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@article{170729,
        author = {Dr.M.Suresh Babu and M.Shesha Sai and M.Nikitha Reddy and M.Sai Prakash Reddy},
        title = {Data Valuation for Machine Learning Using Data Shapley},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {892-897},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170729},
        abstract = {As data becomes the cornerstone of technological advancements, a critical challenge arises in quantifying its value in algorithmic predictions and decision-making. In domains like healthcare and finance, equitable data valuation is essential to ensure fairness and transparency, yet existing methods often fail to address biases and adequately compensate underrepresented groups. This project introduces an advanced framework for equitable data valuation in machine learning using the Data Shapley approach. Data Shapley provides a mathematically grounded metric to evaluate the contribution of individual data points to model performance, uniquely satisfying properties of fairness and transparency. Our work employs Monte Carlo and gradient-based methods for efficient Shapley value estimation in practical settings involving large datasets and complex algorithms. Through experiments on a heart disease dataset, we demonstrate how training data can be segmented into high- and low-impact subsets, enabling improved model performance and targeted data acquisition. Comparative analyses of Shapley value computation techniques—such as TMC, G, and LOO—highlight the robustness of our approach. Additionally, our framework emphasizes inclusivity by dynamically valuing data that mitigates bias. This study offers a novel perspective on data-driven decision-making, fostering ethical innovation in machine learning.},
        keywords = {Fairness, Efficiency, Null data points,  Additivity, Monte carlo approximation, sampling-based Approximation.},
        month = {December},
        }