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Innovate Data Mining with Machine Learning

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By training on historical data, ML models can generate forecasts, classify data, and identify anomalies, making it easier to extract valuable insights. Choosing the appropriate ML algorithm is crucial for achieving accurate results—whether it's using clustering techniques to group data or anomaly detection to flag irregularities. This adaptability makes ML a powerful tool for organizations aiming to leverage data effectively for informed decision-making.","upvoteCount":8,"datePublished":"2024-11-01T13:28:41.049+00:00","dateModified":"","url":"https://www.linkedin.com/advice/3/heres-how-you-can-innovate-data-mining-using-machine-y38hc?contributionUrn=urn%3Ali%3Acomment%3A%28articleSegment%3A%28urn%3Ali%3AlinkedInArticle%3A7247659578262241282%2C7247659580510330880%29%2C7258107684262924288%29&articleSegmentUrn=urn%3Ali%3AarticleSegment%3A%28urn%3Ali%3AlinkedInArticle%3A7247659578262241282%2C7247659580510330880%29&dashContributionUrn=urn%3Ali%3Afsd_comment%3A%287258107684262924288%2CarticleSegment%3A%28urn%3Ali%3AlinkedInArticle%3A7247659578262241282%2C7247659580510330880%29%29","author":{"@type":"Person","url":"https://nl.linkedin.com/in/hamid-jahani","name":"Hamid Jahani","image":"https://media.licdn.com/dms/image/v2/C4D03AQFFicJJVn16Fw/profile-displayphoto-shrink_400_400/profile-displayphoto-shrink_400_400/0/1641792885605?e=2147483647&v=beta&t=0279F8FHCSmXub-htGXiEBjz747n5JuVpTWncs_GG6g","interactionStatistic":{"@type":"InteractionCounter","interactionType":"https://schema.org/FollowAction","name":"Followers","userInteractionCount":0}}},"suggestedAnswer":[{"@type":"Answer","text":"Rubbish in rubbish out. This is a fundamental rule when you work on any data-related project. The target of any ML model is to generate results that you can rely on in taking decision. Preprocessing data in a way that will maximize the accuracy of ML model, without reducing the amount of learning for that model, is a key in ending up with a high-value model. The idea comes down to identifying the preprocessing technique that can be applied to remove the noise and maximize the learning the model will build from training and future data. The techniques will differ according to every aspect of the use case you're solving.","upvoteCount":6,"datePublished":"2024-11-02T14:57:51.389+00:00","dateModified":"","url":"https://www.linkedin.com/advice/3/heres-how-you-can-innovate-data-mining-using-machine-y38hc?contributionUrn=urn%3Ali%3Acomment%3A%28articleSegment%3A%28urn%3Ali%3AlinkedInArticle%3A7247659578262241282%2C7247659580518793217%29%2C7258492513081028608%29&articleSegmentUrn=urn%3Ali%3AarticleSegment%3A%28urn%3Ali%3AlinkedInArticle%3A7247659578262241282%2C7247659580518793217%29&dashContributionUrn=urn%3Ali%3Afsd_comment%3A%287258492513081028608%2CarticleSegment%3A%28urn%3Ali%3AlinkedInArticle%3A7247659578262241282%2C7247659580518793217%29%29","author":{"@type":"Person","url":"https://jo.linkedin.com/in/almutazbellahghanem","name":"Al-Mutaz Bellah Ghanem","image":"https://media.licdn.com/dms/image/v2/D4E03AQFfdQb5W9NRdQ/profile-displayphoto-shrink_400_400/profile-displayphoto-shrink_400_400/0/1730562945571?e=2147483647&v=beta&t=sMXE5ebn78dOQSelWn5kVjccy9JY9otcxnqkAxbO56k","interactionStatistic":{"@type":"InteractionCounter","interactionType":"https://schema.org/FollowAction","name":"Followers","userInteractionCount":0}}},{"@type":"Answer","text":"AI can be helpful in selecting the appropriate algorithm or model. With AI code assistance exploring multiple approaches is more tractable today than it was in the past. In addition, for those newer to the field, exploring pros and cons of different algorithms with LLMs to iterate on options can be