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@article{196498,
        author = {Bhagyashree Gadekar and Dr. Pravin Haribhau Ghosekar},
        title = {An End-to-End Conceptual Modelling Framework from Business Objectives to Machine Learning Solutions with Algorithmic Performance Comparison},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {2803-2807},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196498},
        abstract = {The challenge of matching business goals with machine learning (ML) solutions is also prominent in data-driven environments. The present paper offers a conceptual modeling framework that will be an end-to-end framework and will map out business objectives to analytical tasks and ML models in a systematic way. The framework incorporates a business problem identification, data preprocessing, model development, and evaluation into a single structure. In order to support the framework, five classification algorithms are performed and compared, including Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Random Forest using the common performance measures of accuracy, precision, recall, and F1-score. The findings reveal the difference in performance among models with ensemble methods having a higher predictive accuracy and easier-to-interpret models. The suggested framework will assist in successful decision making because it will help close the divide between machine learning implementation and business strategy and offer scalability to any business analytics implementation.},
        keywords = {Conceptual Modeling Framework, Machine Learning, Business Analytics, Classification Algorithms, Predictive Analytics, Model Evaluation, Decision Support Systems},
        month = {April},
        }