If you’re eager to master the art of data cleaning and unlock the full potential of your machine learning models, Scaler’s Machine Learning Course<\/strong><\/a> delves into the intricacies of data preprocessing, equipping you with the skills and knowledge to ensure your data is pristine and ready for analysis.<\/p>\n\n\nScaler Carousel<\/title>\n\n<!-- Plus Jakarta Sans Font -->\n<link href=\"https:\/\/fonts.googleapis.com\/css2?family=Plus+Jakarta+Sans:wght@400;500;600;700&display=swap\" rel=\"stylesheet\">\n<!-- Swiper CSS -->\n<link rel=\"stylesheet\" href=\"https:\/\/cdn.jsdelivr.net\/npm\/swiper@11\/swiper-bundle.min.css\">\n\n<style>\n.scaler-carousel {\n font-family: 'Plus Jakarta Sans', sans-serif;\n max-width: 900px;\n margin: 24px auto;\n padding: 0;\n box-sizing: border-box;\n}\n.scaler-carousel *,\n.scaler-carousel *::before,\n.scaler-carousel *::after {\n box-sizing: border-box;\n}\n\n\/* Header *\/\n.scaler-header {\n text-align: center;\n margin-bottom: 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background: #255aad;\n}\n\n\/* Card *\/\n.scaler-card {\n display: flex;\n flex-direction: column;\n gap: 14px;\n width: 100%;\n background: #ffffff;\n border: 1px solid #e5e7eb;\n border-radius: 16px;\n padding: 24px;\n box-shadow: 0 2px 8px rgba(0, 0, 0, 0.06);\n transition: box-shadow 0.2s ease;\n}\n.scaler-card:hover {\n box-shadow: 0 6px 16px rgba(0, 0, 0, 0.10);\n}\n\n\/* NSDC badge *\/\n.scaler-badge {\n display: inline-flex;\n align-items: center;\n align-self: flex-start;\n background: #f3f4f6;\n color: #111827;\n font-size: 12px;\n font-weight: 600;\n padding: 4px 10px;\n border-radius: 20px;\n white-space: nowrap;\n letter-spacing: 0.1px;\n}\n\n\/* Top: icon + title + description *\/\n.scaler-card-top {\n display: flex;\n align-items: flex-start;\n gap: 12px;\n min-height: 100px;\n}\n.scaler-icon-wrap {\n display: flex;\n align-items: center;\n justify-content: center;\n width: 64px;\n height: 64px;\n border-radius: 12px;\n background: #f3f4f6;\n color: #374151;\n flex-shrink: 0;\n}\n.scaler-icon-wrap svg {\n width: 26px;\n height: 26px;\n}\n.scaler-card-top-content {\n flex: 1;\n min-width: 0;\n display: flex;\n flex-direction: column;\n gap: 5px;\n}\n.scaler-title {\n font-size: 16px;\n font-weight: 700;\n color: #111827;\n margin: 0;\n line-height: 1.35;\n}\n.scaler-desc {\n font-size: 13px;\n color: #6b7280;\n margin: 0;\n line-height: 1.45;\n}\n\n\/* Stats row *\/\n.scaler-stats {\n display: flex;\n border-top: 1px solid #e5e7eb;\n border-bottom: 1px solid #e5e7eb;\n padding: 12px 0;\n}\n.scaler-stat {\n display: flex;\n flex-direction: column;\n align-items: center;\n gap: 2px;\n flex: 1;\n min-width: 0;\n text-align: center;\n}\n.scaler-stat:not(:last-child) {\n border-right: 1px solid #d1d5db;\n}\n.scaler-stat-value {\n font-size: 14px;\n font-weight: 700;\n color: #111827;\n white-space: nowrap;\n}\n.scaler-stat-label {\n font-size: 12px;\n color: #9ca3af;\n}\n\n\/* Company logos *\/\n.scaler-logos {\n display: flex;\n align-items: center;\n gap: 12px;\n flex-wrap: nowrap;\n}\n.scaler-company-logo {\n height: 20px !important;\n width: auto !important;\n max-width: 70px;\n object-fit: contain;\n display: inline-block;\n}\n.scaler-logo-more {\n font-size: 12px;\n color: #9ca3af;\n white-space: nowrap;\n}\n.scaler-logo-strip {\n display: block;\n width: 100% !important;\n max-width: 100%;\n min-width: 0;\n height: auto !important;\n object-fit: contain;\n}\n\n\/* CTA *\/\n.scaler-card-btns {\n margin-top: auto;\n}\n.scaler-card-btns button {\n display: flex;\n align-items: center;\n justify-content: center;\n gap: 8px;\n width: 100%;\n padding: 12px 16px;\n border: none;\n border-radius: 10px;\n background-color: #255aad;\n color: #ffffff;\n font-family: 'Plus Jakarta Sans', sans-serif;\n font-size: 14px;\n font-weight: 600;\n cursor: pointer;\n text-decoration: none;\n transition: opacity 0.15s ease;\n}\n.scaler-card-btns button:hover,\n.scaler-card-btns button:focus {\n opacity: 0.88;\n color: #ffffff;\n}\n.scaler-card-btns button svg {\n flex-shrink: 0;\n}\n\n@media (max-width: 1000px) {\n .scaler-carousel { max-width: 96vw; }\n}\n<\/style>\n\n\n<div class=\"scaler-carousel\">\n <div class=\"scaler-header\">\n <h2 class=\"scaler-header-title\"><span class=\"ez-toc-section\" id=\"transform-your-career\"><\/span>Transform Your Career<span class=\"ez-toc-section-end\"><\/span><\/h2>\n <p class=\"scaler-header-subtitle\">Choose from our industry-leading programs designed for career success<\/p>\n <\/div>\n\n <div class=\"swiper scaler-swiper\">\n <div class=\"swiper-wrapper\">\n\n <!-- CARD 1 -->\n <div class=\"swiper-slide\">\n <div class=\"scaler-card\">\n <div class=\"scaler-badge\">NSDC Certified<\/div>\n <div class=\"scaler-card-top\">\n <div class=\"scaler-icon-wrap\">\n <svg width=\"26\" height=\"26\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><polyline points=\"16 18 22 12 16 6\"\/><polyline points=\"8 6 2 12 8 18\"\/><\/svg>\n <\/div>\n <div class=\"scaler-card-top-content\">\n <h3 class=\"scaler-title\">Modern Software and AI Engineering Program<\/h3>\n <p class=\"scaler-desc\">Master full-stack development with AI integration<\/p>\n <\/div>\n <\/div>\n <div class=\"scaler-stats\">\n <div class=\"scaler-stat\"><span class=\"scaler-stat-value\">12 Months<\/span><span class=\"scaler-stat-label\">Duration<\/span><\/div>\n <div class=\"scaler-stat\"><span class=\"scaler-stat-value\">AI-Led<\/span><span class=\"scaler-stat-label\">Curriculum<\/span><\/div>\n <div class=\"scaler-stat\"><span class=\"scaler-stat-value\">Career Support<\/span><span class=\"scaler-stat-label\">Support<\/span><\/div>\n <\/div>\n <div class=\"scaler-logos\">\n <img decoding=\"async\" class=\"scaler-company-logo\" src=\"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/2\/2f\/Google_2015_logo.svg\" alt=\"Google\" loading=\"lazy\">\n <img decoding=\"async\" class=\"scaler-company-logo\" src=\"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/a\/a9\/Amazon_logo.svg\" alt=\"Amazon\" loading=\"lazy\">\n <img decoding=\"async\" class=\"scaler-company-logo\" src=\"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/4\/42\/Paytm_logo.png\" alt=\"Paytm\" loading=\"lazy\">\n <span class=\"scaler-logo-more\">+1000 more<\/span>\n <\/div>\n <div class=\"scaler-card-btns\">\n <button onclick=\"window.open('https:\/\/www.scaler.com\/academy\/', '_blank')\">\n Go to Program\n <svg width=\"14\" height=\"14\" viewBox=\"0 0 16 16\" fill=\"none\" aria-hidden=\"true\"><path d=\"M3 8H13M13 8L9 4M13 8L9 12\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg>\n <\/button>\n <\/div>\n <\/div>\n <\/div>\n\n <!-- CARD 2 -->\n <div class=\"swiper-slide\">\n <div class=\"scaler-card\">\n <div class=\"scaler-badge\">NSDC Certified<\/div>\n <div class=\"scaler-card-top\">\n <div class=\"scaler-icon-wrap\">\n <svg width=\"26\" height=\"26\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><ellipse cx=\"12\" cy=\"5\" rx=\"9\" ry=\"3\"\/><path d=\"M3 5v14c0 1.66 4.03 3 9 3s9-1.34 9-3V5\"\/><path d=\"M3 12c0 1.66 4.03 3 9 3s9-1.34 9-3\"\/><\/svg>\n <\/div>\n <div class=\"scaler-card-top-content\">\n <h3 class=\"scaler-title\">Modern Data Science and ML with specialisation in AI<\/h3>\n <p class=\"scaler-desc\">Advanced data science techniques with AI specialization<\/p>\n <\/div>\n <\/div>\n <div class=\"scaler-stats\">\n <div class=\"scaler-stat\"><span class=\"scaler-stat-value\">12 Months<\/span><span class=\"scaler-stat-label\">Duration<\/span><\/div>\n <div class=\"scaler-stat\"><span class=\"scaler-stat-value\">AI-Led<\/span><span class=\"scaler-stat-label\">Curriculum<\/span><\/div>\n <div class=\"scaler-stat\"><span class=\"scaler-stat-value\">Career Support<\/span><span class=\"scaler-stat-label\">Support<\/span><\/div>\n <\/div>\n <div class=\"scaler-logos\">\n <img decoding=\"async\" class=\"scaler-company-logo\" src=\"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/2\/2f\/Google_2015_logo.svg\" alt=\"Google\" loading=\"lazy\">\n <img decoding=\"async\" class=\"scaler-company-logo\" src=\"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/a\/a9\/Amazon_logo.svg\" alt=\"Amazon\" loading=\"lazy\">\n <img decoding=\"async\" class=\"scaler-company-logo\" src=\"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/4\/42\/Paytm_logo.png\" alt=\"Paytm\" loading=\"lazy\">\n <span class=\"scaler-logo-more\">+1000 more<\/span>\n <\/div>\n <div class=\"scaler-card-btns\">\n <button onclick=\"window.open('https:\/\/www.scaler.com\/data-science-course\/', '_blank')\">\n Go to Program\n <svg width=\"14\" height=\"14\" viewBox=\"0 0 16 16\" fill=\"none\" aria-hidden=\"true\"><path d=\"M3 8H13M13 8L9 4M13 8L9 12\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg>\n <\/button>\n <\/div>\n <\/div>\n <\/div>\n\n <!-- CARD 3 -->\n <div class=\"swiper-slide\">\n <div class=\"scaler-card\">\n <div class=\"scaler-badge\">NSDC Certified<\/div>\n <div class=\"scaler-card-top\">\n <div class=\"scaler-icon-wrap\">\n <svg width=\"26\" height=\"26\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M12 2L13.5 8.5L20 10L13.5 11.5L12 18L10.5 11.5L4 10L10.5 8.5L12 2Z\"\/><\/svg>\n <\/div>\n <div class=\"scaler-card-top-content\">\n <h3 class=\"scaler-title\">Advanced AIML with Specialisation in Agentic AI<\/h3>\n <p class=\"scaler-desc\">Deep dive into AIML with focus on Agentic systems<\/p>\n <\/div>\n <\/div>\n <div class=\"scaler-stats\">\n <div class=\"scaler-stat\"><span class=\"scaler-stat-value\">12 Months<\/span><span class=\"scaler-stat-label\">Duration<\/span><\/div>\n <div class=\"scaler-stat\"><span class=\"scaler-stat-value\">AI-Led<\/span><span class=\"scaler-stat-label\">Curriculum<\/span><\/div>\n <div class=\"scaler-stat\"><span class=\"scaler-stat-value\">Career Support<\/span><span class=\"scaler-stat-label\">Support<\/span><\/div>\n <\/div>\n <div class=\"scaler-logos\">\n <img decoding=\"async\" class=\"scaler-company-logo\" src=\"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/2\/2f\/Google_2015_logo.svg\" alt=\"Google\" loading=\"lazy\">\n <img decoding=\"async\" class=\"scaler-company-logo\" src=\"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/a\/a9\/Amazon_logo.svg\" alt=\"Amazon\" loading=\"lazy\">\n <img decoding=\"async\" class=\"scaler-company-logo\" src=\"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/4\/42\/Paytm_logo.png\" alt=\"Paytm\" loading=\"lazy\">\n <span class=\"scaler-logo-more\">+1000 more<\/span>\n <\/div>\n <div class=\"scaler-card-btns\">\n <button onclick=\"window.open('https:\/\/www.scaler.com\/ai-machine-learning-course\/', '_blank')\">\n Go to Program\n <svg width=\"14\" height=\"14\" viewBox=\"0 0 16 16\" fill=\"none\" aria-hidden=\"true\"><path d=\"M3 8H13M13 8L9 4M13 8L9 12\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg>\n <\/button>\n <\/div>\n <\/div>\n <\/div>\n\n <!-- CARD 4 -->\n <div class=\"swiper-slide\">\n <div class=\"scaler-card\">\n <div class=\"scaler-badge\">NSDC Certified<\/div>\n <div class=\"scaler-card-top\">\n <div class=\"scaler-icon-wrap\">\n <svg width=\"26\" height=\"26\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\">\n <path d=\"M17.5 19H9a7 7 0 1 1 6.71-9h1.79a4.5 4.5 0 1 1 0 9Z\" \/>\n <path d=\"M9.2 15.2a3 3 0 1 0 4.24-4.24\" \/>\n <\/svg>\n <\/div>\n <div class=\"scaler-card-top-content\">\n <h3 class=\"scaler-title\">DevOps, Cloud & AI Platform Engineering<\/h3>\n <p class=\"scaler-desc\">Build and manage AI-powered cloud infrastructure<\/p>\n <\/div>\n <\/div>\n <div class=\"scaler-stats\">\n <div class=\"scaler-stat\"><span class=\"scaler-stat-value\">12 Months<\/span><span class=\"scaler-stat-label\">Duration<\/span><\/div>\n <div class=\"scaler-stat\"><span class=\"scaler-stat-value\">AI-Led<\/span><span class=\"scaler-stat-label\">Curriculum<\/span><\/div>\n <div class=\"scaler-stat\"><span class=\"scaler-stat-value\">Career Support<\/span><span class=\"scaler-stat-label\">Support<\/span><\/div>\n <\/div>\n <div class=\"scaler-logos\">\n <img decoding=\"async\" class=\"scaler-company-logo\" src=\"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/2\/2f\/Google_2015_logo.svg\" alt=\"Google\" loading=\"lazy\">\n <img decoding=\"async\" class=\"scaler-company-logo\" src=\"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/a\/a9\/Amazon_logo.svg\" alt=\"Amazon\" loading=\"lazy\">\n <img decoding=\"async\" class=\"scaler-company-logo\" src=\"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/4\/42\/Paytm_logo.png\" alt=\"Paytm\" loading=\"lazy\">\n <span class=\"scaler-logo-more\">+1000 more<\/span>\n <\/div>\n <div class=\"scaler-card-btns\">\n <button onclick=\"window.open('https:\/\/www.scaler.com\/devops-course\/', '_blank')\">\n Go to Program\n <svg width=\"14\" height=\"14\" viewBox=\"0 0 16 16\" fill=\"none\" aria-hidden=\"true\"><path d=\"M3 8H13M13 8L9 4M13 8L9 12\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg>\n <\/button>\n <\/div>\n <\/div>\n <\/div>\n\n <!-- CARD 5 -->\n <div class=\"swiper-slide\">\n <div class=\"scaler-card\">\n <div class=\"scaler-badge\">NSDC Certified<\/div>\n <div class=\"scaler-card-top\">\n <div class=\"scaler-icon-wrap\">\n <svg width=\"26\" height=\"26\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\">\n <line x1=\"3\" y1=\"22\" x2=\"21\" y2=\"22\" \/>\n <line x1=\"6\" y1=\"18\" x2=\"6\" y2=\"11\" \/>\n <line x1=\"10\" y1=\"18\" x2=\"10\" y2=\"11\" \/>\n <line x1=\"14\" y1=\"18\" x2=\"14\" y2=\"11\" \/>\n <line x1=\"18\" y1=\"18\" x2=\"18\" y2=\"11\" \/>\n <polygon points=\"12 2 20 7 4 7\" \/>\n <\/svg>\n <\/div>\n <div class=\"scaler-card-top-content\">\n <h3 class=\"scaler-title\">AI Engineering Advanced Certification by IIT-Roorkee<\/h3>\n <p class=\"scaler-desc\">Premier AI engineering certification from IIT-Roorkee<\/p>\n <\/div>\n <\/div>\n <div class=\"scaler-stats\">\n <div class=\"scaler-stat\"><span class=\"scaler-stat-value\">3 Months<\/span><span class=\"scaler-stat-label\">Duration<\/span><\/div>\n <div class=\"scaler-stat\"><span class=\"scaler-stat-value\">AI-Led<\/span><span class=\"scaler-stat-label\">Curriculum<\/span><\/div>\n <div class=\"scaler-stat\"><span class=\"scaler-stat-value\">Career Support<\/span><span class=\"scaler-stat-label\">Support<\/span><\/div>\n <\/div>\n <div class=\"scaler-logos\">\n <img decoding=\"async\" class=\"scaler-logo-strip\" src=\"https:\/\/scaler-blog-prod-wp-content.s3.ap-south-1.amazonaws.com\/wp-content\/uploads\/2026\/07\/08135139\/iitr-icons-strip.png\" alt=\"Program highlights\" loading=\"lazy\">\n <\/div>\n <div class=\"scaler-card-btns\">\n <button onclick=\"window.open('https:\/\/www.scaler.com\/iit-roorkee-advanced-ai-engineering-course\/', '_blank')\">\n Go to Program\n <svg width=\"14\" height=\"14\" viewBox=\"0 0 16 16\" fill=\"none\" aria-hidden=\"true\"><path d=\"M3 8H13M13 8L9 4M13 8L9 12\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg>\n <\/button>\n <\/div>\n <\/div>\n <\/div>\n\n <!