valuable.","upvoteCount":5,"datePublished":"2024-10-25T13:13:35.936+00:00","dateModified":"","url":"https://www.linkedin.com/advice/3/heres-how-you-can-innovate-data-mining-using-machine-y38hc?contributionUrn=urn%3Ali%3Acomment%3A%28articleSegment%3A%28urn%3Ali%3AlinkedInArticle%3A7247659578262241282%2C7247659580531302401%29%2C7255567172888911872%29&articleSegmentUrn=urn%3Ali%3AarticleSegment%3A%28urn%3Ali%3AlinkedInArticle%3A7247659578262241282%2C7247659580531302401%29&dashContributionUrn=urn%3Ali%3Afsd_comment%3A%287255567172888911872%2CarticleSegment%3A%28urn%3Ali%3AlinkedInArticle%3A7247659578262241282%2C7247659580531302401%29%29","author":{"@type":"Person","url":"https://www.linkedin.com/in/mkamranshah","name":"Kamran 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<div class="article-segment-content-container "> <!----> <div class="x-article-last-update-time text-sm leading-[24px] text-color-text-secondary"> Last updated on Nov 7, 2024 </div> <section class="mb-2"> <ol itemscope itemtype="http://schema.org/BreadcrumbList" class="breadcrumbs "> <li itemprop="itemListElement" itemscope itemtype="http://schema.org/ListItem" class="breadcrumb inline-block mr-0.5 font-sans text-sm babybear:text-xs text-color-text after:content-['/'] after:text-color-text-low-emphasis after:ml-0.5 after:no-underline last:mr-0 last:after:hidden"> <a itemprop="item" class="breadcrumb__link inline-block link-no-visited-state !text-color-text-low-emphasis" href="https://www.linkedin.com/pulse/topics/home/" data-tracking-control-name="breadcrumb" data-tracking-will-navigate> <span class="font-normal" itemprop="name">All</span> </a> <meta itemprop="position" content="1"> </li> <li itemprop="itemListElement" itemscope itemtype="http://schema.org/ListItem" class="breadcrumb inline-block mr-0.5 font-sans text-sm babybear:text-xs text-color-text after:content-['/'] after:text-color-text-low-emphasis after:ml-0.5 after:no-underline last:mr-0 last:after:hidden"> <a itemprop="item" class="breadcrumb__link inline-block link-no-visited-state !text-color-text-low-emphasis" href="https://www.linkedin.com/pulse/topics/engineering-s166/" data-tracking-control-name="breadcrumb" data-tracking-will-navigate> <span class="font-normal" itemprop="name">Engineering</span> </a> <meta itemprop="position" content="2"> </li> <li itemprop="itemListElement" itemscope itemtype="http://schema.org/ListItem" class="breadcrumb inline-block mr-0.5 font-sans text-sm babybear:text-xs text-color-text after:content-['/'] after:text-color-text-low-emphasis after:ml-0.5 after:no-underline last:mr-0 last:after:hidden"> <a aria-current="page" itemprop="item" class="breadcrumb__link inline-block link-no-visited-state !text-color-text" href="https://www.linkedin.com/pulse/topics/engineering-s166/data-mining-s2682/" data-tracking-control-name="breadcrumb" data-tracking-will-navigate> <span class="font-semibold" itemprop="name">Data Mining</span> </a> <meta itemprop="position" content="3"> </li> </ol> </section> <h1 class="pulse-title text-display-md text-color-text lg:text-display-lg font-bold w-11/12 ">Here's how you can innovate in data mining using machine learning algorithms.</h1> <!----> <div class="powered-by-ai py-3 text-sm text-color-text-secondary">Powered by AI and the LinkedIn community</div> <section class="table-of-content pb-6"> <div class="group menu-item relative flex cursor-pointer flex-row justify-start border-0 pl-0" data-chunk-header-id="leverage-ml"> <h3 class="text-color-text group-hover:text-color-text-brand">1</h3> <div class="ml-2 mr-6 mt-0.5 min-w-[75%]"> <a class="fragmented-links font-normal !text-color-text underline decoration-1 underline-offset-4 group-hover:!text-color-text-brand" 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Training </a> <!