[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118406-en":3,"doc-seo-118406-105":30,"detail-sidebar-cat-0-en-105":95},{"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},118406,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Startup Success Prediction with PCA-Enhanced Machine Learning Models","This study evaluates the effectiveness of machine learning algorithms in predicting startup success and examines how Principal Component Analysis (PCA) improves model performance. Using logistic regression, support vector classifier (SVC), XGBoost, and other supervised learning approaches, the research shows PCA enhances generalization for most models. SVC achieves 0.78 accuracy, 0.83 precision, 0.73 recall, and 0.74 F1 without PCA, improving to 0.90 accuracy, 0.90 precision, 0.89 recall, and 0.89 F1 with PCA. The work supports AI-driven venture capital decision-making and discusses dataset and method limitations, suggesting future dataset expansion, alternative dimension reduction, and real-time evaluation.","~~ ~~ J. Technol. Manag. Innov. 2024. Volume 19, Issue 4  \nStartup Success Prediction with PCA-Enhanced Machine Learning Models  \nYoungkeun Choi  \nAbstract  \nThis study evaluates the effectiveness of various machine learning algorithms in predicting startup success and explores the performance improvement achieved by applying Principal Component Analysis (PCA) to the models. By analyzing logistic regression, support vector classifier (SVC), XGBoost, and other supervised learning algorithms, the study demonstrates that PCA enhances the generalization performance of most models. Notably, Support Vector Classifier (SVC) showed an accuracy of 0.78, precision of 0.83, recall of 0.73, and F1 score of 0.74 without PCA, but performance significantly improved with PCA, recording an accuracy of 0.90, precision of 0.90, recall of 0.89, and F1 score of 0.89. Academically, this research contributes to the literature by examining how dimension reduction can boost the accuracy of machine learning models for startup success prediction, providing a valuable intersection of machine learning and venture capital studies. Practically, it offers investors AI-driven decision-making tools to enhance the precision of investment evaluations and better identify startups with high growth potential. Despite its contributions, this study is limited by the specific dataset used, suggesting that future research could explore various datasets and alternative dimension reduction techniques. Future studies could also assess real-time data application and incorporate deep learning models to improve predictive performance in startup success evaluation.  \nKeywords: Startup success prediction, Machine learning, Principal Component Analysis (PCA), Support Vector Classifier (SVC), Venture capital, Investment decision-making  \nSubmitted: November 7, 2024 / Approved: December 18, 2024  \n1. Introduction  \nWith the rise of startups and their growing economic impact, entrepreneurs, investors, and decision-makers increasingly require effective methods to analyze business data from various perspectives. However, identifying relevant factors influencing business volatility has become a challenging task due to ongoing technological advancements, competitive markets, and industry innovation. Recent studies have focused on factors such as mergers and acquisitions, financial determinants for business success, and investments essential for achieving IPO status (Ross et al., 2021) . Nevertheless, these studies often examine only specific methods or limited factors, indicating certain research gaps.  \nWhile venture capital (VC) investment plays a critical role in the global economy, many investments consistently deliver low returns for investors (Mulcahy et al., 2012) . A study analyzing annual returns for VC funds established since 1998 through June 2019 found that the top quartile of funds achieved an average return of 24.8%, while the bottom quartile reported an average return of just 0.5%, effectively incurring losses for limited partners when adjusted for inflation (Associates, 2020) . Further insight into VC funds’ poor performance reveals that from 2000 to 2010, VC returns were lower than S&P 500 returns (Guzy, 2010) . Similarly, the Kauffman Foundation (Mulcahy et al., 2012)  \nreported that between 1997 and 2012, VC funds returned less cash to investors than the capital initially raised. Among 30 VC funds with over $400 million in committed capital, only 4 outperformed the S&P 500. This disparity in success rates extends to individual investors, who, with limited investment choices, often invest small amounts in startups online, bypassing traditional financial intermediaries for minor equity stakes (Mollick, 2014) . However, equity crowdfunding is high-risk (Vroomen & Desa, 2018) and often attracts lower-quality entrepreneurs linked to risky banks, resulting in high failure rates (Blaseg et al., 2021) .  \nAI tools with potential to enhance return on investment (ROI) are lik","cbCaipmeomFHiBBq","https://ap.wps.com/l/cbCaipmeomFHiBBq","pdf",1132573,1,12,"English","en",105,"# Introduction\n## Venture capital performance and adoption of AI\n## Machine learning approaches for startup success\n# Method Overview\n## Supervised learning models and evaluation metrics\n# Results Focus\n## PCA impact on predictive performance\n# Limitations and Future Work","[{\"question\":\"Which machine learning models are used to predict startup success?\",\"answer\":\"The study analyzes logistic regression, support vector classifier (SVC), XGBoost, and other supervised learning algorithms to predict startup success.\"},{\"question\":\"How does PCA affect model performance in the study?\",\"answer\":\"Applying PCA improves generalization performance for most models, with a particularly large boost for SVC.\"},{\"question\":\"What performance metrics does the research report for SVC, and what change occurs with PCA?\",\"answer\":\"Without PCA, SVC reports 0.78 accuracy, 0.83 precision, 0.73 recall, and 0.74 F1; with PCA, these improve to 0.90, 0.90, 0.89, and 0.89 respectively.\"},{\"question\":\"What limitations and future directions does the study propose?\",\"answer\":\"Results depend on a specific dataset; future work should use different datasets and alternative dimension reduction methods, assess real-time data use, and consider deep learning models.\"}]","Startup Success Prediction with PCA-Enhanced Machine Learning Models | PDF",1785683466,30,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"startup-success-prediction-with-pca-enhanced-machine-learning-models","",{"@graph":36,"@context":89},[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/startup-success-prediction-with-pca-enhanced-machine-learning-models/118406/",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",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning models are used to predict startup success?","Question",{"text":75,"@type":76},"The study analyzes logistic regression, support vector classifier (SVC), XGBoost, and other supervised learning algorithms to predict startup success.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does PCA affect model performance in the study?",{"text":80,"@type":76},"Applying PCA improves generalization performance for most models, with a particularly large boost for SVC.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance metrics does the research report for SVC, and what change occurs with PCA?",{"text":84,"@type":76},"Without PCA, SVC reports 0.78 accuracy, 0.83 precision, 0.73 recall, and 0.74 F1; with PCA, these improve to 0.90, 0.90, 0.89, and 0.89 respectively.",{"name":86,"@type":73,"acceptedAnswer":87},"What limitations and future directions does the study propose?",{"text":88,"@type":76},"Results depend on a specific dataset; future work should use different datasets and alternative dimension reduction methods, assess real-time data use, and consider deep learning models.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,126,131,134,138],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":125},"research-report",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":129,"slug":130},9,"Religion & Spirituality",20,"religion-spirituality",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":129,"slug":133},"World Cup","world-cup",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":135,"slug":137},10,"Lifestyle","lifestyle",{"id":139,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":110,"slug":141},19,"General","general"]