-- Continue for remaining cards (6\u20138) using same pattern -->\n\n <\/div>\n <div class=\"swiper-button-next\"><\/div>\n <div class=\"swiper-button-prev\"><\/div>\n <div class=\"swiper-pagination\"><\/div>\n <\/div>\n<\/div>\n\n<!-- Swiper.js -->\n<script src=\"https:\/\/cdn.jsdelivr.net\/npm\/swiper@11\/swiper-bundle.min.js\"><\/script>\n<script>\ndocument.addEventListener(\"DOMContentLoaded\", function () {\n const pathParts = window.location.pathname.split(\"\/\").filter(Boolean);\n const slug = pathParts[pathParts.length - 1] || \"\";\n const readableSlug = encodeURIComponent(slug);\n const buttons = document.querySelectorAll(\".scaler-card-btns button\");\n\n buttons.forEach((btn) => {\n const baseURL = btn.getAttribute(\"onclick\").match(\/'(.*?)'\/)[1];\n if (!baseURL.includes(\"utm_source=\")) {\n const separator = baseURL.includes(\"?\") ? \"&\" : \"?\";\n const updatedURL = `${baseURL}${separator}utm_source=blog&utm_medium=program_carousel&utm_content=${readableSlug}`;\n btn.setAttribute(\"onclick\", `window.open('${updatedURL}', '_blank')`);\n }\n });\n\n new Swiper(\".scaler-swiper\", {\n slidesPerView: 1,\n spaceBetween: 28,\n grabCursor: true,\n navigation: { nextEl: \".swiper-button-next\", prevEl: \".swiper-button-prev\" },\n pagination: { el: \".swiper-pagination\", dynamicBullets: true, clickable: true },\n breakpoints: { 1000: { slidesPerView: 2 } },\n keyboard: { enabled: true },\n });\n});\n<\/script>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"what-is-data-cleaning\"><\/span><strong>What is Data Cleaning?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Data cleaning is the process of identifying and rectifying errors, inconsistencies, and inaccuracies in a dataset. This includes a broad range of activities, including dealing with outliers, handling missing values, fixing typos, eliminating duplicates, and standardizing formats. The goal of data cleaning is to improve the quality, reliability, and usability of data for analysis and machine learning tasks.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Although they are frequently used synonymously, data preprocessing and data cleaning are two different but related ideas:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Data Cleaning:<\/strong> Primarily focuses on fixing or removing incorrect, corrupted, or irrelevant data points. It addresses problems like missing values, duplicate records, and formatting errors that come up during data entry or collection.<\/li>\n\n\n\n<li><strong>Data Preprocessing:<\/strong> This broader term encompasses both data cleaning and additional transformations that prepare the data for analysis. It involves work such as normalization, feature engineering, encoding categorical variables, and feature scaling.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">In essence, data cleaning is a subset of data preprocessing. Data preprocessing includes a broader range of methods to change the data into a format appropriate for machine learning algorithms, whereas data cleaning concentrates on correcting errors and guaranteeing data integrity.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"why-is-data-cleaning-important\"><\/span><strong>Why is Data Cleaning Important?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The unsung hero of machine learning, data cleaning is vital to any project that uses data but is frequently disregarded. Its importance lies in the profound impact it has on both data quality and the performance of machine learning models.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Impact on Data Quality<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Data cleaning improves the quality of your dataset by removing irrelevant information, errors, and inconsistencies. It ensures that your data is accurate, reliable, and representative of the real-world phenomena you’re trying to analyze. This, in turn, lays a solid foundation for robust analysis and modeling.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Impact on Model Performance<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The adage “garbage in, garbage out” rings true in machine learning. Feeding dirty data into your model is akin to constructing a house on shaky ground. It causes skewed results, inaccurate forecasts, and ultimately bad decision-making. By meticulously cleaning your data, you remove the noise and distractions that can mislead your algorithms, allowing them to learn the true patterns and relationships within the data. This translates to improved accuracy, better generalization, and ultimately, more valuable insights.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Importance of Data Cleaning in Data Science and Machine Learning<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Data cleaning is not just a preliminary step; it’s an ongoing process that permeates the entire <a href=\"https:\/\/www.scaler.com\/blog\/what-is-data-science\/\" target=\"_blank\" rel=\"noreferrer noopener\">data science<\/a> lifecycle. Data cleaning is essential at every stage, from gathering and analyzing data to creating and implementing models. It ensures that your data is in optimal condition for analysis, leading to more robust and reliable results.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In the realm of <a href=\"https:\/\/www.scaler.com\/blog\/machine-learning\/\" target=\"_blank\" rel=\"noreferrer noopener\">machine learning<\/a>, clean data is the fuel that powers accurate predictions and insightful models. By investing time and effort in data cleaning, you pave the way for models that can generalize well to new data, make accurate predictions, and ultimately, drive better decision-making in your organization or research.<\/p>\n\n\n<meta charset=\"UTF-8\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n\n <!-- Source Sans Pro (topics font) -->\n <link href=\"https:\/\/fonts.googleapis.com\/css2?family=Source+Sans+3:wght@400;600;700&display=swap\" rel=\"stylesheet\">\n <link rel=\"stylesheet\" href=\"https:\/\/cdn.jsdelivr.net\/npm\/swiper@11\/swiper-bundle.min.css\">\n <script src=\"https:\/\/cdn.jsdelivr.net\/npm\/swiper@11\/swiper-bundle.min.js\"><\/script>\n\n <style>\n :root {\n \/* topics design tokens (variables.scss) *\/\n --scaler-primary: #005bb3; \/* primary-color-80 *\/\n --scaler-primary-30: #8acaff; \/* primary-color-30 *\/\n --scaler-brand-blue: #255aad;\n --scaler-live: #ee001d; \/* accent-v5-60 *\/\n --scaler-live-bg: #ffe6e9; \/* accent-v5-10 *\/\n --scaler-card-bg: #ffffff; \/* neutral-scale-0 *\/\n --scaler-surface: #f4f6f9; \/* neutral-scale-2 *\/\n --scaler-text-primary: #1a2c47; \/* neutral-scale-12 *\/\n --scaler-text-secondary: #61738e; \/* neutral-scale-9 *\/\n --scaler-border: #d6dee5; \/* neutral-scale-4 *\/\n --scaler-radius: 16px;\n --scaler-radius-sm: 8px;\n --scaler-shadow: 0 8px 16px 0 rgba(50, 71, 102, 0.16);\n }\n\n .scaler-events-carousel * { box-sizing: border-box; }\n\n .scaler-events-carousel {\n font-family: \"Source Sans 3\", \"Source Sans Pro\", \"Roboto\", Arial, sans-serif;\n padding: 24px;\n position: relative;\n width: 100%;\n }\n .scaler-events-carousel__inner {\n max-width: 1000px;\n margin: 0 auto;\n position: relative;\n width: 100%;\n }\n\n \/* Header \u2014 calendar icon + \"Live Masterclasses\" *\/\n .scaler-events-header {\n display: flex;\n align-items: center;\n gap: 8px;\n margin-bottom: 8px;\n padding: 8px 0;\n }\n .scaler-events-header__icon { display: inline-flex; color: var(--scaler-text-primary); }\n .scaler-events-header__icon svg { width: 24px; height: 24px; }\n .scaler-events-header__title {\n font-size: 18px;\n font-weight: 700;\n color: var(--scaler-text-primary);\n line-height: 1.3;\n margin: 0;\n }\n\n \/* Swiper wrap (holds side arrows) *\/\n .scaler-events-swiper-wrap { position: relative; }\n .scaler-events-carousel .swiper { width: 100%; padding: 4px; }\n .scaler-event-swiper .swiper-slide {\n width: 296px; \/* topics card_container width *\/\n height: auto;\n display: flex;\n }\n\n \/* Event Card \u2014 matches topics EventCard *\/\n .scaler-event-card {\n background: var(--scaler-card-bg);\n border: 1px solid var(--scaler-border);\n border-radius: var(--scaler-radius);\n padding: 16px;\n display: flex;\n flex-direction: column;\n height: 100%;\n width: 100%;\n cursor: pointer;\n }\n\n \/* Header row: headshot + title\/name *\/\n .scaler-event-card__header { display: flex; align-items: flex-start; }\n .scaler-event-card__image-wrapper {\n position: relative;\n width: 88px;\n height: 88px;\n min-width: 88px;\n border-radius: var(--scaler-radius-sm);\n overflow: hidden;\n background: var(--scaler-primary-30);\n }\n .scaler-event-card__image {\n position: absolute;\n top: 0; left: 0;\n width: 100%; height: 100%;\n object-fit: cover;\n }\n \/* attendee-count footer (default) *\/\n .scaler-event-card__img-footer {\n position: absolute;\n left: 0; right: 0; bottom: 0;\n display: flex;\n align-items: center;\n justify-content: center;\n gap: 4px;\n padding: 1px 8px;\n background: var(--scaler-surface);\n color: var(--scaler-text-primary);\n font-size: 13px;\n line-height: 22px;\n }\n .scaler-event-card__img-footer svg { width: 14px; height: 14px; }\n \/* live chip (shown when status === current) *\/\n .scaler-event-card__live-badge {\n position: absolute;\n left: 0; right: 0; bottom: 0;\n display: flex;\n align-items: center;\n justify-content: center;\n gap: 5px;\n padding: 1px 8px;\n background: var(--scaler-live-bg);\n color: var(--scaler-live);\n font-size: 12px;\n line-height: 22px;\n text-transform: uppercase;\n letter-spacing: 0.18em;\n }\n .scaler-event-card__live-badge::before {\n content: \"\";\n width: 6px; height: 6px;\n background: var(--scaler-live);\n border-radius: 50%;\n }\n .scaler-event-card__text { margin-left: 16px; min-width: 0; }\n .scaler-event-card__title {\n font-size: 20px;\n font-weight: 600;\n color: var(--scaler-text-primary);\n margin: 0;\n line-height: 1.4;\n display: -webkit-box;\n -webkit-line-clamp: 2;\n -webkit-box-orient: vertical;\n overflow: hidden;\n }\n .scaler-event-card__speaker {\n margin-top: 8px;\n font-size: 14px;\n line-height: 1.4;\n color: var(--scaler-text-primary);\n }\n\n \/* Takeaways *\/\n .scaler-event-card__takeaways {\n margin-top: 12px;\n min-height: 50px;\n max-height: 50px;\n overflow: hidden;\n }\n .scaler-event-card__takeaway-item {\n display: flex;\n align-items: flex-start;\n gap: 6px;\n }\n .scaler-event-card__dot {\n width: 4px; height: 4px;\n min-width: 4px;\n border-radius: 50%;\n background: var(--scaler-text-secondary);\n margin-top: 11px;\n }\n .scaler-event-card__takeaway-text {\n flex: 1;\n min-width: 0;\n font-size: 14px;\n line-height: 24px;\n color: var(--scaler-text-primary);\n white-space: nowrap;\n overflow: hidden;\n text-overflow: ellipsis;\n }\n .scaler-event-card__knowmore,\n .scaler-event-card__knowmore:link,\n .scaler-event-card__knowmore:visited {\n flex-shrink: 0;\n font-size: 14px;\n line-height: 24px;\n color: var(--scaler-primary) !important;\n text-decoration: none;\n white-space: nowrap;\n }\n .scaler-event-card__knowmore:hover { text-decoration: underline; }\n\n \/* Date + duration *\/\n .scaler-event-card__meta {\n display: flex;\n align-items: center;\n justify-content: space-between;\n margin-top: 20px;\n font-size: 13px;\n line-height: 24px;\n color: var(--scaler-text-primary);\n }\n .scaler-event-card__duration {\n display: flex;\n align-items: center;\n gap: 4px;\n }\n .scaler-event-card__duration svg { width: 15px; height: 15px; }\n\n .scaler-event-card__spacer { flex-grow: 1; min-height: 4px; }\n\n \/* CTA \u2014 outlined \"Register with 1-Click\" *\/\n .scaler-event-card__cta,\n .scaler-event-card__cta:link,\n .scaler-event-card__cta:visited {\n display: flex;\n align-items: center;\n justify-content: center;\n width: 100%;\n margin-top: 16px;\n padding: 8px;\n background: transparent;\n color: var(--scaler-primary) !important;\n font-family: inherit;\n font-size: 14px;\n font-weight: 600;\n line-height: 24px;\n text-decoration: none;\n border: 1px solid var(--scaler-primary);\n border-radius: var(--scaler-radius-sm);\n cursor: pointer;\n transition: background 0.2s ease, color 0.2s ease;\n }\n .scaler-event-card__cta:hover,\n .scaler-event-card__cta:focus {\n background: var(--scaler-primary);\n color: #fff !important;\n }\n\n \/* Side navigation arrows *\/\n .scaler-events-nav__btn {\n position: absolute !important;\n top: 50% !important;\n transform: translateY(-50%) !important;\n z-index: 2;\n width: 46px !important;\n height: 46px !important;\n min-width: 46px;\n display: flex !important;\n align-items: center;\n justify-content: center;\n padding: 0 !important;\n background: var(--scaler-card-bg) !important;\n border: none !important;\n border-radius: var(--scaler-radius-sm) !important;\n box-shadow: var(--scaler-shadow) !important;\n color: var(--scaler-text-secondary) !important;\n cursor: pointer;\n transition: opacity 0.2s ease, visibility 0.2s ease;\n }\n .scaler-nav-prev { left: 8px !important; }\n .scaler-nav-next { right: 8px !important; }\n .scaler-events-nav__btn svg {\n width: 20px !important;\n height: 20px !important;\n fill: var(--scaler-text-secondary) !important;\n }\n .scaler-events-nav__btn.swiper-button-disabled {\n opacity: 0;\n visibility: hidden;\n pointer-events: none;\n }\n\n \/* View All Events *\/\n .scaler-events-viewall,\n .scaler-events-viewall:link,\n .scaler-events-viewall:visited {\n display: flex;\n align-items: center;\n justify-content: center;\n gap: 6px;\n margin-top: 12px;\n font-size: 14px;\n font-weight: 600;\n color: var(--scaler-brand-blue) !important;\n text-decoration: none;\n }\n .scaler-events-viewall:hover { text-decoration: underline; }\n .scaler-events-viewall svg { width: 16px; height: 16px; fill: var(--scaler-brand-blue); }\n\n \/* hide swiper defaults *\/\n .scaler-events-pagination { display: none; }\n .scaler-events-carousel .swiper-button-prev,\n .scaler-events-carousel .swiper-button-next { display: none; }\n\n \/* Skeleton & Empty States *\/\n .scaler-event-card--skeleton { pointer-events: none; }\n .scaler-event-card--skeleton .scaler-event-card__image-wrapper,\n .scaler-event-card--skeleton .scaler-event-card__title,\n .scaler-event-card--skeleton .scaler-event-card__speaker,\n .scaler-event-card--skeleton .scaler-event-card__takeaways,\n .scaler-event-card--skeleton .scaler-event-card__meta,\n .scaler-event-card--skeleton .scaler-event-card__cta {\n background: linear-gradient(90deg, var(--scaler-border) 25%, var(--scaler-surface) 50%, var(--scaler-border) 75%);\n background-size: 200% 100%;\n animation: shimmer 1.5s infinite;\n color: transparent !important;\n border-color: transparent !important;\n border-radius: 4px;\n }\n .scaler-event-card--skeleton .scaler-event-card__title { min-height: 44px; }\n .scaler-event-card--skeleton .scaler-event-card__speaker { min-height: 16px; }\n .scaler-event-card--skeleton .scaler-event-card__meta { min-height: 24px; }\n .scaler-event-card--skeleton .scaler-event-card__cta { min-height: 40px; }\n .scaler-event-card--skeleton .scaler-event-card__image,\n .scaler-event-card--skeleton .scaler-event-card__img-footer,\n .scaler-event-card--skeleton .scaler-event-card__live-badge { display: none; }\n\n @keyframes shimmer {\n 0% { background-position: 200% 0; }\n 100% { background-position: -200% 0; }\n }\n\n .scaler-events-empty {\n text-align: center;\n padding: 40px 20px;\n color: var(--scaler-text-secondary);\n font-size: 15px;\n }\n\n @media (max-width: 768px) {\n .scaler-events-carousel { padding: 16px; }\n .scaler-event-swiper .swiper-slide { width: calc(100vw - 52px); }\n .scaler-events-nav__btn { display: none; }\n }\n <\/style>\n\n\n\n <div class=\"scaler-events-carousel js-scaler-carousel\">\n\n <template class=\"js-event-card-template\">\n <div class=\"swiper-slide\">\n <article class=\"scaler-event-card\">\n <div class=\"scaler-event-card__header\">\n <div class=\"scaler-event-card__image-wrapper\">\n <img decoding=\"async\" src=\"\" alt=\"\" class=\"scaler-event-card__image\" loading=\"lazy\">\n <div class=\"scaler-event-card__img-footer\">\n <svg fill=\"currentColor\" viewBox=\"0 0 32 32\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M11.199 7.957c-3.086 0-5.588 2.502-5.588 5.588s2.502 5.588 5.588 5.588c3.086 0 5.588-2.502 5.588-5.588s-2.502-5.588-5.588-5.588zM3.611 13.545c0-4.191 3.397-7.588 7.588-7.588s7.588 3.397 7.588 7.588c0 4.191-3.397 7.588-7.588 7.588s-7.588-3.397-7.588-7.588z\"\/><path d=\"M21.090 6.369h0.003c1.989 0 3.897 0.79 5.303 2.197s2.197 3.314 2.197 5.303-0.79 3.897-2.197 5.303c-1.406 1.407-3.314 2.197-5.303 2.197-0.552 0-1-0.448-1-1s0.448-1 1-1c1.459 0 2.858-0.579 3.889-1.611s1.611-2.43 1.611-3.889c0-1.459-0.58-2.858-1.611-3.889s-2.429-1.611-3.888-1.611c-0.507 0.002-1.013 0.070-1.502 0.203-0.533 0.145-1.082-0.17-1.227-0.703s0.17-1.082 0.703-1.227c0.659-0.179 1.339-0.27 2.021-0.272z\"\/><path d=\"M10.906 21.364c-1.605 0-3.186 0.386-4.61 1.126s-2.649 1.811-3.572 3.124c-0.318 0.452-0.941 0.561-1.393 0.243s-0.561-0.941-0.243-1.393c1.107-1.576 2.577-2.862 4.286-3.749s3.606-1.351 5.532-1.351 3.823 0.463 5.532 1.351c1.709 0.888 3.179 2.174 4.286 3.749 0.318 0.452 0.209 1.076-0.243 1.393s-1.076 0.209-1.393-0.243c-0.923-1.313-2.148-2.385-3.572-3.124s-3.005-1.126-4.61-1.126z\"\/><\/svg>\n <span class=\"js-event-count\"><\/span>\n <\/div>\n <div class=\"scaler-event-card__live-badge\" style=\"display: none;\">Live<\/div>\n <\/div>\n <div class=\"scaler-event-card__text\">\n <h3 class=\"scaler-event-card__title\"><\/h3>\n <div class=\"scaler-event-card__speaker js-event-speaker\"><\/div>\n <\/div>\n <\/div>\n\n <div class=\"scaler-event-card__takeaways js-event-takeaways\"><\/div>\n\n <div class=\"scaler-event-card__spacer\"><\/div>\n\n <div class=\"scaler-event-card__meta\">\n <span class=\"js-event-date\"><\/span>\n <span class=\"scaler-event-card__duration\">\n <svg fill=\"currentColor\" viewBox=\"0 0 32 32\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M16 6.333c6.443 0 11.667 5.223 11.667 11.667s-5.223 11.667-11.667 11.667c-6.443 0-11.667-5.223-11.667-11.667v0c0.008-6.44 5.226-11.659 11.666-11.667h0.001zM16 28.333c5.707 0 10.333-4.626 10.333-10.333s-4.626-10.333-10.333-10.333c-5.707 0-10.333 4.626-10.333 10.333v0c0.007 5.704 4.629 10.326 10.333 10.333h0.001z\"\/><path d=\"M16 18.667c-0 0-0.001 0-0.001 0-0.184 0-0.35-0.074-0.471-0.195v0c-0.121-0.121-0.195-0.287-0.195-0.471s0.075-0.351 0.195-0.471l4.951-4.952c0.121-0.121 0.287-0.195 0.471-0.195s0.351 0.075 0.471 0.195v0c0.121 0.121 0.195 0.287 0.195 0.471s-0.075 0.351-0.195 0.471l-4.949 4.951c-0.121 0.121-0.288 0.196-0.472 0.196v0z\"\/><path d=\"M19 3.667h-6c-0.368 0-0.667-0.298-0.667-0.667v0c0-0.368 0.298-0.667 0.667-0.667v0h6c0.368 0 0.667 0.298 0.667 0.667v0c0 0.368-0.298 0.667-0.667 0.667v0z\"\/><\/svg>\n <span class=\"js-event-duration\"><\/span>\n <\/span>\n <\/div>\n\n <a href=\"#\" class=\"scaler-event-card__cta js-event-cta\">Register with 1-Click<\/a>\n <\/article>\n <\/div>\n <\/template>\n\n <div class=\"scaler-events-carousel__inner\">\n <header class=\"scaler-events-header\">\n <span class=\"scaler-events-header__icon\">\n <svg fill=\"currentColor\" viewBox=\"0 0 33 32\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M6.406 6.8c-0.11 0-0.2 0.090-0.2 0.2v20c0 0.11 0.090 0.2 0.2 0.2h20c0.11 0 0.2-0.090 0.2-0.2v-20c0-0.11-0.090-0.2-0.2-0.2h-20zM4.606 7c0-0.994 0.806-1.8 1.8-1.8h20c0.994 0 1.8 0.806 1.8 1.8v20c0 0.994-0.806 1.8-1.8 1.8h-20c-0.994 0-1.8-0.806-1.8-1.8v-20z\"\/><path d=\"M22.406 3.2c0.442 0 0.8 0.358 0.8 0.8v4c0 0.442-0.358 0.8-0.8 0.8s-0.8-0.358-0.8-0.8v-4c0-0.442 0.358-0.8 0.8-0.8z\"\/><path d=\"M10.406 3.2c0.442 0 0.8 0.358 0.8 0.8v4c0 0.442-0.358 0.8-0.8 0.8s-0.8-0.358-0.8-0.8v-4c0-0.442 0.358-0.8 0.8-0.8z\"\/><path d=\"M4.606 12c0-0.442 0.358-0.8 0.8-0.8h22c0.442 0 0.8 0.358 0.8 0.8s-0.358 0.8-0.8 0.8h-22c-0.442 0-0.8-0.358-0.8-0.8z\"\/><path d=\"M21.488 16.451c0.303 0.322 0.288 0.828-0.034 1.131l-5.838 5.5c-0.309 0.291-0.791 0.29-1.099-0.002l-3.162-3c-0.321-0.304-0.334-0.81-0.030-1.131s0.81-0.334 1.131-0.030l2.614 2.48 5.287-4.981c0.322-0.303 0.828-0.288 1.131 0.034z\"\/><\/svg>\n <\/span>\n <h2 class=\"scaler-events-header__title\"><span class=\"ez-toc-section\" id=\"live-masterclasses\"><\/span>Live Masterclasses<span class=\"ez-toc-section-end\"><\/span><\/h2>\n <\/header>\n\n <div class=\"scaler-events-swiper-wrap\">\n <div class=\"swiper scaler-event-swiper\">\n <div class=\"swiper-wrapper scaler-events-wrapper\"><\/div>\n <div class=\"swiper-pagination scaler-events-pagination\"><\/div>\n <\/div>\n\n <button class=\"scaler-events-nav__btn scaler-nav-prev\" aria-label=\"Previous slide\">\n <svg fill=\"currentColor\" viewBox=\"0 0 32 32\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"transform: rotate(-90deg);\"><path d=\"M5.313 16l10.688-10.688 10.688 10.688-1.938 1.875-7.438-7.438v16.25h-2.625v-16.25l-7.5 7.438z\"\/><\/svg>\n <\/button>\n <button class=\"scaler-events-nav__btn scaler-nav-next\" aria-label=\"Next slide\">\n <svg fill=\"currentColor\" viewBox=\"0 0 32 32\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"transform: rotate(90deg);\"><path d=\"M5.313 16l10.688-10.688 10.688 10.688-1.938 1.875-7.438-7.438v16.25h-2.625v-16.25l-7.5 7.438z\"\/><\/svg>\n <\/button>\n <\/div>\n\n <a class=\"scaler-events-viewall\" href=\"https:\/\/www.scaler.com\/events\/\">\n <span>View All Events<\/span>\n <svg fill=\"currentColor\" viewBox=\"0 0 32 32\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"transform: rotate(90deg);\"><path d=\"M5.313 16l10.688-10.688 10.688 10.688-1.938 1.875-7.438-7.438v16.25h-2.625v-16.25l-7.5 7.438z\"\/><\/svg>\n <\/a>\n <\/div>\n <\/div>\n\n <script>\n document.addEventListener(\"DOMContentLoaded\", () => {\n\n const monthNames = [\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"];\n\n function formatTime(st) {\n const t = new Date(st);\n const date = t.getDate();\n const month = monthNames[t.getMonth()];\n const year = t.getFullYear();\n const h = t.getHours();\n const m = String(t.getMinutes()).padStart(2, \"0\");\n let time;\n if (h === 0) time = `12:${m} AM`;\n else if (h === 12) time = `12:${m} PM`;\n else if (h > 12) time = `${h - 12}:${m} PM`;\n else time = `${h}:${m} AM`;\n return `${date} ${month} ${year} | ${time}`;\n }\n\n const carouselInstances = document.querySelectorAll('.js-scaler-carousel');\n\n carouselInstances.forEach(container => {\n\n if(container.dataset.initialized === \"true\") return;\n container.dataset.initialized = \"true\";\n\n const swiperElement = container.querySelector(\".scaler-event-swiper\");\n const swiperWrapper = container.querySelector(\".scaler-events-wrapper\");\n const template = container.querySelector(\".js-event-card-template\");\n const nextBtn = container.querySelector(\".scaler-nav-next\");\n const prevBtn = container.querySelector(\".scaler-nav-prev\");\n const paginationEl = container.querySelector(\".scaler-events-pagination\");\n\n if (!swiperWrapper || !template) {\n console.error(\"Scaler Carousel: Missing required elements inside container\");\n return;\n }\n\n const swiper = new Swiper(swiperElement, {\n slidesPerView: \"auto\",\n spaceBetween: 16,\n grabCursor: true,\n observer: true,\n observeParents: true,\n pagination: {\n el: paginationEl,\n clickable: true,\n dynamicBullets: true\n },\n navigation: {\n nextEl: nextBtn,\n prevEl: prevBtn\n },\n });\n\n function showSkeletons(count = 4) {\n swiperWrapper.innerHTML = \"\";\n for (let i = 0; i < count; i++) {\n const clone = template.content.cloneNode(true);\n const card = clone.querySelector(\".scaler-event-card\");\n card.classList.add(\"scaler-event-card--skeleton\");\n swiperWrapper.appendChild(clone);\n }\n swiper.update();\n }\n\n function renderEvents(events) {\n swiperWrapper.innerHTML = \"\";\n\n if (events.length === 0) {\n swiperWrapper.innerHTML = `<div class=\"scaler-events-empty\">No upcoming masterclasses found.<\/div>`;\n return;\n }\n\n const pathParts = location.pathname.split(\"\/\").filter(Boolean);\n const currentSlug = pathParts.length > 0 ? pathParts[pathParts.length - 1] : \"homepage\";\n\n events.forEach(event => {\n const attr = event.attributes;\n const cd = attr.custom_data || {};\n const clone = template.content.cloneNode(true);\n const joinUrl = `\/event\/${attr.slug}\/?utm_source=blog&utm_medium=master_class&utm_content=${currentSlug}`;\n\n \/\/ Instructor headshot\n const instructor = (cd.instructors && cd.instructors[0]) || {};\n const img = clone.querySelector(\".scaler-event-card__image\");\n img.src = instructor.media_url || attr.image_url ||\n \"https:\/\/images.unsplash.com\/photo-1531891437562-4301cf35b7e4?w=200&h=200&fit=crop\";\n img.alt = instructor.name || attr.instructor_name || attr.title;\n\n \/\/ Title + speaker\n clone.querySelector(\".scaler-event-card__title\").textContent = attr.title;\n clone.querySelector(\".js-event-speaker\").textContent = instructor.name || attr.instructor_name || \"\";\n\n \/\/ Live chip vs attendee count\n const isLive = attr.status === \"current\";\n const footer = clone.querySelector(\".scaler-event-card__img-footer\");\n const liveBadge = clone.querySelector(\".scaler-event-card__live-badge\");\n if (isLive) {\n footer.style.display = \"none\";\n liveBadge.style.display = \"flex\";\n liveBadge.textContent = \"Live\";\n } else if (attr.registration_count) {\n clone.querySelector(\".js-event-count\").textContent = attr.registration_count;\n } else {\n footer.style.display = \"none\";\n }\n\n \/\/ Takeaways (up to 2, Know More on the last)\n const takeaways = (cd.takeaways || []).slice(0, 2);\n const listEl = clone.querySelector(\".js-event-takeaways\");\n listEl.innerHTML = \"\";\n takeaways.forEach((t, idx) => {\n const item = document.createElement(\"div\");\n item.className = \"scaler-event-card__takeaway-item\";\n const dot = document.createElement(\"span\");\n dot.className = \"scaler-event-card__dot\";\n const txt = document.createElement(\"span\");\n txt.className = \"scaler-event-card__takeaway-text\";\n txt.textContent = t;\n item.appendChild(dot);\n item.appendChild(txt);\n if (idx === takeaways.length - 1) {\n const km = document.createElement(\"a\");\n km.className = \"scaler-event-card__knowmore\";\n km.textContent = \"Know More\";\n km.href = joinUrl;\n item.appendChild(km);\n }\n listEl.appendChild(item);\n });\n\n \/\/ Date + duration\n clone.querySelector(\".js-event-date\").textContent = formatTime(attr.start_time);\n let hrs = \"\";\n if (attr.duration) {\n hrs = (attr.duration \/ 60).toFixed(2);\n } else if (attr.end_time) {\n hrs = (Math.abs(new Date(attr.start_time) - new Date(attr.end_time)) \/ 3600000).toFixed(2);\n }\n clone.querySelector(\".js-event-duration\").textContent = hrs ? `${hrs} Hrs` : \"\";\n\n \/\/ CTA\n clone.querySelector(\".js-event-cta\").href = joinUrl;\n\n swiperWrapper.appendChild(clone);\n });\n\n swiper.update();\n swiper.slideTo(0);\n }\n\n async function fetchEvents() {\n try {\n showSkeletons();\n const res = await fetch(\n \"https:\/\/www.scaler.com\/api\/v4\/events?event_type[]=company&distributor=scaler&type=upcoming&serializer_mode=L2&limit=8&program[]=software_development&program[]=data_science&program[]=devops&program[]=ai_ml\"\n );\n const json = await res.json();\n const events = json.data || [];\n renderEvents(events);\n } catch (error) {\n console.error(\"Failed to load events:\", error);\n if(swiperWrapper) swiperWrapper.innerHTML = `<div class=\"scaler-events-empty\">Failed to load events.<\/div>`;\n }\n }\n\n fetchEvents();\n });\n });\n <\/script>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"steps-to-perform-data-cleaning-in-machine-learning\"><\/span><strong>Steps to Perform Data Cleaning<\/strong> in Machine Learning<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In order to guarantee data quality and dependability, data cleaning is a multifaceted process that calls for a methodical approach. Let’s break down the essential steps involved:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Data Inspection and Exploration<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Begin by thoroughly understanding your dataset. Utilize tools like df.info() in Python’s pandas library to get a quick overview of the data types, missing values, and column names. Sort the columns by category and number, then look at how the values are distributed in each. Counting unique values in categorical columns can reveal potential inconsistencies or errors. Visualizing the data using histograms or scatter plots can also provide valuable insights into its structure and potential issues.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Removing Unwanted Observations<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Identify and remove irrelevant columns or rows that do not contribute to your analysis. Columns like “Name” or “Ticket,” for example, may not be relevant for predicting survival in a Titanic dataset. Similarly, remove duplicate rows to avoid biases and ensure accurate analysis.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3. Handling Missing Data<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Missing data is a common challenge in real-world datasets. Based on the type and degree of missingness, you have a few options for techniques:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Deletion:<\/strong> If missing values are few and random, you can simply delete the corresponding rows or columns.