----> </div> <icon class="invisible absolute right-2 top-1/2 flex -translate-y-2/4 items-center justify-between rounded-full border-1 border-solid border-color-container-brand p-1 group-hover:visible" data-delayed-url="https://static.licdn.com/aero-v1/sc/h/6q8fxh9snmcwlh1wxg6fktwgf" data-svg-class-name="w-2 h-2 text-color-text-brand"></icon> </div> <div class="group menu-item relative flex cursor-pointer flex-row justify-start border-0 pl-0" data-chunk-header-id="evaluate-results"> <h3 class="text-color-text group-hover:text-color-text-brand">5</h3> <div class="ml-2 mr-6 mt-0.5 min-w-[75%]"> <a class="fragmented-links font-normal !text-color-text underline decoration-1 underline-offset-4 group-hover:!text-color-text-brand" href="#evaluate-results" data-tracking-will-navigate data-tracking-control-name="article-ssr-frontend-x-article__toc-article-segment-navigation">Evaluate Results </a> <!----> </div> <icon class="invisible absolute right-2 top-1/2 flex -translate-y-2/4 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group-hover:visible" data-delayed-url="https://static.licdn.com/aero-v1/sc/h/6q8fxh9snmcwlh1wxg6fktwgf" data-svg-class-name="w-2 h-2 text-color-text-brand"></icon> </div> </section> <div class="x-article-introduction"> <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Data mining is a powerful tool for uncovering patterns and insights from large datasets. When combined with machine learning algorithms, it becomes even more potent, offering innovative ways to analyze and predict trends. Understanding how these technologies work together can significantly enhance your data mining projects. In the realm of data mining, machine learning serves as a catalyst for innovation, enabling you to delve deeper into your data and extract valuable information that can inform business decisions and strategies.</span><!----> </p> </div> </div> </div> </div> </div> <div class="column-article-segment-contributions md:column md:w-1/2"> <code id="shouldExpandContributionBox" style="display: none"><!--true--></code> <div class="x-featured-expert-section__wrapper flex h-full flex-1 flex-col"> <div class="x-featured-expert-section h-full"> <div class="x-featured-expert-section__article-experts"> <div class="x-featured-expert-section__title mt-2 pb-0.5 text-xl font-bold text-color-text"> Top experts in this article </div> <div class="x-featured-expert-section__subtitle pb-3 text-sm text-color-text-secondary"> Selected by the community from 34 contributions. <a href="https://www.linkedin.com/help/linkedin/answer/a1652832" target="_blank" aria-label="Learn more about collaborative articles" 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whitespace-pre-wrap pt-1 text-md leading-open text-color-text md:mb-0 md:pt-0 line-clamp-5 lg:text-lg" data-tracking-control-name="comment_text">ML has transformed data mining by enabling systems to automatically identify patterns without explicit programming, making it far easier to uncover insights and hidden relationships in massive data sets. 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<span class="mr-0.5 truncate text-sm font-bold text-color-text lg:text-md"> Harsh Tripathi </span> <!----><!----> </div> <p class="m-0 truncate text-sm text-color-text-low-emphasis md:text-md" data-test-id="contribution-lock-up-headline"> Data Analyst | MSc in Data Science | Python &amp; Machine Learning Expert | Driving Business Decisions Through Data Insights | Open to Exciting Opportunities. 🚀 </p> </div> </a> <div class="flex items-center"> <!