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@article{185897,
        author = {Om Varpe and Sejal Kamble and Kashish Shimpi and Mohan Badgujar and Prof. Archana Mate},
        title = {Democratizing Machine Learning with a Zero-Code Platform : DataAlchemy},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {4044-4048},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185897},
        abstract = {This paper presents Data Alchemy, a novel Zero-Code Automated Machine Learning (AutoML) platform designed to bridge the accessibility gap in traditional ML development. Existing pipelines are hindered by reliance on manual coding, inefficient data preprocessing, and complex hyperparameter optimization (HPO), often consuming 70–80% of project time. Data Alchemy employs a three-tier architecture and a powerful AutoML Core Engine to fully automate the ML workflow, from data cleaning and feature engineering (including imputation, encoding, and scaling) to the benchmarking and tuning of a comprehensive suite of machine learning algorithms, including leading models such as Random Forest, XGBoost, LightGBM, SVM, and KNN. The platform provides transparent, interpretable results using quantitative metrics (Accuracy, AUC-ROC, R², RMSE) and visualizations (Feature Importance, Confusion Matrices), successfully democratizing high-performance ML capabilities for non-technical users and domain experts.1},
        keywords = {Automated Machine Learning (AutoML); Zero-Code; Data Preprocessing; Hyperparameter Optimization (HPO); Feature Engineering; Machine Learning.1},
        month = {November},
        }