<\/li>\n\n\n\n<li><strong>Imputation:<\/strong> This involves replacing missing values with estimated values based on other available data. The mean, median, mode, and more complex methods like regression or K-nearest neighbours imputation are examples of common imputation techniques. <\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>4. Handling Outliers<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Outliers are data points that deviate significantly from the rest of the distribution. They may distort analysis and produce unreliable findings. Detect outliers using statistical methods like the z-score or interquartile range (IQR). Once identified, you can choose to remove them or replace them with more reasonable values, depending on your analysis goals.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>5. Data Transformation<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Transforming data into a format that can be used for analysis and modeling is known as data transformation. This can include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Standardization:<\/strong> Scaling features to have zero mean and unit variance.<\/li>\n\n\n\n<li><strong>Normalization:<\/strong> Scaling features to a range between 0 and 1.<\/li>\n\n\n\n<li><strong>Encoding:<\/strong> Converting categorical variables into numerical representations that machine learning algorithms can understand.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>6. Data Validation and Verification<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The final step involves verifying the accuracy and consistency of the cleaned data. This entails looking for any remaining errors or outliers, as well as data type inconsistencies and inconsistencies across various data sources. Thorough validation ensures that your cleaned dataset is ready for analysis and modeling.<\/p>\n\n\n\n<!DOCTYPE html>\n<html>\n <head>\n <title>Hello World!<\/title>\n <link rel=\"preconnect\" href=\"https:\/\/fonts.googleapis.com\">\n <link rel=\"preconnect\" href=\"https:\/\/fonts.gstatic.com\" crossorigin>\n <link href=\"https:\/\/fonts.googleapis.com\/css2?family=Lato:wght@400;600;700&display=swap\" rel=\"stylesheet\">\n <style>\n .iitr_banner_container {\n font-family: lato;\n display: flex;\n flex-direction: row;\n justify-content: space-between;\n border-radius: 16px;\n background: linear-gradient(88deg, #19000F 24.45%, #66003F 83.33%);\n position: relative;\n\n @media (max-width: 768px) {\n min-height: 450px;\n overflow: hidden;\n flex-direction: column;\n }\n }\n .iitr_banner_content {\n display: flex;\n flex-direction: column;\n align-items: flex-start;\n justify-content: center;\n padding: 20px;\n max-width: 50%;\n\n @media (max-width: 768px) {\n max-width: 100%;\n }\n }\n .iitr_banner_title {\n font-size: 24px;\n font-weight: bold;\n color: #FFFFFF;\n\n @media (max-width: 768px) {\n font-size: 20px;\n }\n }\n .iitr_banner_title_highlight {\n color: #FF0071;\n }\n .iitr_banner_subtitle {\n font-size: 14px;\n color: #FFFFFF;\n margin: 10px 0;\n }\n .iitr_banner_btn {\n display: flex;\n justify-content: center;\n align-items: center;\n padding: 8px 48px;\n background-color: #F8F9F9;\n border-radius: 8px;\n border: 1px solid #E3E8E8;\n font-size: 1.4rem;\n font-weight: 600;\n color: #0D3231;\n text-decoration: none;\n margin-top: 16px;\n\n @media (max-width: 768px) {\n padding: 8px 32px;\n }\n }\n .iitr_banner_image {\n position: absolute;\n bottom: 0;\n right: 0;\n\n @media (max-width: 768px) {\n right: auto;\n object-fit: cover;\n min-width: 100%\n }\n }\n .iitr_banner_image_logo {\n margin-bottom: 16px;\n \n @media (max-width: 768px) {\n width: 240px;\n }\n }\n\n \/* Responsive visibility utilities *\/\n .show-in-mobile {\n display: none;\n }\n .hide-in-mobile {\n display: block;\n }\n\n \/* Mobile breakpoint (768px and below) *\/\n @media (max-width: 768px) {\n .show-in-mobile {\n display: block;\n }\n .hide-in-mobile {\n display: none;\n }\n }\n <\/style>\n <\/head>\n <body>\n <div class=\"iitr_banner_container\">\n <div class=\"iitr_banner_content\">\n <img decoding=\"async\" src=\"https:\/\/d2beiqkhq929f0.cloudfront.net\/public_assets\/assets\/000\/176\/281\/original\/Frame_1430102419.svg?1769058073\" class=\"iitr_banner_image_logo\" \/>\n <div class=\"iitr_banner_title\">\n AI Engineering Course Advanced Certification by \n <span class=\"iitr_banner_title_highlight\">\n IIT-Roorkee CEC\n <\/span>\n <\/div>\n <div class=\"iitr_banner_subtitle\">\n A hands on AI engineering program covering Machine Learning, Generative AI, and LLMs – designed for working professionals & delivered by IIT Roorkee in collaboration with Scaler.\n <\/div>\n <a class=\"iitr_banner_btn\" href=\"#\" id=\"iitr_banner_btn\">Enrol Now<\/a>\n <\/div>\n <!-- Desktop Image -->\n <img decoding=\"async\" class=\"iitr_banner_image hide-in-mobile\" src=\"https:\/\/d2beiqkhq929f0.cloudfront.net\/public_assets\/assets\/000\/176\/282\/original\/iitr_2.svg?1769058132\" \/>\n <!-- Mobile Image -->\n <img decoding=\"async\" class=\"iitr_banner_image show-in-mobile\" src=\"https:\/\/d2beiqkhq929f0.cloudfront.net\/public_assets\/assets\/000\/176\/283\/original\/iitr_2_%281%29.svg?1769059469\" \/>\n <\/div>\n <script>\n document.addEventListener(\"DOMContentLoaded\", () => {\n const pathParts = location.pathname.split(\"\/\").filter(Boolean);\n const currentSlug = pathParts.length > 0 ? pathParts[pathParts.length - 1] : \"homepage\";\n const url = `https:\/\/www.scaler.com\/iit-roorkee-advanced-ai-engineering-course?utm_source=blog&utm_medium=iit_roorkee&utm_content=${currentSlug}`;\n const btns = document.querySelectorAll(\".iitr_banner_btn\");\n btns.forEach(btn => {\n btn.href = url;\n });\n });\n <\/script>\n <\/body>\n<\/html>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"python-implementation-for-data-cleaning\"><\/span><strong>Python Implementation for Data Cleaning<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Python, with its rich ecosystem of libraries like pandas and NumPy, offers a powerful toolkit for data cleaning. Let’s walk through a practical implementation of the data-cleaning process using Python code snippets.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Sample CSV File: Let’s take an example CSV file data for the code implementation:<\/strong><\/h4>\n\n\n\n<pre class=\"wp-block-code\"><code>\"ID\",\"Name\",\"Age\",\"Score\",\"City\",\"Country\" \n1,\"John Doe\",25,85,\"New York\",\"USA\" \n2,\"Jane Smith\",30,90,\"Los Angeles\",\"USA\" \n3,\"Bob Johnson\",28,78,\"Chicago\",\"USA\" \n4,\"Alice Brown\",22,92,\"Houston\",\"USA\" \n5,\"Mike Davis\",35,88,\"Philadelphia\",\"USA\" \n6,\"Emily Taylor\",29,95,\"Phoenix\",\"USA\" \n7,\"Sarah Lee\",26,80,\"San Antonio\",\"USA\" \n8,\"Kevin White\",31,89,\"San Diego\",\"USA\" \n9,\"Lisa Hall\",27,91,\"Dallas\",\"USA\" \n10,\"Tom Harris\",33,87,\"San Jose\",\"USA\"<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Data Inspection and Exploration<\/strong><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>import pandas as pd\n\n# Load your dataset\ndf = pd.read_csv(\"your_dataset.csv\")\n\n# Display basic information about the dataset\nprint(df.info())\n\n# Display descriptive statistics for numerical columns\nprint(df.describe())\n\n# Display the first few rows of the dataset\nprint(df.head())\n\n# Visualize the distribution of numerical columns\nimport matplotlib.pyplot as plt\ndf.hist(bins=30, figsize=(10, 8))\nplt.show()<\/code><\/pre>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Output:<\/strong><\/h4>\n\n\n\n<h4 class=\"wp-block-heading\"><strong> Kotlin:<\/strong><\/h4>\n\n\n\n<pre class=\"wp-block-code\"><code><class 'pandas.core.frame.DataFrame'>\nRangeIndex: 10 entries, 0 to 9\nData columns (total 6 columns):\n # Column Non-Null Count Dtype\n--- ------ -------------- -----\n 0 ID 10 non-null int64\n 1 Name 10 non-null object\n 2 Age 10 non-null int64\n 3 Score 10 non-null int64\n 4 City 10 non-null object\n 5 Country 10 non-null object\ndtypes: int64(3), object(3)\nmemory usage: 608.0+ bytes<\/code><\/pre>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Shell:<\/strong><\/h4>\n\n\n\n<pre class=\"wp-block-code\"><code> ID Age Score\ncount 10.00000 10.000000 10.000000\nmean 5.50000 28.600000 87.500000\nstd 3.02765 3.683748 5.724556\nmin 1.00000 22.000000 78.000000\n25% 3.25000 25.750000 85.250000\n50% 5.50000 28.000000 88.500000\n75% 7.75000 31.000000 91.000000\nmax 10.00000 35.000000 95.000000<\/code><\/pre>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Mathematica:<\/strong><\/h4>\n\n\n\n<pre class=\"wp-block-code\"><code> ID Name Age Score City Country\n0 1 John Doe 25 85 New York USA\n1 2 Jane Smith 30 90 Los Angeles USA\n2 3 Bob Johnson 28 78 Chicago USA\n3 4 Alice Brown 22 92 Houston USA\n4 5 Mike Davis 35 88 Philadelphia USA<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Removing Unwanted Observations<\/strong><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code># Drop irrelevant columns (assuming 'ID' is not needed)\ndf.drop([\"ID\"], axis=1, inplace=True)\n\n# Remove duplicate rows (if any)\ndf.drop_duplicates(inplace=True)\n\n# Remove rows based on a condition (example: Age less than 24)\ndf = df[df['Age'] >= 24]\n\n# Display the cleaned dataframe\nprint(df.head())<\/code><\/pre>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Output:<\/strong><\/h4>\n\n\n\n<pre class=\"wp-block-code\"><code> Name Age Score City Country\n0 John Doe 25 85 New York USA\n1 Jane Smith 30 90 Los Angeles USA\n2 Bob Johnson 28 78 Chicago USA\n4 Mike Davis 35 88 Philadelphia USA\n5 Emily Taylor 29 95 Phoenix USA<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3. Handling Missing Data<\/strong><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code># Drop rows with missing values (if appropriate)\ndf.dropna(inplace=True)\n\n# Impute missing values with mean (for numerical columns)\ndf[\"Age\"].fillna(df[\"Age\"].mean(), inplace=True)\n\n# Impute missing values with mode (for categorical columns)\ndf[\"City\"].fillna(df[\"City\"].mode()[0], inplace=True)\n\n# Display the dataframe after handling missing data\nprint(df.info())<\/code><\/pre>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Output: Kotlin<\/strong><\/h4>\n\n\n\n<pre class=\"wp-block-code\"><code><class 'pandas.core.frame.DataFrame'>\nInt64Index: 9 entries, 0 to 9\nData columns (total 5 columns):\n# Column Non-Null Count Dtype\n--- ------ -------------- -----\n0 Name 9 non-null object\n1 Age 9 non-null int64\n2 Score 9 non-null int64\n3 City 9 non-null object\n4 Country 9 non-null object\ndtypes: int64(2), object(3)\nmemory usage: 432.0+ bytes<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>4. Handling Outliers<\/strong><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>import numpy as np\nfrom scipy import stats\n\n# Detect outliers using z-score (for numerical columns)\nz_scores = stats.zscore(df[\"Score\"])\nabs_z_scores = np.abs(z_scores)\nfiltered_entries = (abs_z_scores < 3)\ndf = df[filtered_entries]\n\n# Detect outliers using IQR (Interquartile Range)\nQ1 = df['Score'].quantile(0.25)\nQ3 = df['Score'].quantile(0.75)\nIQR = Q3 - Q1\ndf = df[~((df['Score'] < (Q1 - 1.5 * IQR)) | (df['Score'] > (Q3 + 1.5 * IQR)))]\n\n# Display the dataframe after handling outliers\nprint(df.describe())<\/code><\/pre>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Output: <\/strong>Shell<\/h4>\n\n\n\n<pre class=\"wp-block-code\"><code> Age Score\ncount 9.000000 9.000000\nmean 28.555556 88.222222\nstd 3.709775 5.244044\nmin 25.000000 78.000000\n25% 26.000000 85.000000\n50% 29.000000 88.000000\n75% 30.000000 91.000000\nmax 35.000000 95.000000<\/code><\/pre>\n\n\n<meta charset=\"UTF-8\">\n 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.scaler-course-card__instructor b { font-weight: 700; }\n .scaler-course-card__desc {\n flex-grow: 1;\n margin-top: 8px;\n margin-bottom: 12px;\n font-size: 16px;\n line-height: 1.5;\n color: var(--cl-text-secondary);\n display: -webkit-box;\n -webkit-line-clamp: 2;\n -webkit-box-orient: vertical;\n overflow: hidden;\n }\n\n .scaler-course-card__footer {\n display: flex;\n align-items: center;\n justify-content: space-between;\n width: 100%;\n }\n .scaler-course-card__stat {\n display: flex;\n align-items: center;\n gap: 4px;\n font-size: 14px;\n color: var(--cl-text-primary);\n }\n .scaler-course-card__stat svg { width: 18px; height: 18px; flex-shrink: 0; }\n .scaler-course-card__stat img { width: 20px; height: 20px; flex-shrink: 0; }\n\n \/* Side arrows *\/\n .scaler-courses__nav {\n position: absolute !important;\n top: 50% !important;\n transform: translateY(-50%) !important;\n z-index: 2;\n width: 46px !important; height: 46px !important; min-width: 46px;\n display: flex !important; align-items: center; justify-content: center;\n padding: 0 !important;\n background: var(--cl-card-bg) !important;\n border: none !important;\n border-radius: var(--cl-radius-sm) !important;\n box-shadow: var(--cl-shadow) !important;\n color: var(--cl-text-secondary) !important;\n cursor: pointer;\n transition: opacity 0.2s ease, visibility 0.2s ease;\n }\n .scaler-courses__nav--prev { left: 8px !important; }\n .scaler-courses__nav--next { right: 8px !important; }\n .scaler-courses__nav svg { width: 20px !important; height: 20px !important; fill: var(--cl-text-secondary) !important; }\n .scaler-courses__nav.swiper-button-disabled { opacity: 0; visibility: hidden; pointer-events: none; }\n\n .scaler-courses .swiper-button-prev,\n .scaler-courses .swiper-button-next,\n .scaler-courses .swiper-pagination { display: none; }\n\n \/* Skeleton *\/\n .scaler-course-card--skeleton { pointer-events: none; }\n .scaler-course-card--skeleton .scaler-course-card__image,\n .scaler-course-card--skeleton .scaler-course-card__heading,\n .scaler-course-card--skeleton .scaler-course-card__instructor,\n .scaler-course-card--skeleton .scaler-course-card__desc,\n .scaler-course-card--skeleton .scaler-course-card__footer {\n background: linear-gradient(90deg, var(--cl-border) 25%, var(--cl-surface) 50%, var(--cl-border) 75%);\n background-size: 200% 100%;\n animation: cl-shimmer 1.5s infinite;\n color: transparent !important;\n border-radius: 4px;\n }\n .scaler-course-card--skeleton .scaler-course-card__heading { height: 24px; margin: 0 0 8px; }\n .scaler-course-card--skeleton .scaler-course-card__instructor { height: 16px; }\n .scaler-course-card--skeleton .scaler-course-card__footer { height: 20px; }\n @keyframes cl-shimmer { 0% { background-position: 200% 0; } 100% { background-position: -200% 0; } }\n\n .scaler-courses__empty { display: none; }\n\n @media (max-width: 768px) {\n .scaler-courses { padding: 16px; }\n .scaler-courses-swiper .swiper-slide { width: 260px; }\n .scaler-courses__nav { display: none !important; }\n }\n <\/style>\n\n\n <div class=\"scaler-courses js-scaler-courses\">\n\n <template class=\"js-course-card-template\">\n <div class=\"swiper-slide\">\n <a class=\"scaler-course-card js-course-link\" href=\"#\" target=\"_blank\" rel=\"noopener\">\n <img decoding=\"async\" class=\"scaler-course-card__image js-course-image\" src=\"\" alt=\"\" loading=\"lazy\">\n <div class=\"scaler-course-card__detail\">\n <div class=\"scaler-course-card__heading js-course-title\"><\/div>\n <div class=\"scaler-course-card__instructor js-course-instructor\"><\/div>\n <div class=\"scaler-course-card__desc js-course-desc\"><\/div>\n <div class=\"scaler-course-card__footer\">\n <span class=\"scaler-course-card__stat js-course-star\">\n <svg fill=\"currentColor\" viewBox=\"0 0 32 32\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M14.83 2.868c0.342-0.239 0.75-0.368 1.168-0.368s0.826 0.128 1.168 0.368c0.338 0.236 0.596 0.569 0.741 0.955l2.759 6.936c0.003 0.007 0.007 0.013 0.013 0.017s0.013 0.007 0.020 0.007l7.402 0.476c0.411 0.036 0.802 0.194 1.124 0.452s0.559 0.607 0.683 1 0.127 0.815 0.011 1.211c-0.116 0.396-0.347 0.748-0.664 1.013l-5.655 4.714c-0.013 0.011-0.022 0.025-0.028 0.040s-0.006 0.032-0.002 0.047l1.821 7.163c0.107 0.395 0.094 0.812-0.037 1.2-0.133 0.391-0.38 0.734-0.71 0.983s-0.727 0.393-1.14 0.413c-0.413 0.020-0.822-0.084-1.175-0.3l-0.015-0.009-6.307-4.005c-0.004-0.003-0.009-0.004-0.014-0.004s-0.009 0.001-0.013 0.004l-5.876 3.721c-0.389 0.235-0.839 0.347-1.292 0.323s-0.889-0.184-1.251-0.459c-0.361-0.275-0.632-0.652-0.777-1.082-0.143-0.426-0.157-0.885-0.038-1.319l1.684-6.629c0.004-0.016 0.003-0.033-0.002-0.049s-0.015-0.030-0.027-0.040l-5.653-4.714c-0.317-0.264-0.548-0.617-0.664-1.013s-0.112-0.817 0.011-1.211c0.124-0.394 0.361-0.742 0.683-1s0.713-0.416 1.124-0.452l0.023-0.002 7.38-0.476c0.007-0 0.014-0.003 0.020-0.007s0.010-0.010 0.013-0.017l0.008-0.020 2.746-6.913c0.145-0.386 0.403-0.719 0.741-0.956z\"\/><\/svg>\n <span class=\"js-course-star-val\"><\/span>\n <\/span>\n <span class=\"scaler-course-card__stat\">\n <img decoding=\"async\" src=\"https:\/\/d19zaobb0foqu6.cloudfront.net\/topics\/images\/user-check-dark.svg\" alt=\"Enrolled\" loading=\"lazy\">\n <span class=\"js-course-reg\"><\/span>\n <\/span>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n <\/template>\n\n <div class=\"scaler-courses__inner\">\n <div class=\"scaler-courses__header\">\n <span class=\"scaler-courses__header-icon\">\n <svg fill=\"currentColor\" viewBox=\"0 0 32 32\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M26.259 23.289h-20.518c-1.193 0-2.16-0.967-2.16-2.16v0-14.588c0-1.193 0.967-2.16 2.16-2.16v0h20.518c1.193 0 2.16 0.967 2.16 2.16v0 14.588c0 1.193-0.967 2.16-2.16 2.16v0zM5.741 5.501c-0.574 0-1.040 0.466-1.040 1.040v0 14.588c0 0.574 0.466 1.040 1.040 1.040v0h20.518c0.574 0 1.040-0.466 1.040-1.040v0-14.588c0-0.574-0.466-1.040-1.040-1.040v0z\"\/><path d=\"M19.764 27.619h-7.53c-0.309 0-0.56-0.251-0.56-0.56v0c0-0.309 0.251-0.56 0.56-0.56h7.53c0.309 0 0.56 0.251 0.56 0.56v0c0 0.309-0.251 0.56-0.56 0.56v0z\"\/><path d=\"M14.023 9.322c0.116 0 0.223 0.035 0.312 0.096l-0.002-0.001 5.93 3.953c0.151 0.102 0.249 0.272 0.249 0.466s-0.098 0.364-0.247 0.465l-0.002 0.001-5.93 3.953c-0.087 0.059-0.195 0.094-0.31 0.094-0.309 0-0.56-0.25-0.56-0.559v-7.906c0-0.309 0.251-0.56 0.56-0.56v0zM18.943 13.835l-4.36-2.906v5.813z\"\/><\/svg>\n <\/span>\n <h2 class=\"scaler-courses__title\"><span class=\"ez-toc-section\" id=\"free-courses-by-top-scaler-instructors\"><\/span>Free Courses by top Scaler instructors<span class=\"ez-toc-section-end\"><\/span><\/h2>\n <\/div>\n\n <div class=\"scaler-courses__swiper-wrap\">\n <div class=\"swiper scaler-courses-swiper\">\n <div class=\"swiper-wrapper js-courses-wrapper\"><\/div>\n <div class=\"swiper-pagination\"><\/div>\n <\/div>\n\n <button class=\"scaler-courses__nav scaler-courses__nav--prev js-courses-prev\" aria-label=\"Previous\">\n <svg fill=\"currentColor\" viewBox=\"0 0 32 32\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"transform: rotate(-90deg);\"><path d=\"M5.313 16l10.688-10.688 10.688 10.688-1.938 1.875-7.438-7.438v16.25h-2.625v-16.25l-7.5 7.438z\"\/><\/svg>\n <\/button>\n <button class=\"scaler-courses__nav scaler-courses__nav--next js-courses-next\" aria-label=\"Next\">\n <svg fill=\"currentColor\" viewBox=\"0 0 32 32\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"transform: rotate(90deg);\"><path d=\"M5.313 16l10.688-10.688 10.688 10.688-1.938 1.875-7.438-7.438v16.25h-2.625v-16.25l-7.5 7.438z\"\/><\/svg>\n <\/button>\n <\/div>\n <\/div>\n <\/div>\n\n <script>\n document.addEventListener(\"DOMContentLoaded\", () => {\n const EXTRA_COUNT_ADDED = 1000;\n\n \/\/ Mirrors getInstructorStar(): first token of the headline.\n function getInstructorStar(headline) {\n return (headline || \"\").toString().split(\" \")[0] || \"\";\n }\n\n document.querySelectorAll(\".js-scaler-courses\").forEach(container => {\n if (container.dataset.initialized === \"true\") return;\n container.dataset.initialized = \"true\";\n\n const swiperElement = container.querySelector(\".scaler-courses-swiper\");\n const wrapper = container.querySelector(\".js-courses-wrapper\");\n const template = container.querySelector(\".js-course-card-template\");\n const nextBtn = container.querySelector(\".js-courses-next\");\n const prevBtn = container.querySelector(\".js-courses-prev\");\n\n if (!wrapper || !template) return;\n\n const swiper = new Swiper(swiperElement, {\n slidesPerView: \"auto\",\n spaceBetween: 16,\n grabCursor: true,\n observer: true,\n observeParents: true,\n navigation: { nextEl: nextBtn, prevEl: prevBtn },\n });\n\n const pathParts = location.pathname.split(\"\/\").filter(Boolean);\n const currentSlug = pathParts.length > 0 ? pathParts[pathParts.length - 1] : \"homepage\";\n\n function showSkeletons(count = 4) {\n wrapper.innerHTML = \"\";\n for (let i = 0; i < count; i++) {\n const clone = template.content.cloneNode(true);\n clone.querySelector(\".scaler-course-card\").classList.add(\"scaler-course-card--skeleton\");\n wrapper.appendChild(clone);\n }\n swiper.update();\n }\n\n function renderCourses(courses) {\n wrapper.innerHTML = \"\";\n if (!courses.length) { container.style.display = \"none\"; return; }\n\n courses.forEach(item => {\n const a = item.attributes || {};\n const clone = template.content.cloneNode(true);\n const instructor = (a.instructors_data && a.instructors_data[0]) || {};\n\n clone.querySelector(\".js-course-link\").href =\n `https:\/\/www.scaler.com\/topics\/course\/${a.slug}\/?utm_source=blog&utm_medium=free_courses&utm_content=${currentSlug}`;\n\n const img = clone.querySelector(\".js-course-image\");\n img.src = a.cover_image_thumbnail || a.featured_image || \"\";\n img.alt = a.title || \"\";\n\n clone.querySelector(\".js-course-title\").textContent = a.title || \"\";\n\n const instrEl = clone.querySelector(\".js-course-instructor\");\n if (instructor.name) {\n instrEl.innerHTML = \"A Course by <b><\/b>\";\n instrEl.querySelector(\"b\").textContent = instructor.name;\n } else {\n instrEl.style.display = \"none\";\n }\n\n clone.querySelector(\".js-course-desc\").textContent = a.description || \"\";\n\n \/\/ Star\n const star = getInstructorStar(instructor.star_headline);\n const starWrap = clone.querySelector(\".js-course-star\");\n if (star && star !== \"0\") clone.querySelector(\".js-course-star-val\").textContent = star;\n else starWrap.style.display = \"none\";\n\n \/\/ Enrolled count (+1000 like topics)\n clone.querySelector(\".js-course-reg\").textContent =\n ((a.user_registration_count || 0) + EXTRA_COUNT_ADDED).toLocaleString(\"en-US\");\n\n wrapper.appendChild(clone);\n });\n\n swiper.update();\n swiper.slideTo(0);\n }\n\n async function fetchCourses() {\n try {\n showSkeletons();\n \/\/ Public same-origin endpoint (topics course search). Returns 6 free courses.\n const res = await fetch(\"\/topics\/api\/v1\/search\/courses\", {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application\/json\",\n \"Accept\": \"application\/json\",\n \"X-Requested-With\": \"XMLHttpRequest\",\n },\n body: JSON.stringify({\n filters: {\n q: \"\",\n page: { limit: 6, current_page: 1 },\n sort: { key: \"user_registration_count\", order: \"desc\" },\n filter_type: null,\n tags: [],\n program: [],\n level: [],\n category_type: [],\n },\n }),\n });\n const json = await res.json();\n const courses = (json && json.data) || [];\n renderCourses(courses);\n } catch (error) {\n console.error(\"Failed to load courses:\", error);\n container.style.display = \"none\";\n }\n }\n\n fetchCourses();\n });\n });\n <\/script>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>5. Data Transformation<\/strong><\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code>from sklearn.preprocessing import StandardScaler, MinMaxScaler\n\n# Standardize numerical columns (zero mean, unit variance)\nscaler = StandardScaler()\ndf[[\"Age\", \"Score\"]] = scaler.fit_transform(df[[\"Age\", \"Score\"]])\n\n# Normalize numerical columns (range between 0 and 1)\nscaler = MinMaxScaler()\ndf[[\"Score\"]] = scaler.fit_transform(df[[\"Score\"]])\n\n# Encode categorical columns\ndf = pd.get_dummies(df, columns=[\"City\"])\n\n# Display the transformed dataframe\nprint(df.head())<\/code><\/pre>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Output:<\/strong><\/h4>\n\n\n\n<table id=\"tablepress-110\" class=\"tablepress tablepress-id-110 tablepress-responsive\">\n<thead>\n<tr class=\"row-1 odd\">\n\t<th class=\"column-1\"><strong>Name<\/strong><\/th><th class=\"column-2\"><strong>Age<\/strong><\/th><th class=\"column-3\"><strong>Score<\/strong><\/th><th class=\"column-4\"><strong>Country<\/strong><\/th><th class=\"column-5\"><strong>City_Chicago<\/strong><\/th><th class=\"column-6\"><strong>City_Dallas<\/strong><\/th><th class=\"column-7\"><strong>City_Houston<\/strong><\/th><th class=\"column-8\"><strong>City_Los Angeles<\/strong><\/th><th class=\"column-9\"><strong>City_New York<\/strong><\/th><th class=\"column-10\"><strong>City_Philadelphia<\/strong><\/th><th class=\"column-11\"><strong>City_Phoenix<\/strong><\/th><th class=\"column-12\"><strong>City_San Antonio<\/strong><\/th><th class=\"column-13\"><strong>City_San Diego<\/strong><\/th><th class=\"column-14\"><strong>City_San Jose<\/strong><\/th>\n<\/tr>\n<\/thead>\n<tbody class=\"row-hover\">\n<tr class=\"row-2 even\">\n\t<td class=\"column-1\">John Doe<\/td><td class=\"column-2\">-1.014185<\/td><td class=\"column-3\">0.388889<\/td><td class=\"column-4\">USA<\/td><td class=\"column-5\">0<\/td><td class=\"column-6\">0<\/td><td class=\"column-7\">0<\/td><td class=\"column-8\">0<\/td><td class=\"column-9\">1<\/td><td class=\"column-10\">0<\/td><td class=\"column-11\">0<\/td><td class=\"column-12\">0<\/td><td class=\"column-13\">0<\/td><td class=\"column-14\">0<\/td>\n<\/tr>\n<tr class=\"row-3 odd\">\n\t<td class=\"column-1\">Jane Smith<\/td><td class=\"column-2\">0.389607<\/td><td class=\"column-3\">0.833333<\/td><td class=\"column-4\">USA<\/td><td class=\"column-5\">0<\/td><td class=\"column-6\">0<\/td><td class=\"column-7\">0<\/td><td class=\"column-8\">1<\/td><td class=\"column-9\">0<\/td><td class=\"column-10\">0<\/td><td class=\"column-11\">0<\/td><td class=\"column-12\">0<\/td><td class=\"column-13\">0<\/td><td class=\"column-14\">0<\/td>\n<\/tr>\n<tr class=\"row-4 even\">\n\t<td class=\"column-1\">Bob Johnson<\/td><td class=\"column-2\">-0.149877<\/td><td class=\"column-3\">0<\/td><td class=\"column-4\">USA<\/td><td class=\"column-5\">1<\/td><td class=\"column-6\">0<\/td><td class=\"column-7\">0<\/td><td class=\"column-8\">0<\/td><td class=\"column-9\">0<\/td><td class=\"column-10\">0<\/td><td class=\"column-11\">0<\/td><td class=\"column-12\">0<\/td><td class=\"column-13\">0<\/td><td class=\"column-14\">0<\/td>\n<\/tr>\n<tr class=\"row-5 odd\">\n\t<td class=\"column-1\">Mike Davis<\/td><td class=\"column-2\">1.918889<\/td><td class=\"column-3\">0.666667<\/td><td class=\"column-4\">USA<\/td><td class=\"column-5\">0<\/td><td class=\"column-6\">0<\/td><td class=\"column-7\">0<\/td><td class=\"column-8\">0<\/td><td class=\"column-9\">0<\/td><td class=\"column-10\">1<\/td><td class=\"column-11\">0<\/td><td class=\"column-12\">0<\/td><td class=\"column-13\">0<\/td><td class=\"column-14\">0<\/td>\n<\/tr>\n<tr class=\"row-6 even\">\n\t<td class=\"column-1\">Emily Taylor<\/td><td class=\"column-2\">0.810293<\/td><td class=\"column-3\">1<\/td><td class=\"column-4\">USA<\/td><td class=\"column-5\">0<\/td><td class=\"column-6\">0<\/td><td class=\"column-7\">0<\/td><td class=\"column-8\">0<\/td><td class=\"column-9\">0<\/td><td class=\"column-10\">0<\/td><td class=\"column-11\">1<\/td><td class=\"column-12\">0<\/td><td class=\"column-13\">0<\/td><td class=\"column-14\">0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Remember:<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The specific data cleaning steps and techniques you choose will depend on the nature and characteristics of your dataset.<\/li>\n\n\n\n<li>Always carefully inspect and analyze your data before applying any transformations.<\/li>\n\n\n\n<li>Data cleaning is an iterative process, and you may need to experiment with different techniques to achieve the best results.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Join the tech revolution with <strong><a href=\"https:\/\/www.scaler.com\/courses\/machine-learning-course-training\/?utm_source=organic_blog&utm_medium=in_content_middle&utm_content=data-cleaning-in-machine-learning\" target=\"_blank\" rel=\"noreferrer noopener\">Scaler’s Machine Learning Course<\/a><\/strong>. Acquire the skills needed to excel in the rapidly advancing field of machine learning.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"data-cleaning-tools\"><\/span><strong>Data Cleaning Tools<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In the pursuit of accurate and reliable machine learning models, having the right tools for data cleaning is essential. To assist you in selecting the data cleaning tool that best meets your needs, let us examine a few widely used options, each with advantages and disadvantages of their own.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. OpenRefine<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">OpenRefine, formerly known as Google Refine, is an effective open-source tool for handling jumbled data. It allows you to explore, clean, transform, and reconcile data in various formats.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Pros:<\/strong> Free, versatile, handles large datasets, supports various data formats, offers powerful filtering and transformation capabilities.