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Email is among the most extensively used communication techniques. Fraud Emails or Spam Emails has become a major concern as it is valueless to the recipient. It is a wastage of network capacity and filtering spam emails are time consuming and removing all of these is a concerning issue. There are various methods to categorize emails, such as Naïve Bayes, SVM etc. This Paper proposes a system to predict spam mails using machine learning in Google Colab a Jupyter notebook environment.
Email Classification, Spam Filtering, Email classification
Email Classification, Spam Filtering, Email classification
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 9 |

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