learning\/calculating-precision-recall-and-f1-score\/\" target=\"_blank\" rel=\"noreferrer noopener\">precision, recall, F1-score,<\/a> and mean absolute error. The appropriate metric depends on whether you are predicting categories or numerical values.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Recognise model limitations<\/strong><\/h3>\n\n\n\n<p>A model can perform well on training data but struggle with unfamiliar examples. This problem, known as overfitting, makes reliable evaluation essential.<\/p>\n\n\n\n<p>Check for missing values, biased samples, data leakage, and changing business conditions. Explain these limitations when presenting recommendations to stakeholders.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Build Practical Projects<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Start with manageable datasets<\/strong><\/h3>\n\n\n\n<p>Choose a project that connects machine learning to a clear business question. Public datasets allow you to practise without needing access to confidential company information.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Communicate your findings<\/strong><\/h3>\n\n\n\n<p>A successful project should explain the business problem, <a href=\"https:\/\/www.guvi.in\/blog\/what-is-data-preparation-processes-and-tools\/\" target=\"_blank\" rel=\"noreferrer noopener\">data preparation<\/a>, selected method, evaluation results, and recommendations. Include visualisations that help non-technical stakeholders understand the findings.<\/p>\n\n\n\n<p>Explain what the model can and cannot establish. This demonstrates business judgement alongside technical knowledge.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n\n\n\n<p>Business analysts can begin learning machine learning without mastering advanced programming immediately. Start with business problems, statistics, data preparation, common algorithms, and basic Python.<\/p>\n\n\n\n<p>Apply these skills through practical projects and explain the results in business terms. The goal is not simply to build models, but to use evidence to make better decisions.<\/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-1793263321199\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>1. Can business analysts learn machine learning without coding experience?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes, beginners can start with machine learning concepts and business applications before learning Python. Basic programming becomes important when building and evaluating models independently.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793263327838\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>2. Which machine learning algorithm should beginners learn first?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Start with linear regression and logistic regression, followed by decision trees and clustering. Choose algorithms based on the business problem rather than popularity alone.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793263343329\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>3. Is Python necessary for business analysts learning machine learning?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Python is useful for building models, preparing datasets, and automating analytical tasks. However, you can begin by understanding concepts and exploring accessible analytical tools.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793263353096\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>4. How is machine learning different from traditional business analytics?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Traditional analytics often summarises historical performance and explains observed trends. Machine learning can additionally identify patterns and predict outcomes from historical data.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793263460630\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>5. What statistics should business analysts learn?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Focus on descriptive statistics, probability, distributions, correlation, regression, and hypothesis testing. These foundations help you interpret model performance and understand uncertainty.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793263475944\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>6. Which projects are suitable for beginners?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Customer churn prediction, sales forecasting, and customer segmentation are practical starting points. Each project allows you to connect data analysis with a recognisable business objective.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1793263499989\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>7. How long does it take to learn machine learning basics?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>The timeline depends on your existing analytical and programming skills and the time you practise each week. With consistent study and hands-on projects, you can gradually build a working foundation over several weeks or months.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Machine learning is becoming useful for business analysts who need to understand customer behaviour, forecast sales, and identify business opportunities. You do not need to become a machine learning engineer to get started. Focus on understanding the concepts, identifying suitable business problems, and interpreting model results. TL;DR Quick Answer Business analysts should start with machine [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":145103,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[745,13,16],"tags":[],"views":"19","authorinfo":{"name":"HCL GUVI","url":"https:\/\/www.guvi.in\/blog\/author\/guvipr\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/10\/Machine-Learning-for-Business-Analysts-Where-to-Start-300x116.webp","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/145100"}],"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\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/comments?post=145100"}],"version-history":[{"count":5,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/145100\/revisions"}],"predecessor-version":[{"id":145497,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/145100\/revisions\/145497"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/145103"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=145100"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=145100"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=145100"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}