<\/li>\n\n\n\n<li><strong>Cons: <\/strong>Higher learning curve; this is mainly a desktop program that needs some technical understanding.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Trifacta Wrangler<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Trifacta Wrangler is a platform for visual data wrangling that makes cleaning and preparing data easier. It offers an interactive interface where you can explore, transform, and enrich data using a variety of visual tools and transformations.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Pros:<\/strong> Intuitive visual interface, easy to learn and use, powerful data profiling and transformation capabilities, supports various data sources.<\/li>\n\n\n\n<li><strong>Cons:<\/strong> Can be expensive for enterprise use, limited in handling very large datasets, and may require additional training for advanced features.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3. DataCleaner<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">An open-source program for preparing and enhancing data, DataCleaner is intended for both technical and non-technical users. It offers a range of features for data profiling, cleaning, validation, and enrichment.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Pros:<\/strong> Free, user-friendly interface, automated data profiling and cleaning suggestions support various data sources, and offers a visual workflow designer.<\/li>\n\n\n\n<li><strong>Cons: <\/strong>May need extra plugins for specialized tasks, limited ability to handle very large datasets, and possibly less comprehensive community support when compared to commercial tools.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>4. WinPure Clean & Match<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A full suite of software tools for data quality, including data cleansing, deduplication, and matching, is offered by WinPure Clean & Match. It’s designed to help businesses improve data accuracy, completeness, and consistency.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Pros:<\/strong> User-friendly interface, robust data cleansing and deduplication capabilities, fuzzy matching for identifying similar records, customizable workflows.<\/li>\n\n\n\n<li><strong>Cons:<\/strong> Can be expensive, primarily focused on customer data, and may not be suitable for all types of data cleaning tasks.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>5. Drake<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Drake is a Python data workflow tool that lets you create intricate pipelines for processing data. While not strictly a data-cleaning tool, it can be used to automate data-cleaning tasks within a broader workflow.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Pros:<\/strong> Python-based, flexible and customizable, ideal for automating repetitive data cleaning tasks, integrates well with other Python data science tools.<\/li>\n\n\n\n<li><strong>Cons:<\/strong> Requires programming knowledge, can be complex for non-technical users, the learning curve might be steep for those new to Python.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">These are just a few examples of the many data-cleaning tools available. The tool you choose will rely on your needs, financial situation, and level of technical proficiency. Experiment with different tools to find the one that best fits your workflow and empowers you to achieve data-cleaning success.<\/p>\n\n\n<style data-wp-block-html=\"css\">\n<style>\n :root {\n --sp-brand: #255aad; \/* brand blue *\/\n --sp-brand-hover: #19468c;\n --sp-card-bg: #ffffff; \/* neutral-scale-0 *\/\n --sp-surface: #f4f6f9; \/* neutral-scale-2 *\/\n --sp-pill-bg: #f8fafc;\n --sp-border: #d6dee5; \/* neutral-scale-4 *\/\n --sp-text-primary: #1a2c47; \/* neutral-scale-12 *\/\n --sp-text-secondary: #61738e; \/* neutral-scale-9 *\/\n }\n\n .scaler-placement * { box-sizing: border-box; }\n\n .scaler-placement {\n font-family: \"Source Sans 3\", \"Source Sans Pro\", \"Roboto\", Arial, sans-serif;\n width: 100%;\n max-width: 100%;\n margin: 24px auto;\n display: flex;\n flex-direction: column;\n }\n\n \/* ---- Placement report (OnlineCallout) ---- *\/\n .scaler-placement__title {\n font-size: 24px;\n font-weight: 600;\n line-height: 1.3;\n color: var(--sp-text-primary);\n margin: 0 0 16px;\n }\n\n .scaler-placement__card {\n display: flex;\n flex-direction: column;\n width: 100%;\n background: var(--sp-card-bg);\n border: 1px solid var(--sp-border);\n border-radius: 20px;\n padding: 16px;\n box-shadow: 0 8px 32px rgba(37, 90, 173, 0.04);\n overflow: hidden;\n gap: 5px;\n }\n\n .scaler-placement__top {\n display: flex;\n flex-direction: row;\n align-items: center;\n width: 100%;\n gap: 18px;\n }\n\n .scaler-placement__stat-block {\n display: flex;\n flex-direction: column;\n justify-content: center;\n align-items: center;\n width: 30%;\n min-width: 200px;\n flex-shrink: 0;\n background: var(--sp-surface);\n border: 1px solid var(--sp-border);\n border-radius: 16px;\n padding: 16px 10px;\n gap: 8px;\n }\n .scaler-placement__stat-number {\n font-size: 48px;\n font-weight: 600;\n line-height: 1;\n color: var(--sp-brand);\n text-align: center;\n }\n .scaler-placement__stat-label {\n font-size: 13px;\n font-weight: 600;\n line-height: 14px;\n color: rgba(37, 90, 173, 0.85);\n text-transform: uppercase;\n letter-spacing: 0.8px;\n }\n\n .scaler-placement__info {\n display: flex;\n flex-direction: column;\n justify-content: center;\n flex: 1;\n gap: 10px;\n }\n .scaler-placement__proof-header {\n display: flex;\n flex-direction: row;\n align-items: center;\n gap: 8px;\n }\n .scaler-placement__proof-header svg { width: 18px; height: 18px; flex-shrink: 0; }\n .scaler-placement__proof-label {\n font-size: 12px;\n font-weight: 600;\n line-height: 16px;\n color: var(--sp-brand);\n text-transform: uppercase;\n letter-spacing: 1px;\n }\n .scaler-placement__body-text {\n font-size: 16px;\n font-weight: 400;\n line-height: 24px;\n color: var(--sp-text-primary);\n margin: 0;\n }\n\n .scaler-placement__bottom {\n display: flex;\n flex-direction: column;\n align-items: flex-start;\n margin-left: calc(30% + 18px);\n width: calc(70% - 18px);\n gap: 12px;\n }\n .scaler-placement__stats-row {\n display: flex;\n flex-direction: row;\n align-items: center;\n gap: 24px;\n }\n .scaler-placement__stat-item {\n display: flex;\n flex-direction: row;\n align-items: center;\n gap: 8px;\n }\n .scaler-placement__stat-item > svg {\n color: var(--sp-brand);\n width: 18px;\n height: 18px;\n flex-shrink: 0;\n }\n .scaler-placement__stat-text {\n display: flex;\n flex-direction: row;\n align-items: center;\n gap: 4px;\n }\n .scaler-placement__stat-value {\n font-size: 16px;\n font-weight: 600;\n line-height: 24px;\n color: var(--sp-text-primary);\n }\n .scaler-placement__stat-desc {\n font-size: 14px;\n font-weight: 400;\n line-height: 20px;\n color: var(--sp-text-secondary);\n }\n\n .scaler-placement__cta,\n .scaler-placement__cta:link,\n .scaler-placement__cta:visited {\n display: inline-flex;\n align-items: center;\n align-self: flex-start;\n font-size: 14px;\n font-weight: 600;\n line-height: 20px;\n color: var(--sp-brand) !important;\n gap: 6px;\n text-decoration: none;\n cursor: pointer;\n margin-top: 2px;\n transition: color 0.25s ease;\n }\n .scaler-placement__cta:hover,\n .scaler-placement__cta:focus { color: var(--sp-brand-hover) !important; }\n .scaler-placement__cta svg {\n width: 14px;\n height: 14px;\n fill: currentColor;\n transition: transform 0.25s ease;\n }\n .scaler-placement__cta:hover svg { transform: translateX(3px); }\n\n \/* ---- Hiring Partners ---- *\/\n .scaler-hiring {\n display: flex;\n align-items: center;\n justify-content: center;\n flex-wrap: wrap;\n gap: 8px 12px;\n margin: 24px 0 8px;\n padding-top: 16px;\n border-top: 1px solid var(--sp-border);\n }\n .scaler-hiring__label {\n font-size: 14px;\n color: var(--sp-text-secondary);\n white-space: nowrap;\n }\n .scaler-hiring__pills {\n display: flex;\n align-items: center;\n flex-wrap: wrap;\n gap: 8px;\n }\n .scaler-hiring__pill {\n display: inline-flex;\n align-items: center;\n gap: 6px;\n padding: 5px 12px 5px 5px;\n background: var(--sp-pill-bg);\n border-radius: 999px;\n }\n .scaler-hiring__badge {\n display: inline-flex;\n align-items: center;\n justify-content: center;\n flex-shrink: 0;\n width: 24px;\n height: 24px;\n border-radius: 50%;\n background: #ffffff;\n }\n .scaler-hiring__logo {\n width: 16px !important;\n height: 16px !important;\n object-fit: contain;\n margin: 0 !important;\n display: block;\n }\n .scaler-hiring__name {\n font-size: 14px;\n font-weight: 600;\n color: var(--sp-text-primary);\n }\n .scaler-hiring__more {\n font-size: 14px;\n color: var(--sp-text-secondary);\n }\n\n @media (max-width: 768px) {\n .scaler-placement__title { font-size: 18px; margin-bottom: 12px; }\n .scaler-placement__card { gap: 16px; border-radius: 16px; }\n .scaler-placement__top { gap: 12px; }\n .scaler-placement__stat-block { width: 35%; min-width: 110px; padding: 12px 8px; gap: 4px; }\n .scaler-placement__stat-number { font-size: 24px; }\n .scaler-placement__stat-label { font-size: 10px; }\n .scaler-placement__body-text { font-size: 14px; line-height: 20px; }\n .scaler-placement__bottom { margin-left: 0; width: 100%; gap: 16px; }\n .scaler-placement__stats-row { flex-wrap: wrap; gap: 10px 16px; }\n .scaler-placement__stat-value { font-size: 14px; line-height: 20px; }\n .scaler-placement__stat-desc { font-size: 12px; line-height: 16px; }\n }\n<\/style>\n\n<meta charset=\"UTF-8\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n\n <!-- Source Sans Pro (topics font) -->\n <link href=\"https:\/\/fonts.googleapis.com\/css2?family=Source+Sans+3:wght@400;600;700&display=swap\" rel=\"stylesheet\">\n\n<div class=\"scaler-placement\">\n <h2 class=\"scaler-placement__title\"><span class=\"ez-toc-section\" id=\"scaler-placement-report-and-statistics\"><\/span>Scaler Placement Report and Statistics<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n <div class=\"scaler-placement__card\">\n <div class=\"scaler-placement__top\">\n <div class=\"scaler-placement__stat-block\">\n <div class=\"scaler-placement__stat-number\">\u20b923L<\/div>\n <div class=\"scaler-placement__stat-label\">AVG CTC<\/div>\n <\/div>\n <div class=\"scaler-placement__info\">\n <div class=\"scaler-placement__proof-header\">\n <svg viewBox=\"0 0 21 21\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M8.64 0 0 8.683V21h12.257l8.64-8.683V0H8.64zm-.11 17.257H3.725v-4.828H8.53v4.828zm7.901-1.88-4.174 4.194V8.684H1.447l4.076-4.14h10.914l-.006 10.83v.004z\" fill=\"#255aad\"><\/path><\/svg>\n <span class=\"scaler-placement__proof-label\">SCALER PLACEMENT PROOF<\/span>\n <\/div>\n <p class=\"scaler-placement__body-text\">\n Scaler learners achieved 2.5x salary growth with average post-Scaler CTC reaching \u20b923L.\n <\/p>\n <\/div>\n <\/div>\n\n <div class=\"scaler-placement__bottom\">\n <div class=\"scaler-placement__stats-row\">\n <div class=\"scaler-placement__stat-item\">\n <svg fill=\"currentColor\" viewBox=\"0 0 32 32\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M11.199 7.957c-3.086 0-5.588 2.502-5.588 5.588s2.502 5.588 5.588 5.588c3.086 0 5.588-2.502 5.588-5.588s-2.502-5.588-5.588-5.588zM3.611 13.545c0-4.191 3.397-7.588 7.588-7.588s7.588 3.397 7.588 7.588c0 4.191-3.397 7.588-7.588 7.588s-7.588-3.397-7.588-7.588z\"><\/path><path d=\"M21.090 6.369h0.003c1.989 0 3.897 0.79 5.303 2.197s2.197 3.314 2.197 5.303-0.79 3.897-2.197 5.303c-1.406 1.407-3.314 2.197-5.303 2.197-0.552 0-1-0.448-1-1s0.448-1 1-1c1.459 0 2.858-0.579 3.889-1.611s1.611-2.43 1.611-3.889c0-1.459-0.58-2.858-1.611-3.889s-2.429-1.611-3.888-1.611c-0.507 0.002-1.013 0.070-1.502 0.203-0.533 0.145-1.082-0.17-1.227-0.703s0.17-1.082 0.703-1.227c0.659-0.179 1.339-0.27 2.021-0.272z\"><\/path><path d=\"M10.906 21.364c-1.605 0-3.186 0.386-4.61 1.126s-2.649 1.811-3.572 3.124c-0.318 0.452-0.941 0.561-1.393 0.243s-0.561-0.941-0.243-1.393c1.107-1.576 2.577-2.862 4.286-3.749s3.606-1.351 5.532-1.351 3.823 0.463 5.532 1.351c1.709 0.888 3.179 2.174 4.286 3.749 0.318 0.452 0.209 1.076-0.243 1.393s-1.076 0.209-1.393-0.243c-0.923-1.313-2.148-2.385-3.572-3.124s-3.005-1.126-4.61-1.126z\"><\/path><path d=\"M25.704 22.493c-1.424-0.74-3.005-1.125-4.61-1.124-0.552 0-1-0.447-1.001-0.999s0.447-1 0.999-1.001c1.926-0.001 3.824 0.462 5.533 1.349s3.179 2.174 4.286 3.751c0.317 0.452 0.208 1.076-0.244 1.393s-1.076 0.208-1.393-0.244c-0.922-1.313-2.147-2.385-3.571-3.125z\"><\/path><\/svg>\n <div class=\"scaler-placement__stat-text\">\n <span class=\"scaler-placement__stat-value\">11,000+<\/span>\n <span class=\"scaler-placement__stat-desc\">placements<\/span>\n <\/div>\n <\/div>\n <div class=\"scaler-placement__stat-item\">\n <svg fill=\"currentColor\" viewBox=\"0 0 32 32\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M1.2 27.5c0-0.442 0.358-0.8 0.8-0.8h28c0.442 0 0.8 0.358 0.8 0.8s-0.358 0.8-0.8 0.8h-28c-0.442 0-0.8-0.358-0.8-0.8z\"><\/path><path d=\"M3.727 4.227c0.338-0.338 0.795-0.527 1.273-0.527h12c0.477 0 0.935 0.19 1.273 0.527s0.527 0.795 0.527 1.273v22c0 0.442-0.358 0.8-0.8 0.8s-0.8-0.358-0.8-0.8v-22c0-0.053-0.021-0.104-0.059-0.141s-0.088-0.059-0.141-0.059h-12c-0.053 0-0.104 0.021-0.141 0.059s-0.059 0.088-0.059 0.141v22c0 0.442-0.358 0.8-0.8 0.8s-0.8-0.358-0.8-0.8v-22c0-0.477 0.19-0.935 0.527-1.273z\"><\/path><path d=\"M17.2 12.5c0-0.442 0.358-0.8 0.8-0.8h9c0.477 0 0.935 0.19 1.273 0.527s0.527 0.795 0.527 1.273v14c0 0.442-0.358 0.8-0.8 0.8s-0.8-0.358-0.8-0.8v-14c0-0.053-0.021-0.104-0.059-0.141s-0.088-0.059-0.141-0.059h-9c-0.442 0-0.8-0.358-0.8-0.8z\"><\/path><path d=\"M7.2 9.5c0-0.442 0.358-0.8 0.8-0.8h4c0.442 0 0.8 0.358 0.8 0.8s-0.358 0.8-0.8 0.8h-4c-0.442 0-0.8-0.358-0.8-0.8z\"><\/path><path d=\"M9.2 17.5c0-0.442 0.358-0.8 0.8-0.8h4c0.442 0 0.8 0.358 0.8 0.8s-0.358 0.8-0.8 0.8h-4c-0.442 0-0.8-0.358-0.8-0.8z\"><\/path><path d=\"M7.2 22.5c0-0.442 0.358-0.8 0.8-0.8h4c0.442 0 0.8 0.358 0.8 0.8s-0.358 0.8-0.8 0.8h-4c-0.442 0-0.8-0.358-0.8-0.8z\"><\/path><path d=\"M21.2 22.5c0-0.442 0.358-0.8 0.8-0.8h2c0.442 0 0.8 0.358 0.8 0.8s-0.358 0.8-0.8 0.8h-2c-0.442 0-0.8-0.358-0.8-0.8z\"><\/path><path d=\"M21.2 17.5c0-0.442 0.358-0.8 0.8-0.8h2c0.442 0 0.8 0.358 0.8 0.8s-0.358 0.8-0.8 0.8h-2c-0.442 0-0.8-0.358-0.8-0.8z\"><\/path><\/svg>\n <div class=\"scaler-placement__stat-text\">\n <span class=\"scaler-placement__stat-value\">650+<\/span>\n <span class=\"scaler-placement__stat-desc\">companies<\/span>\n <\/div>\n <\/div>\n <div class=\"scaler-placement__stat-item\">\n <svg viewBox=\"0 0 25 24\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M5.60938 18.8906C4.74688 18.0281 5.31875 16.2188 4.87813 15.1594C4.4375 14.1 2.75 13.1719 2.75 12C2.75 10.8281 4.41875 9.9375 4.87813 8.84063C5.3375 7.74375 4.74688 5.97187 5.60938 5.10938C6.47187 4.24688 