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Automated predictive analytics with nomogram / Mohammad Nasir Abdullah

In the era of data-driven decision-making, the demand for predictive analytics tools accessible to non-statisticians is rising. Traditional software often requires extensive manual work, hindering efficiency and usability. An innovative application was developed using R programming and the Shiny app...

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Main Author: Abdullah, Mohammad Nasir
Format: Conference or Workshop Item
Language:English
Published: 2024
Subjects:
Analytic mechanics
Online Access:https://ir.uitm.edu.my/id/eprint/105848/1/105848.pdf
https://ir.uitm.edu.my/id/eprint/105848/
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spelling my.uitm.ir.1058482025-01-20T04:55:25Z https://ir.uitm.edu.my/id/eprint/105848/ Automated predictive analytics with nomogram / Mohammad Nasir Abdullah Abdullah, Mohammad Nasir Analytic mechanics In the era of data-driven decision-making, the demand for predictive analytics tools accessible to non-statisticians is rising. Traditional software often requires extensive manual work, hindering efficiency and usability. An innovative application was developed using R programming and the Shiny app package to address this gap. This application aims to empower non-statisticians to conduct predictive analytics swiftly and accurately, providing automated processes and delivering relevant results crucial for researchers. The application development involved leveraging R programming and the Shiny app package to create a user-friendly interface for data upload, preprocessing, model building, evaluation, and result interpretation. Advanced statistical techniques such as Fast Backward step-down regression for feature selection, calibration plots, and performance metrics calculation were integrated to ensure robust predictive modelling. The application successfully automates critical aspects of predictive analytics, including data cleaning, feature selection, model building, and validation. Users can upload their datasets, specify variables, choose regression methods, and interpret results through descriptive statistics, visualisations, and model summaries. The app’s automation capabilities significantly reduce manual effort and provide researchers with actionable insights for informed decision-making. In conclusion, the app fills a crucial need by offering non-statisticians a user-friendly platform to conduct predictive analytics efficiently. By automating repetitive tasks and focusing on relevant results, the application empowers researchers to derive meaningful insights from their data, thereby enhancing decision-making processes and driving impactful outcomes across industries. 2024 Conference or Workshop Item PeerReviewed text en https://ir.uitm.edu.my/id/eprint/105848/1/105848.pdf Automated predictive analytics with nomogram / Mohammad Nasir Abdullah. (2024) In: UNSPECIFIED.
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Analytic mechanics
spellingShingle Analytic mechanics
Abdullah, Mohammad Nasir
Automated predictive analytics with nomogram / Mohammad Nasir Abdullah
description In the era of data-driven decision-making, the demand for predictive analytics tools accessible to non-statisticians is rising. Traditional software often requires extensive manual work, hindering efficiency and usability. An innovative application was developed using R programming and the Shiny app package to address this gap. This application aims to empower non-statisticians to conduct predictive analytics swiftly and accurately, providing automated processes and delivering relevant results crucial for researchers. The application development involved leveraging R programming and the Shiny app package to create a user-friendly interface for data upload, preprocessing, model building, evaluation, and result interpretation. Advanced statistical techniques such as Fast Backward step-down regression for feature selection, calibration plots, and performance metrics calculation were integrated to ensure robust predictive modelling. The application successfully automates critical aspects of predictive analytics, including data cleaning, feature selection, model building, and validation. Users can upload their datasets, specify variables, choose regression methods, and interpret results through descriptive statistics, visualisations, and model summaries. The app’s automation capabilities significantly reduce manual effort and provide researchers with actionable insights for informed decision-making. In conclusion, the app fills a crucial need by offering non-statisticians a user-friendly platform to conduct predictive analytics efficiently. By automating repetitive tasks and focusing on relevant results, the application empowers researchers to derive meaningful insights from their data, thereby enhancing decision-making processes and driving impactful outcomes across industries.
format Conference or Workshop Item
author Abdullah, Mohammad Nasir
author_facet Abdullah, Mohammad Nasir
author_sort Abdullah, Mohammad Nasir
title Automated predictive analytics with nomogram / Mohammad Nasir Abdullah
title_short Automated predictive analytics with nomogram / Mohammad Nasir Abdullah
title_full Automated predictive analytics with nomogram / Mohammad Nasir Abdullah
title_fullStr Automated predictive analytics with nomogram / Mohammad Nasir Abdullah
title_full_unstemmed Automated predictive analytics with nomogram / Mohammad Nasir Abdullah
title_sort automated predictive analytics with nomogram / mohammad nasir abdullah
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/105848/1/105848.pdf
https://ir.uitm.edu.my/id/eprint/105848/
_version_ 1823097819358035968
score 13.239859

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