----> <div class="ellipsis-menu absolute right-0 top-0 contribution-control-menu relative" data-feed-action="show-comment-dropdown" data-id="comment__ellipsis-menu" data-feed-control="control_menu" data-tracking-control-name="comment_control_menu" data-feed-action-type="expandControl" data-feed-action-category="expand"> <div class="collapsible-dropdown flex items-center relative hyphens-auto"> <button class="ellipsis-menu__trigger collapsible-dropdown__button btn-md btn-tertiary cursor-pointer !py-[6px] !px-1 flex items-center rounded-[50%] 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data-tracking-control-name="comment_control_menu_report" data-feed-action-type="expandContributionReporting" data-feed-action-category="expand" data-test-id="contribution-control-menu-report"> <a href="/uas/login?session_redirect=https%3A%2F%2Fwww.linkedin.com%2Fadvice%2F3%2Fheres-how-you-can-innovate-data-mining-using-machine-y38hc&amp;trk=comment-semaphore-sign-in-redirect&amp;guestReportContentType=AUTO_GENERATED_SKILL_ARTICLE_CONTRIBUTION&amp;_f=guest-reporting" data-tracking-control-name="comment-semaphore-sign-in-redirect" data-tracking-will-navigate data-item-type="semaphore" data-semaphore-content-type="AUTO_GENERATED_SKILL_ARTICLE_CONTRIBUTION" data-semaphore-content-urn="urn:li:comment:(articleSegment:(urn:li:linkedInArticle:7247659578262241282,7247659580510330880),7258476875830693888)" data-semaphore-author-urn="urn:li:member:722672700" data-semaphore-tracking-prefix="comment-semaphore" data-is-logged-in="false" data-modal="semaphore__toggle" class="semaphore__toggle 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</div> <code id="loginUrl" style="display: none"><!--"https://www.linkedin.com/signup/cold-join?session_redirect=%2Fadvice%2F3%2Fheres-how-you-can-innovate-data-mining-using-machine-y38hc"--></code> <div class="article-segment" data-is-logged-in="false" data-thread-urn="urn:li:articleSegment:(urn:li:linkedInArticle:7247659578262241282,7247659580518793217)" data-test-id="article-segment" data-total-count="8" data-tracking-prefix="article-ssr-frontend-x-article" role="presentation"> <div class="md:row pb-6 md:flex"> <div class="column-article-segment-content md:column md:mb-8 md:mr-4 md:w-1/2 md:flex-1 md:pr-[88px]"> <div class="md:pt-6 sticky-on-scroll"> <div class="article-segment-content-container "> <div id="content-urn_li_articleSegment__urn_li_linkedInArticle_7247659578262241282_7247659580518793217_"> <div class="article-main__content" data-test-id="publishing-text-block"> <h2 class="flex"> <a id="preprocess-data" href="#preprocess-data" class="dummy-anchor invisible absolute mt-[-100px]"></a> <span class="header-index pr-1.5">2</span> <span class>Preprocess Data</span><!----> </h2> </div> <div class="article-main__content" data-test-id="publishing-text-block"> <p> <span class>Before applying machine learning to your data mining efforts, it's crucial to preprocess your data. 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This is a fundamental rule when you work on any data-related project. The target of any ML model is to generate results that you can rely on in taking decision. Preprocessing data in a way that will maximize the accuracy of ML model, without reducing the amount of learning for that model, is a key in ending up with a high-value model. The idea comes down to identifying the preprocessing technique that can be applied to remove the noise and maximize the learning the model will build from training and future data. 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Comienza limpiando los datos, eliminando inconsistencias y valores faltantes, lo que garantiza que el análisis no esté sesgado. También es crucial transformar los datos en un formato compatible con los algoritmos de ML, utilizando técnicas como el escalado y la normalización, para asegurar que todas las variables tengan un peso equitativo en el análisis. 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clipboard is unsupported."