8.28125 4.81875 9.34063 4.37813C10.4 3.9375 11.3281 2.25 12.5 2.25C13.6719 2.25 14.5625 3.91875 15.6594 4.37813C16.7562 4.8375 18.5281 4.24688 19.3906 5.10938C20.2531 5.97187 19.6812 7.78125 20.1219 8.84063C20.5625 9.9 22.25 10.8281 22.25 12C22.25 13.1719 20.5813 14.0625 20.1219 15.1594C19.6625 16.2562 20.2531 18.0281 19.3906 18.8906C18.5281 19.7531 16.7188 19.1812 15.6594 19.6219C14.6 20.0625 13.6719 21.75 12.5 21.75C11.3281 21.75 10.4375 20.0813 9.34063 19.6219C8.24375 19.1625 6.47187 19.7531 5.60938 18.8906Z\" fill=\"#ffffff\" stroke=\"#255aad\" stroke-width=\"1.4\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><\/path><path d=\"M16.625 9.75L11.1219 15L8.375 12.375\" stroke=\"#255aad\" stroke-width=\"1.4\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><\/path><\/svg>\n <span class=\"scaler-placement__stat-desc\">Verified data<\/span>\n <\/div>\n <\/div>\n\n <a class=\"scaler-placement__cta\" href=\"https:\/\/d2beiqkhq929f0.cloudfront.net\/public_assets\/assets\/000\/195\/869\/original\/Scaler_Career_Report.pdf?1778499863\" target=\"_blank\" rel=\"noopener\">\n See full placement report\n <svg viewBox=\"0 0 32 32\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"transform: rotate(90deg);\"><path d=\"M5.313 16l10.688-10.688 10.688 10.688-1.938 1.875-7.438-7.438v16.25h-2.625v-16.25l-7.5 7.438z\"><\/path><\/svg>\n <\/a>\n <\/div>\n <\/div>\n\n <div class=\"scaler-hiring\">\n <span class=\"scaler-hiring__label\">Hiring Partners:<\/span>\n <div class=\"scaler-hiring__pills\">\n <span class=\"scaler-hiring__pill\">\n <span class=\"scaler-hiring__badge\"><img decoding=\"async\" class=\"scaler-hiring__logo\" src=\"https:\/\/d19zaobb0foqu6.cloudfront.net\/topics\/images\/googleicon_.svg\" alt=\"Google\" loading=\"lazy\"><\/span>\n <span class=\"scaler-hiring__name\">Google<\/span>\n <\/span>\n <span class=\"scaler-hiring__pill\">\n <span class=\"scaler-hiring__badge\"><img decoding=\"async\" class=\"scaler-hiring__logo\" src=\"https:\/\/d19zaobb0foqu6.cloudfront.net\/topics\/images\/amazonicon_.svg\" alt=\"Amazon\" loading=\"lazy\"><\/span>\n <span class=\"scaler-hiring__name\">Amazon<\/span>\n <\/span>\n <span class=\"scaler-hiring__pill\">\n <span class=\"scaler-hiring__badge\"><img decoding=\"async\" class=\"scaler-hiring__logo\" src=\"https:\/\/d19zaobb0foqu6.cloudfront.net\/topics\/images\/microsoft.svg\" alt=\"Microsoft\" loading=\"lazy\"><\/span>\n <span class=\"scaler-hiring__name\">Microsoft<\/span>\n <\/span>\n <span class=\"scaler-hiring__pill\">\n <span class=\"scaler-hiring__badge\"><img decoding=\"async\" class=\"scaler-hiring__logo\" src=\"https:\/\/d19zaobb0foqu6.cloudfront.net\/topics\/images\/flipkarticon_.svg\" alt=\"Flipkart\" loading=\"lazy\"><\/span>\n <span class=\"scaler-hiring__name\">Flipkart<\/span>\n <\/span>\n <span class=\"scaler-hiring__pill\">\n <span class=\"scaler-hiring__badge\"><img decoding=\"async\" class=\"scaler-hiring__logo\" src=\"https:\/\/d19zaobb0foqu6.cloudfront.net\/topics\/images\/adobeicon_.svg\" alt=\"Adobe\" loading=\"lazy\"><\/span>\n <span class=\"scaler-hiring__name\">Adobe<\/span>\n <\/span>\n <span class=\"scaler-hiring__more\">1200+ more<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"advantages-and-disadvantages-of-data-cleaning-in-machine-learning\"><\/span><strong>Advantages and Disadvantages of Data Cleaning in Machine Learning<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>In the field of machine learning, data cleaning has pros and cons. It has enormous advantages but also presents certain difficulties. <\/strong>Understanding these trade-offs is crucial for making informed decisions about how to approach data cleaning in your projects.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Advantages<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Improved Data Quality:<\/strong> The most obvious benefit of data cleaning is the improvement in data quality. You can produce a dataset that is more accurate, dependable, and representative of the real-world phenomena you are researching by eliminating mistakes, inconsistencies, and unnecessary information.<\/li>\n\n\n\n<li><strong>Enhanced Model Performance:<\/strong> Clean data leads to better machine-learning models. When your model is trained on high-quality data, it's more likely to learn the true patterns and relationships within the data, resulting in more accurate predictions and better decision-making.<\/li>\n\n\n\n<li><strong>Deeper Insights:<\/strong> Clean data allows you to uncover deeper insights and discover hidden patterns that may have been obscured by noise and errors. This can lead to new discoveries, improved understanding of your data, and ultimately, better outcomes.<\/li>\n\n\n\n<li><strong>More Efficient Analysis:<\/strong> By removing irrelevant and redundant information, data cleaning simplifies your dataset, making it easier to analyze and visualize. By doing this, you can free up important time and resources to concentrate on gaining insightful knowledge.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Disadvantages<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Time-Consuming:<\/strong> Data cleaning can be a time-consuming process, especially when dealing with large and complex datasets. It frequently calls for manual data validation, correction, and inspection, which can be time-consuming and difficult.<\/li>\n\n\n\n<li><strong>Requires Expertise:<\/strong> Effective data cleaning requires domain knowledge and expertise. A thorough understanding of the topic matter and data analysis principles is necessary to recognize potential errors and inconsistencies, understand the subtleties of your data, and select the appropriate cleaning techniques.<\/li>\n\n\n\n<li><strong>Potential for Bias:<\/strong> If not done carefully, data cleaning can introduce bias into your dataset. The underlying distribution of the data may be slightly altered by eliminating outliers or imputing missing values using assumptions, which may produce skewed results.<\/li>\n\n\n\n<li><strong>Not a One-Size-Fits-All Solution:<\/strong> There is no single \"best\" way to clean data. The best strategy will vary depending on your dataset's unique properties, your analysis objectives, and the resources at your disposal.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">To gain a comprehensive understanding of data cleaning techniques and best practices, consider enrolling in <a href=\"https:\/\/www.scaler.com\/courses\/machine-learning-course-training\/?utm_source=organic_blog&utm_medium=in_content_footer&utm_content=data-cleaning-in-machine-learning\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Scaler's Machine Learning Course<\/strong><\/a>. With expert-led instruction, practical projects, and one-on-one mentoring, this course will give you the knowledge and abilities you need to become a machine learning pro at data cleaning.<\/p>\n\n\n<meta charset=\"UTF-8\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n\n <!-- Source Sans Pro (topics font) -->\n <link href=\"https:\/\/fonts.googleapis.com\/css2?family=Source+Sans+3:wght@400;600;700&display=swap\" rel=\"stylesheet\">\n <link rel=\"stylesheet\" href=\"https:\/\/cdn.jsdelivr.net\/npm\/swiper@11\/swiper-bundle.min.css\">\n <script src=\"https:\/\/cdn.jsdelivr.net\/npm\/swiper@11\/swiper-bundle.min.js\"><\/script>\n\n <style>\n :root {\n --ss-primary: #005bb3; \/* primary-color-80 *\/\n --ss-card-bg: #ffffff;\n --ss-border: #e6e6e6;\n --ss-quote-bg: #f6f6f6;\n --ss-text-dark: #1c1c1c;\n --ss-text-mid: #6b6b6b;\n --ss-text-muted: #9a9a9a;\n --ss-radius: 12px;\n --ss-radius-sm: 8px;\n --ss-shadow: 0 8px 16px 0 rgba(50, 71, 102, 0.16);\n }\n\n .scaler-ss * { box-sizing: border-box; }\n\n .scaler-ss {\n font-family: \"Source Sans 3\", \"Source Sans Pro\", \"Roboto\", Arial, sans-serif;\n padding: 24px;\n width: 100%;\n }\n .scaler-ss__inner {\n max-width: 1000px;\n margin: 0 auto;\n position: relative;\n width: 100%;\n }\n\n \/* Heading *\/\n .scaler-ss__heading {\n font-size: 26px;\n line-height: 34px;\n font-weight: 700;\n margin: 0 0 16px;\n text-align: center;\n color: var(--ss-text-dark);\n }\n\n \/* Swiper *\/\n .scaler-ss__swiper-wrap { position: relative; }\n .scaler-ss .swiper { width: 100%; padding: 4px; }\n .scaler-ss-swiper .swiper-slide {\n width: 300px; \/* topics card width *\/\n height: auto;\n display: flex;\n }\n\n \/* Card *\/\n .scaler-ss-card {\n display: flex;\n flex-direction: column;\n width: 100%;\n border: 1px solid var(--ss-border);\n border-radius: var(--ss-radius);\n overflow: hidden;\n background: var(--ss-card-bg);\n }\n\n .scaler-ss-card__hike {\n padding: 8px 12px;\n background: var(--ss-primary);\n color: #ffffff;\n font-size: 14px;\n font-weight: 700;\n text-align: center;\n letter-spacing: 0.4px;\n }\n\n .scaler-ss-card__body {\n display: flex;\n flex: 1 1 auto;\n flex-direction: column;\n gap: 12px;\n padding: 16px;\n }\n\n \/* Person *\/\n .scaler-ss-card__person {\n display: flex;\n align-items: center;\n gap: 10px;\n }\n .scaler-ss-card__avatar {\n width: 44px;\n height: 44px;\n min-width: 44px;\n border-radius: 50%;\n object-fit: cover;\n background: var(--ss-quote-bg);\n }\n .scaler-ss-card__name {\n font-size: 16px;\n font-weight: 700;\n line-height: 20px;\n color: var(--ss-text-dark);\n }\n .scaler-ss-card__city {\n font-size: 13px;\n line-height: 18px;\n color: var(--ss-text-mid);\n }\n\n \/* Before \/ Now transition *\/\n .scaler-ss-card__transition {\n display: flex;\n align-items: center;\n justify-content: space-between;\n gap: 8px;\n }\n .scaler-ss-card__col {\n flex: 1;\n min-width: 0;\n }\n .scaler-ss-card__col-label {\n font-size: 11px;\n font-weight: 600;\n letter-spacing: 0.4px;\n color: var(--ss-text-muted);\n margin-bottom: 2px;\n }\n .scaler-ss-card__col-role {\n font-size: 13px;\n line-height: 18px;\n color: var(--ss-text-mid);\n }\n .scaler-ss-card__col-company {\n font-size: 14px;\n font-weight: 700;\n line-height: 20px;\n color: var(--ss-text-dark);\n }\n .scaler-ss-card__company-logo {\n margin-top: 2px;\n max-width: 64px;\n max-height: 20px;\n width: auto;\n height: auto;\n object-fit: contain;\n display: block;\n }\n .scaler-ss-card__arrow {\n flex-shrink: 0;\n width: 28px;\n height: 8px;\n color: var(--ss-text-mid);\n fill: currentColor;\n }\n\n \/* Quote *\/\n .scaler-ss-card__quote {\n font-size: 13px;\n font-style: italic;\n line-height: 19px;\n color: var(--ss-text-mid);\n background: var(--ss-quote-bg);\n border-radius: var(--ss-radius-sm);\n padding: 8px 10px;\n }\n\n \/* View Profile *\/\n .scaler-ss-card__cta,\n .scaler-ss-card__cta:link,\n .scaler-ss-card__cta:visited {\n display: block;\n margin-top: auto;\n text-align: center;\n font-size: 14px;\n font-weight: 600;\n color: var(--ss-primary) !important;\n text-decoration: none;\n border: 1px solid var(--ss-primary);\n border-radius: var(--ss-radius-sm);\n padding: 9px 12px;\n cursor: pointer;\n transition: background 0.2s ease, color 0.2s ease;\n }\n .scaler-ss-card__cta:hover,\n .scaler-ss-card__cta:focus {\n background: var(--ss-primary);\n color: #ffffff !important;\n }\n\n \/* Side navigation arrows *\/\n .scaler-ss__nav {\n position: absolute !important;\n top: 50% !important;\n transform: translateY(-50%) !important;\n z-index: 2;\n width: 46px !important;\n height: 46px !important;\n min-width: 46px;\n display: flex !important;\n align-items: center;\n justify-content: center;\n padding: 0 !important;\n background: var(--ss-card-bg) !important;\n border: none !important;\n border-radius: var(--ss-radius-sm) !important;\n box-shadow: var(--ss-shadow) !important;\n color: #61738e !important;\n cursor: pointer;\n transition: opacity 0.2s ease, visibility 0.2s ease;\n }\n .scaler-ss__nav--prev { left: 8px !important; }\n .scaler-ss__nav--next { right: 8px !important; }\n .scaler-ss__nav svg {\n width: 20px !important;\n height: 20px !important;\n fill: #61738e !important;\n }\n .scaler-ss__nav.swiper-button-disabled {\n opacity: 0;\n visibility: hidden;\n pointer-events: none;\n }\n\n .scaler-ss .swiper-button-prev,\n .scaler-ss .swiper-button-next,\n .scaler-ss .swiper-pagination { display: none; }\n\n \/* Skeleton *\/\n .scaler-ss-card--skeleton { pointer-events: none; }\n .scaler-ss-card--skeleton .scaler-ss-card__hike,\n .scaler-ss-card--skeleton .scaler-ss-card__avatar,\n .scaler-ss-card--skeleton .scaler-ss-card__person-text,\n .scaler-ss-card--skeleton .scaler-ss-card__transition,\n .scaler-ss-card--skeleton .scaler-ss-card__quote,\n .scaler-ss-card--skeleton .scaler-ss-card__cta {\n background: linear-gradient(90deg, var(--ss-border) 25%, var(--ss-quote-bg) 50%, var(--ss-border) 75%);\n background-size: 200% 100%;\n animation: ss-shimmer 1.5s infinite;\n color: transparent !important;\n border-color: transparent !important;\n border-radius: 4px;\n }\n .scaler-ss-card--skeleton .scaler-ss-card__person-text { flex: 1; height: 32px; }\n .scaler-ss-card--skeleton .scaler-ss-card__transition { height: 56px; }\n .scaler-ss-card--skeleton .scaler-ss-card__quote { height: 48px; }\n .scaler-ss-card--skeleton .scaler-ss-card__cta { height: 38px; }\n\n @keyframes ss-shimmer {\n 0% { background-position: 200% 0; }\n 100% { background-position: -200% 0; }\n }\n\n @media (max-width: 768px) {\n .scaler-ss { padding: 16px; }\n .scaler-ss__heading { font-size: 22px; line-height: 30px; }\n .scaler-ss-swiper .swiper-slide { width: 270px; }\n .scaler-ss__nav { display: none !important; }\n }\n <\/style>\n\n\n\n <div class=\"scaler-ss js-scaler-ss\">\n\n <template class=\"js-ss-card-template\">\n <div class=\"swiper-slide\">\n <article class=\"scaler-ss-card\">\n <div class=\"scaler-ss-card__hike js-ss-hike\"><\/div>\n <div class=\"scaler-ss-card__body\">\n <div class=\"scaler-ss-card__person\">\n <img decoding=\"async\" class=\"scaler-ss-card__avatar js-ss-avatar\" src=\"\" alt=\"\" loading=\"lazy\">\n <div class=\"scaler-ss-card__person-text\">\n <div class=\"scaler-ss-card__name js-ss-name\"><\/div>\n <div class=\"scaler-ss-card__city js-ss-city\"><\/div>\n <\/div>\n <\/div>\n\n <div class=\"scaler-ss-card__transition\">\n <div class=\"scaler-ss-card__col\">\n <div class=\"scaler-ss-card__col-label\">BEFORE:<\/div>\n <div class=\"scaler-ss-card__col-role js-ss-before-role\"><\/div>\n <div class=\"scaler-ss-card__col-company js-ss-before-company\"><\/div>\n <\/div>\n <svg class=\"scaler-ss-card__arrow\" viewBox=\"0 0 32 8\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M31.3882 4.35355C31.5835 4.15829 31.5835 3.84171 31.3882 3.64645L28.2062 0.464466C28.011 0.269204 27.6944 0.269204 27.4991 0.464466C27.3039 0.659728 27.3039 0.976311 27.4991 1.17157L30.3276 4L27.4991 6.82843C27.3039 7.02369 27.3039 7.34027 27.4991 7.53553C27.6944 7.7308 28.011 7.7308 28.2062 7.53553L31.3882 4.35355ZM0.034668 4.5H31.0347V3.5H0.034668V4.5Z\"><\/path><\/svg>\n <div class=\"scaler-ss-card__col\">\n <div class=\"scaler-ss-card__col-label\">NOW:<\/div>\n <div class=\"scaler-ss-card__col-role js-ss-now-role\"><\/div>\n <div class=\"js-ss-now-company\"><\/div>\n <\/div>\n <\/div>\n\n <div class=\"scaler-ss-card__quote js-ss-quote\"><\/div>\n\n <a class=\"scaler-ss-card__cta js-ss-cta\" href=\"#\" target=\"_blank\" rel=\"noopener\">View Profile<\/a>\n <\/div>\n <\/article>\n <\/div>\n <\/template>\n\n <div class=\"scaler-ss__inner\">\n <h2 class=\"scaler-ss__heading\"><span class=\"ez-toc-section\" id=\"scaler-alumni-and-their-success-stories\"><\/span>Scaler Alumni and Their Success Stories<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n <div class=\"scaler-ss__swiper-wrap\">\n <div class=\"swiper scaler-ss-swiper\">\n <div class=\"swiper-wrapper js-ss-wrapper\"><\/div>\n <\/div>\n\n <button class=\"scaler-ss__nav scaler-ss__nav--prev js-ss-prev\" aria-label=\"Previous\">\n <svg fill=\"currentColor\" viewBox=\"0 0 32 32\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"transform: rotate(-90deg);\"><path d=\"M5.313 16l10.688-10.688 10.688 10.688-1.938 1.875-7.438-7.438v16.25h-2.625v-16.25l-7.5 7.438z\"><\/path><\/svg>\n <\/button>\n <button class=\"scaler-ss__nav scaler-ss__nav--next js-ss-next\" aria-label=\"Next\">\n <svg fill=\"currentColor\" viewBox=\"0 0 32 32\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"transform: rotate(90deg);\"><path d=\"M5.313 16l10.688-10.688 10.688 10.688-1.938 1.875-7.438-7.438v16.25h-2.625v-16.25l-7.5 7.438z\"><\/path><\/svg>\n <\/button>\n <\/div>\n <\/div>\n <\/div>\n\n <script>\n document.addEventListener(\"DOMContentLoaded\", () => {\n\n const ASSET_CDN = \"https:\/\/d2beiqkhq929f0.cloudfront.net\/public_assets\/assets\";\n const AVATAR_FALLBACK =\n \"data:image\/svg+xml,%3Csvg xmlns='http:\/\/www.w3.org\/2000\/svg' width='44' height='44' viewBox='0 0 44 44'%3E%3Ccircle cx='22' cy='22' r='22' fill='%23e6e6e6'\/%3E%3Ccircle cx='22' cy='17' r='7' fill='%23b9b9b9'\/%3E%3Cpath d='M8 40c0-8 6.3-13 14-13s14 5 14 13' fill='%23b9b9b9'\/%3E%3C\/svg%3E\";\n\n \/\/ Mirrors buildAssetUrl(): pass through absolute urls, else build the CDN url.