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@article{142385,
        author = {sunidhi bansal and Sunidhi Bansal and Dr. Kanwal Garg},
        title = {Spam Detection using KNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {2},
        number = {1},
        pages = {290-293},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=142385},
        abstract = {Social networking sites have become part of life for most of the people today. Among all OSNs twitter is one of the most used and powerful way of communication and news source. With twitter growth spamming activities in it has also increased.There is a need for more accurate but efficient spam detection methods to avoid causing inconvenience to legitimate users. This paper presents the implementation of KNN algorithm for spam detection marking tweets as spam or non-spam and experiment is done with different training percentages. Performance evaluation of KNN is also done using different standards like execution time, accuracy, sensitivity, specificity, precision, recall, f-measure, g-mean. The results show that KNN provides goodaccuracy even when less percent data is trained and it increases more when training percentage is taken high},
        keywords = {Spam Detection, KNN, Feature selection},
        month = {},
        }