--></code> </div> </div> <div class="contribution__container left__border -mb-2 -mt-1 animate-show ml-0 pl-0 md:ml-[-41px] md:pl-[32px] pr-0 lg:ml-[-33px] lg:pl-[40px] lg:pr-1 pt-2 pb-4"> <div class="flex flex-col justify-between lg:flex-col"> <div class="flex items-center justify-between gap-1"> </div> </div> <div class="contribution__text-wrapper relative"> <p class="contribution__text mb-1 whitespace-pre-wrap pt-1 text-md leading-open text-color-text md:mb-0 md:pt-0 line-clamp-5 lg:text-lg" data-tracking-control-name="comment_text">Este es uno de los puntos a los que hay que dedicar más esfuerzos actualmente. Todos los algoritmos de ML/DL se entrenan utilizando datos. Bases de datos que se han ido construyendo durante el pasado y de las que ahora disponemos. Aunque una empresa no pueda tener sistemas de almacenamiento de datos eficiente, no debería dejar de almacenarnos, pues el histórico no se puede construir a futuro. 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Each algorithm is designed with unique strengths, suited to particular types of data and objectives. Ex. K-Means is ideal for finding natural groupings within data, while Support Vector Machines excel in classification tasks. Neural Networks are powerful for handling highly complex, non-linear data, making them invaluable in deep learning. Understanding each algorithm enables you to align the model’s capabilities with the specific characteristics of your dataset, such as size, complexity, and target outcomes. 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<span class="mr-0.5 truncate text-sm font-bold text-color-text lg:text-md"> Lukman Raheem </span> <!----><!----> </div> <p class="m-0 truncate text-sm text-color-text-low-emphasis md:text-md" data-test-id="contribution-lock-up-headline"> B. Tech. 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Choose Algorithms: Select appropriate ML algorithms based on the problem at hand. 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<span class="mr-0.5 truncate text-sm font-bold text-color-text lg:text-md"> Mauricio Mora Caballero </span> <!----><!----> </div> <p class="m-0 truncate text-sm text-color-text-low-emphasis md:text-md" data-test-id="contribution-lock-up-headline"> AI Product Manager | Especialista en IA, Big Data y Data Science | Mejorando Experiencias del Cliente y Estrategias de Productos | Facilitador de Decisiones Basadas en Datos | Editor del Newsletter Analítica </p> </div> </a> <div class="flex items-center"> <!----> <div class="ellipsis-menu absolute right-0 top-0 contribution-control-menu relative" data-feed-action="show-comment-dropdown" data-id="comment__ellipsis-menu" data-feed-control="control_menu" data-tracking-control-name="comment_control_menu" data-feed-action-type="expandControl" data-feed-action-category="expand"> <div class="collapsible-dropdown flex items-center relative hyphens-auto"> <button class="ellipsis-menu__trigger collapsible-dropdown__button btn-md btn-tertiary 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</ul> <!----> </div> </div> <code id="contributionCopyLink" style="display: none"><!--"Link copied to clipboard"--></code> <code id="contributionCopyFail" style="display: none"><!--"Copy failed&#44; try again later."--></code> <code id="contributionCopyUnsupported" style="display: none"><!--"Copy to clipboard is unsupported."--></code> </div> </div> <div class="contribution__container left__border -mb-2 -mt-1 animate-show ml-0 pl-0 md:ml-[-41px] md:pl-[32px] pr-0 lg:ml-[-33px] lg:pl-[40px] lg:pr-1 pt-2 pb-4"> <div class="flex flex-col justify-between lg:flex-col"> <div class="flex items-center justify-between gap-1"> </div> </div> <div class="contribution__text-wrapper relative"> <p class="contribution__text mb-1 whitespace-pre-wrap pt-1 text-md leading-open text-color-text md:mb-0 md:pt-0 line-clamp-5 lg:text-lg" data-tracking-control-name="comment_text">Elegir el algoritmo adecuado es clave para el éxito en minería de datos. K-Means es ideal para la agrupación de datos sin etiquetar, mientras que las máquinas de vectores de soporte (SVM) son excelentes para clasificación en conjuntos de datos más pequeños. Para datos complejos o no estructurados, como imágenes o texto, las redes neuronales y el aprendizaje profundo son altamente efectivos. 