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@article{180995,
        author = {Divya Kashid},
        title = {A Study on Data Cleaning Techniques for Large Datasets},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {3513-3514},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180995},
        abstract = {Abstract—Data cleaning is an essential phase in the data preparation process, especially when working with large datasets. These datasets include often missing value, duplicate records, noise and anomalies that must be addressed for reliable analysis and decision making. This research examines several types of data cleaning techniques, including missing value copying, deduction, external identification and generalization, and evaluating their application on a large-scale dataset. The paper also reviews modern equipment and outlines that facilitates automatic and scalable data cleaning},
        keywords = {},
        month = {June},
        }