AutoSynth AI: Automated Data Intelligence and Model Generation System

Author(s)	Akshatha R, Vedashree M R, Chitra M, Neha R, Anup Santosh Patil
Country	India
Abstract	Machine learning has become an important technology for analysing data and making predictions across various domains. However, developing machine-learning models often requires expertise in data preprocessing, feature selection, algorithm selection, hyperparameter tuning, and model evaluation. These requirements can make machine learning challenging for beginners and non-technical users. To address these challenges, AutoSynth AI is developed as an automated machine-learning framework that simplifies the model development process through a unified workflow. It supports multiple file formats, automates data preprocessing, selects suitable algorithms, evaluates model performance, and provides explainability features. With a user-friendly interface and local execution, the system aims to make machine learning more accessible, efficient, and transparent for learning, experimentation, and predictive modelling.
Keywords	AutoML, automated preprocessing, model selection, explainable AI, TF-IDF, SHAP, LIME, Streamlit, FastAPI
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 17, Issue 11, November 2026
Published On	2026-11-10

About IJTAS Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics	Join as a Reviewer Editors & Reviewers Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijtas.com

International Journal of Technology and Applied Science