International Journal For Multidisciplinary Research

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Spam SMS Classifier using Machine Learning Algorithms

Author(s) AARYAN SHARMA, HARSHIT KUMAR SIMBAL, SMRITI KUMARI, GAUTAM KUMAR, HARSHVARDHAN KUMAR
Country India
Abstract Mobile SMS communication is insecure as a result
of a significant problem with spam detection. A technique or
model with high accuracy and precision is required to address
this spam SMS issue. The amount of spam emails has
dramatically increased over the last few years. SMS spam has
major negative impacts since it harms both consumers and
service providers, eroding their mutual trust to a great extent.
Different types of classifier algorithm have been implemented
like Naïve bayes, Random Forest, KNN and Support vector
classifier on a raw dataset collected from UCI repository in this
research. Metrices like Accuracy, Precision and Recall are
takes as performance metrics for calculating the efficiency of
the algorithm. After experimenting, the result of these
algorithms and compared them with another models. We
showed the comparison using Visualization Techniques.
Keywords SMS, Machine learning, KNN, SVM, Naive Bayes, Spam detection, Random forest, messages
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-30
Cite This Spam SMS Classifier using Machine Learning Algorithms - AARYAN SHARMA, HARSHIT KUMAR SIMBAL, SMRITI KUMARI, GAUTAM KUMAR, HARSHVARDHAN KUMAR - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.19483
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.19483
Short DOI https://doi.org/gts4nz
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