OptiML Suite: Streamlined Solutions for Data-Driven Model Development

Yogesh Sankaranarayanan Jayanthi, Rithvik Rahul Prabhakaran, Bajji Saravanan Ranjith, Sowmiya Sree C.

2025

Abstract

The field of automated machine learning (AutoML) has emerged to streamline and democratize the data science process, enabling users to develop machine learning models with minimal manual intervention. Traditional approaches often require extensive expertise and time-consuming tasks, such as data cleaning, preprocessing, and model selection, which can be barriers for many practitioners Tschalzev et al., (2024). To address these challenges, we propose” OptiML Suite: Streamlined Solutions for Data-Driven Model Development,” an application designed to automate the end-to-end machine learning workflow. Our system accepts CSV files from users, performs automated data profiling and cleaning, visualizes the differences between raw and processed data, facilitates data analysis through various charting options, and handles pre-processing tasks including label encoding and outlier elimination using methods like IQR and Z-score. Upon selecting the target variable, the application leverages PyCaret to generate and evaluate models, ultimately deploying the best- performing model in a user-friendly interface for predictions. This approach overcomes limitations in existing systems by reducing the need for manual data handling and model tuning, thereby accelerating the development process and making machine learning more accessible Y. Zhao et al., (2022) and H.C. Vazquez (2023). Experimental results demonstrate the effectiveness of OptiML Suite in producing accurate models with reduced development time.

Paper Citation

in Harvard Style

Jayanthi Y., Prabhakaran R., Ranjith B. and C. S. (2025). OptiML Suite: Streamlined Solutions for Data-Driven Model Development. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 26-38. DOI: 10.5220/0013922000004919

in Bibtex Style

@conference{icrdicct`2525,
author={Yogesh Jayanthi and Rithvik Prabhakaran and Bajji Ranjith and Sowmiya C.},
title={OptiML Suite: Streamlined Solutions for Data-Driven Model Development},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={26-38},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013922000004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - OptiML Suite: Streamlined Solutions for Data-Driven Model Development
SN - 978-989-758-777-1
AU - Jayanthi Y.
AU - Prabhakaran R.
AU - Ranjith B.
AU - C. S.
PY - 2025
SP - 26
EP - 38
DO - 10.5220/0013922000004919
PB - SciTePress