[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117218-en":3,"doc-seo-117218-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117218,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Application of Machine Learning Based Predictive Algorithms - A Comprehensive Review","Machine learning-based predictive algorithms extract insights and enable reliable forecasting from complex datasets across industries. This comprehensive review surveys state-of-the-art methods for predictive analytics, covering supervised and unsupervised learning as well as regression, classification, clustering, and dimensionality reduction. It discusses real-world applications including healthcare, finance, manufacturing, and transportation, such as disease progression forecasting, fraud detection, equipment failure prediction, supply-chain optimization, and autonomous navigation. The review further addresses feature engineering, model selection, hyperparameter tuning, performance evaluation, and emerging trends like automated machine learning, interpretable AI, and edge computing, while highlighting challenges in data quality, interpretability, and ethics.","Application of Machine Learning Based Predictive Algorithms: A Comprehensive Review  \nAnnotation: Machine learning-based predictive algorithms have emerged as powerful tools for extracting insights and making predictions from complex datasets across diverse domains. This comprehensive review examines the state-ofthe-art in applying machine learning for predictive analytics, covering key algorithms, application areas, and recent advances. We provide an overview of popular supervised and unsupervised learning techniques used for prediction tasks, including regression, classification, clustering, and dimensionality reduction. The review explores applications of predictive machine learning infields such as healthcare, finance, manufacturing, and transportation. We analyze how machine learning models are being used to forecast disease progression, detect financial fraud, predict equipment failures, optimize supply chains, and enable autonomous navigation. The paper also discusses important considerations in developing predictive models, including feature engineering, model selection, hyperparameter tuning, and performance evaluation. Additionally, we examine emerging trends like automated machine learning, interpretable AI, and edge computing for real-time predictions. Key challenges such as data quality, model interpretability, and ethical concerns are highlighted. The review concludes by identifying promising research directions to further advance the capabilities and real-world impact of machine learning-based predictive analytics. This comprehensive survey provides researchers and practitioners with an up-to-date perspective on the current landscape and future potential of predictive machine learning across industries.  \nInformation about the authors  \nBolanle Oluwapamilerin  \nComputer science and technology, University of Obafemi awolowo  \n1. Introduction  \nMachine learning has revolutionized the field of predictive analytics by enabling the development of algorithms that can automatically learn patterns and make predictions from large, complex datasets. Asthe volume and variety of data continue to grow exponentially across industries, machine learning-based predictive models have become indispensable tools for extracting actionable insights and forecasting future trends. This review paper provides a comprehensive overview of the application of machine learning algorithms for predictive analytics, covering fundamental concepts, key techniques, diverse application areas, and emerging trends [1] .  \nPredictive analytics aims to use historical data and statistical algorithms to identify the likelihood of future outcomes. Traditional approaches relied heavily on human-crafted rules and simple statistical techniques. However, the advent of more sophisticated machine learning algorithms has dramatically expanded the capabilities of predictive modeling. Machine learning enables the automatic discovery of intricate patterns in high-dimensional data that may be difficult for humans to detect. It can capture complex non-linear relationships and interactions between variables. Moreover, machine learning  \nmodels can continuously improve their predictive performance as they are exposed to more training data [2, 3] .  \nThe power of machine learning for prediction stems from its ability to learn from examples without being explicitly programmed. Supervised learning algorithms can be trained on labeled historical data to make predictions on new, unseen data. For instance, a supervised model could be trained on past customer data to predict which customers are likely to churn in the future. Unsupervised learning techniques can uncover hidden structures and patterns in unlabeled data, which can serve as a foundation for predictive modeling. Semi-supervised and reinforcement learning approaches provide additional paradigms for predictive analytics in scenarios with limited labeled data or dynamic environments [4] .  \nMachine learning encompasse","cbCaimoTwVOSr8eo","https://ap.wps.com/l/cbCaimoTwVOSr8eo","pdf",534145,1,11,"English","en",105,"# Introduction\n## Predictive analytics and machine learning\n## Learning paradigms for prediction\n## Core algorithms for predictive modeling\n## Cross-domain applications\n## Developing predictive models","[{\"question\":\"What is predictive analytics and how does machine learning improve it?\",\"answer\":\"Predictive analytics uses historical data and statistical methods to estimate future outcomes. Machine learning expands predictive modeling by automatically discovering patterns and capturing complex nonlinear relationships from high-dimensional data.\"},{\"question\":\"Which learning approaches are discussed for predictive tasks?\",\"answer\":\"The review covers supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. These paradigms support prediction when labels are available, hidden structures must be found, or data is limited and environments change.\"},{\"question\":\"What kinds of real-world domains use machine learning for prediction?\",\"answer\":\"Applications span healthcare, finance, manufacturing, retail, and transportation. Examples include forecasting disease onset and outcomes, credit scoring and fraud detection, predictive maintenance, customer segmentation and demand forecasting, and traffic prediction and autonomous navigation.\"}]","Application of Machine Learning Based Predictive Algorithms - A Comprehensive Review | PDF",1785674456,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"application-of-machine-learning-based-predictive-algorithms-a-comprehensive-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/application-of-machine-learning-based-predictive-algorithms-a-comprehensive-review/117218/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is predictive analytics and how does machine learning improve it?","Question",{"text":75,"@type":76},"Predictive analytics uses historical data and statistical methods to estimate future outcomes. Machine learning expands predictive modeling by automatically discovering patterns and capturing complex nonlinear relationships from high-dimensional data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which learning approaches are discussed for predictive tasks?",{"text":80,"@type":76},"The review covers supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. These paradigms support prediction when labels are available, hidden structures must be found, or data is limited and environments change.",{"name":82,"@type":73,"acceptedAnswer":83},"What kinds of real-world domains use machine learning for prediction?",{"text":84,"@type":76},"Applications span healthcare, finance, manufacturing, retail, and transportation. Examples include forecasting disease onset and outcomes, credit scoring and fraud detection, predictive maintenance, customer segmentation and demand forecasting, and traffic prediction and autonomous navigation.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]