\n function buildAssetUrl(raw) {\n if (!raw) return \"\";\n if (String(raw).startsWith(\"http\")) return raw;\n return `${ASSET_CDN}\/${raw}\/original`;\n }\n\n const instances = document.querySelectorAll(\".js-scaler-ss\");\n\n instances.forEach(container => {\n if (container.dataset.initialized === \"true\") return;\n container.dataset.initialized = \"true\";\n\n const swiperElement = container.querySelector(\".scaler-ss-swiper\");\n const wrapper = container.querySelector(\".js-ss-wrapper\");\n const template = container.querySelector(\".js-ss-card-template\");\n const nextBtn = container.querySelector(\".js-ss-next\");\n const prevBtn = container.querySelector(\".js-ss-prev\");\n\n if (!wrapper || !template) {\n console.error(\"Scaler Success Stories: missing required elements\");\n return;\n }\n\n const swiper = new Swiper(swiperElement, {\n slidesPerView: \"auto\",\n spaceBetween: 16,\n grabCursor: true,\n observer: true,\n observeParents: true,\n navigation: { nextEl: nextBtn, prevEl: prevBtn },\n });\n\n const pathParts = location.pathname.split(\"\/\").filter(Boolean);\n const currentSlug = pathParts.length > 0 ? pathParts[pathParts.length - 1] : \"homepage\";\n const profileUrl =\n `https:\/\/www.scaler.com\/review\/?utm_source=blog&utm_medium=success_stories&utm_content=${currentSlug}`;\n\n function showSkeletons(count = 4) {\n wrapper.innerHTML = \"\";\n for (let i = 0; i < count; i++) {\n const clone = template.content.cloneNode(true);\n clone.querySelector(\".scaler-ss-card\").classList.add(\"scaler-ss-card--skeleton\");\n wrapper.appendChild(clone);\n }\n swiper.update();\n }\n\n function renderStories(stories) {\n wrapper.innerHTML = \"\";\n if (!stories.length) {\n container.style.display = \"none\"; \/\/ match topics: render nothing when empty\n return;\n }\n\n stories.forEach(story => {\n const a = story.attributes || {};\n const clone = template.content.cloneNode(true);\n\n const hike = String(a.growth || \"\").replace(\"%\", \"\").trim() || \"150\";\n clone.querySelector(\".js-ss-hike\").textContent = `${hike}% HIKE`;\n\n \/\/ Avatar\n const avatar = buildAssetUrl(a.image_url) || a.generated_thumbnail_url || \"\";\n const img = clone.querySelector(\".js-ss-avatar\");\n img.src = avatar || AVATAR_FALLBACK;\n img.alt = a.name || \"Scaler alumnus\";\n img.onerror = function () { this.onerror = null; this.src = AVATAR_FALLBACK; };\n\n clone.querySelector(\".js-ss-name\").textContent = a.name || \"\";\n const cityEl = clone.querySelector(\".js-ss-city\");\n if (a.city) cityEl.textContent = a.city; else cityEl.style.display = \"none\";\n\n \/\/ BEFORE\n clone.querySelector(\".js-ss-before-role\").textContent = a.pre_scaler_role || a.pre_academy_company || \"\";\n const beforeCompany = clone.querySelector(\".js-ss-before-company\");\n if (a.pre_academy_company) beforeCompany.textContent = a.pre_academy_company;\n else beforeCompany.style.display = \"none\";\n\n \/\/ NOW\n clone.querySelector(\".js-ss-now-role\").textContent = a.role || \"\";\n const nowCompanyBox = clone.querySelector(\".js-ss-now-company\");\n const companyLogo = buildAssetUrl(a.company_logo_url);\n if (companyLogo) {\n const logo = document.createElement(\"img\");\n logo.className = \"scaler-ss-card__company-logo\";\n logo.src = companyLogo;\n logo.alt = a.post_academy_company || \"company\";\n logo.loading = \"lazy\";\n logo.onerror = function () {\n this.onerror = null;\n this.replaceWith(Object.assign(document.createElement(\"div\"), {\n className: \"scaler-ss-card__col-company\",\n textContent: a.post_academy_company || \"\",\n }));\n };\n nowCompanyBox.appendChild(logo);\n } else if (a.post_academy_company) {\n nowCompanyBox.className = \"scaler-ss-card__col-company\";\n nowCompanyBox.textContent = a.post_academy_company;\n }\n\n \/\/ Quote (clip to 110 chars like topics)\n const quoteEl = clone.querySelector(\".js-ss-quote\");\n quoteEl.textContent = `\u201c${String(a.content).slice(0, 110)}\u2026\u201d`;\n\n \/\/ View Profile\n clone.querySelector(\".js-ss-cta\").href = profileUrl;\n\n wrapper.appendChild(clone);\n });\n\n swiper.update();\n swiper.slideTo(0);\n }\n\n async function fetchStories() {\n try {\n showSkeletons();\n \/\/ Same-origin POST on www.scaler.com (the blog canonical host).\n \/\/ The endpoint only returns JSON when the Referer is a \/topics\/ page\n \/\/ (a soft anti-hotlinking gate); a \/blog\/ referer gets a Turbolinks\n \/\/ redirect. Since blog + topics are the same origin, we set the\n \/\/ fetch referrer to a topics URL so the server serves the data.\n const res = await fetch(\"\/success-stories\/testimonials\", {\n method: \"POST\",\n credentials: \"include\",\n referrer: \"https:\/\/www.scaler.com\/topics\/\",\n referrerPolicy: \"unsafe-url\",\n headers: {\n \"Content-Type\": \"application\/json\",\n \"Accept\": \"application\/json\",\n \"X-Requested-With\": \"XMLHttpRequest\",\n },\n \/\/ q:{} => all programs (no program filter)\n body: JSON.stringify({\n page_number: 1,\n per_page: 12,\n sort_by: \"eligible_for_qna\",\n new_quick_filters: true,\n quick_filter: [],\n q: {},\n }),\n });\n const json = await res.json();\n const all = (json && json.data && json.data.data) || [];\n \/\/ Only show stories that carry a testimonial quote (matches topics).\n const withQuote = all.filter(s => s.attributes && s.attributes.content);\n renderStories(withQuote);\n } catch (error) {\n console.error(\"Failed to load success stories:\", error);\n container.style.display = \"none\";\n }\n }\n\n fetchStories();\n });\n });\n <\/script>\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Data cleaning, often overlooked, is the bedrock upon which successful machine learning models are built. Data cleaning ensures data quality and reliability by carefully addressing errors, inconsistencies, and outliers. This has a direct impact on model accuracy and the validity of insights derived from the data. From data inspection and preprocessing to handling missing values and outliers, each step in the data cleaning process is crucial for unlocking the true potential of your data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Your machine-learning projects will succeed if you put in the time and effort to clean the data. Clean data leads to better models, more accurate predictions, and ultimately, more informed decision-making.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"faqs\"><\/span><strong>FAQs<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<div class=\"wp-block-foxiz-elements-accordion gb-wrap gb-accordion yes-shadow yes-open\" style=\"--border-width:0 0 0 0;--desktop-padding:15px 30px 15px 30px;--tablet-padding:10px 25px 10px 25px;--mobile-padding:10px 20px 10px 20px\">\t\t<div class=\"gb-accordion-item wp-block-foxiz-elements-accordion-item\">\r\n\t\t\t<div class=\"accordion-item-header\">\r\n\t\t\t\t<h3 class=\"accordion-title gb-heading\"><strong>What does it mean to cleanse our data?<\/strong><\/h3>\t\t\t\t<i class=\"rbi rbi-angle-down gb-heading\"><\/i>\r\n\t\t\t<\/div>\r\n\t\t\t<div class=\"accordion-item-content\">\r\n\t\t\t\t\n\n<p class=\"wp-block-paragraph\">The process of finding and fixing mistakes, inconsistencies, and inaccuracies in a dataset is called data cleansing, sometimes referred to as data cleaning. This involves tasks like handling missing values, correcting typos, removing duplicates, and standardizing formats to improve data quality.<\/p>\n\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t\t\n\n\t\t<div class=\"gb-accordion-item wp-block-foxiz-elements-accordion-item\">\r\n\t\t\t<div class=\"accordion-item-header\">\r\n\t\t\t\t<h3 class=\"accordion-title gb-heading\">What is an example of cleaning data?<\/h3>\t\t\t\t<i class=\"rbi rbi-angle-down gb-heading\"><\/i>\r\n\t\t\t<\/div>\r\n\t\t\t<div class=\"accordion-item-content\">\r\n\t\t\t\t\n\n<p class=\"wp-block-paragraph\">An example of data cleaning is correcting inconsistent date formats in a dataset. If some dates are formatted as \"dd\/mm\/yyyy\" and others as \"mm\/dd\/yyyy,\" you would standardize them to a single format to ensure consistency and accuracy.<\/p>\n\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t\t\n\n\t\t<div class=\"gb-accordion-item wp-block-foxiz-elements-accordion-item\">\r\n\t\t\t<div class=\"accordion-item-header\">\r\n\t\t\t\t<h3 class=\"accordion-title gb-heading\">What is the meaning of a data wash?<\/h3>\t\t\t\t<i class=\"rbi rbi-angle-down gb-heading\"><\/i>\r\n\t\t\t<\/div>\r\n\t\t\t<div class=\"accordion-item-content\">\r\n\t\t\t\t\n\n<p class=\"wp-block-paragraph\">Data wash is not a standard term in data science or machine learning. It may refer to a colloquial way of describing data cleaning or preprocessing, but it's not widely used in the field.<\/p>\n\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t\t\n\n\t\t<div class=\"gb-accordion-item wp-block-foxiz-elements-accordion-item\">\r\n\t\t\t<div class=\"accordion-item-header\">\r\n\t\t\t\t<h3 class=\"accordion-title gb-heading\">How is data cleansing done?<\/h3>\t\t\t\t<i class=\"rbi rbi-angle-down gb-heading\"><\/i>\r\n\t\t\t<\/div>\r\n\t\t\t<div class=\"accordion-item-content\">\r\n\t\t\t\t\n\n<p class=\"wp-block-paragraph\">Data cleansing involves various steps, including data inspection and exploration, removing unwanted observations, handling missing data and outliers, data transformation, and data validation. This can be done manually or with the help of automated tools and scripts.<\/p>\n\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t\t\n\n\t\t<div class=\"gb-accordion-item wp-block-foxiz-elements-accordion-item\">\r\n\t\t\t<div class=\"accordion-item-header\">\r\n\t\t\t\t<h3 class=\"accordion-title gb-heading\">What is data cleansing in cybersecurity?<\/h3>\t\t\t\t<i class=\"rbi rbi-angle-down gb-heading\"><\/i>\r\n\t\t\t<\/div>\r\n\t\t\t<div class=\"accordion-item-content\">\r\n\t\t\t\t\n\n<p class=\"wp-block-paragraph\">In cybersecurity, data cleansing focuses on removing or sanitizing sensitive data to protect it from unauthorized access or exposure. This can involve techniques like data masking, tokenization, or encryption.<\/p>\n\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t\t\n\n\t\t<div class=\"gb-accordion-item wp-block-foxiz-elements-accordion-item\">\r\n\t\t\t<div class=\"accordion-item-header\">\r\n\t\t\t\t<h3 class=\"accordion-title gb-heading\">How to clean data using SQL?<\/h3>\t\t\t\t<i class=\"rbi rbi-angle-down gb-heading\"><\/i>\r\n\t\t\t<\/div>\r\n\t\t\t<div class=\"accordion-item-content\">\r\n\t\t\t\t\n\n<p class=\"wp-block-paragraph\">SQL (Structured Query Language) provides various functions and commands for data cleaning, such as TRIM for removing leading and trailing spaces, REPLACE for replacing specific characters, and CASE statements for handling inconsistencies. You can also use SQL queries to filter out unwanted data, correct errors, and aggregate data to identify outliers.<\/p>\n\n\t\t\t<\/div>\r\n\t\t<\/div>\r\n\t\t<\/div>\n","protected":false},"excerpt":{"rendered":"<p>The quality of data is crucial in the field of machine learning since it serves as the basis for the models that are created. Data cleaning, also known as data cleansing or data scrubbing, is the process of identifying and rectifying errors, inconsistencies, and inaccuracies within a dataset. It’s a crucial step that often goes […]<\/p>\n","protected":false},"author":242,"featured_media":9823,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"class_list":["post-9811","post","type-post","status-publish","format-standard","has-post-thumbnail","category-artificial-intelligence-machine-learning"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/posts\/9811","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/users\/242"}],"replies":[{"embeddable":true,"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/comments?post=9811"}],"version-history":[{"count":10,"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/posts\/9811\/revisions"}],"predecessor-version":[{"id":13701,"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/posts\/9811\/revisions\/13701"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/media\/9823"}],"wp:attachment":[{"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/media?parent=9811"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/categories?post=9811"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.scaler.com\/blog\/wp-json\/wp\/v2\/tags?post=9811"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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<\/figure>\n<\/div>\n\n\n