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<span class="mr-0.5 truncate text-sm font-bold text-color-text lg:text-md"> Cephas Acquah Forson (MGhIS, MSc) </span> <!----><!----> </div> <p class="m-0 truncate text-sm text-color-text-low-emphasis md:text-md" data-test-id="contribution-lock-up-headline"> Passionate Data Science &amp; Engineering Professional | GIS and Database Specialist | AI &amp; ML Enthusiast </p> </div> </a> <div class="flex items-center"> <!----> <div class="ellipsis-menu absolute right-0 top-0 contribution-control-menu relative" data-feed-action="show-comment-dropdown" data-id="comment__ellipsis-menu" data-feed-control="control_menu" data-tracking-control-name="comment_control_menu" data-feed-action-type="expandControl" data-feed-action-category="expand"> <div class="collapsible-dropdown flex items-center relative hyphens-auto"> <button class="ellipsis-menu__trigger collapsible-dropdown__button btn-md btn-tertiary cursor-pointer !py-[6px] !px-1 flex items-center rounded-[50%] ellipsis-menu__trigger--small 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Key to this process is dividing the dataset into training and testing sets, allowing accurate evaluation of the model’s performance. Additionally, hyperparameter tuning refines parameters governing the learning process, enhancing model accuracy and robustness. 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Tech. 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This step is crucial to understand the model’s effectiveness.</p> <button class="contribution__see-more-button absolute bottom-0 right-0 hidden cursor-pointer border-0 bg-color-surface p-0 text-md leading-open text-color-text-secondary hover:text-color-action hover:underline lg:text-lg" data-tracking-control-name="comment_expand" data-feed-control="comment_expand" data-feed-action-type="expandContributionText" data-feed-action-category="EXPAND" role="button" type="button">…see more</button> </div> <!----> <div class="contribution__social-action-bar relative right-1 flex flex-row items-center justify-start gap-1"> <div class="contribution-social-activity inline-flex items-center justify-start"> <code id="i18n_reaction_selection_state" style="display: none"><!--"%reactionType% %selectionState%"--></code> <div class="reactions-menu flex flex-row relative items-center justify-center select-none [-webkit-user-select:none] pointer-events-auto contribution-social-activity__reactions-menu " 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<span class="mr-0.5 truncate text-sm font-bold text-color-text lg:text-md"> Mauricio Mora Caballero </span> <!----><!----> </div> <p class="m-0 truncate text-sm text-color-text-low-emphasis md:text-md" data-test-id="contribution-lock-up-headline"> AI Product Manager | Especialista en IA, Big Data y Data Science | Mejorando Experiencias del Cliente y Estrategias de Productos | Facilitador de Decisiones Basadas en Datos | Editor del Newsletter Analítica </p> </div> </a> <div class="flex items-center"> <!----> <div class="ellipsis-menu absolute right-0 top-0 contribution-control-menu relative" data-feed-action="show-comment-dropdown" data-id="comment__ellipsis-menu" data-feed-control="control_menu" data-tracking-control-name="comment_control_menu" data-feed-action-type="expandControl" data-feed-action-category="expand"> <div class="collapsible-dropdown flex items-center relative hyphens-auto"> <button class="ellipsis-menu__trigger collapsible-dropdown__button btn-md btn-tertiary 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En modelos de clasificación, utiliza métricas como la exactitud, precisión, recuperación y puntuación F1 para obtener una visión completa de su rendimiento. Para modelos de regresión, el error cuadrático medio (MSE) es una buena métrica para medir la precisión. 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