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The model with accuracy score 0.98 learns the email and creates a 'bag of words', from which the probability of an email to be spam with the presence of that word is calculated. Test emails are checked and if those threat words are found it is labeled as spam.

Python 1.35% HTML 0.71% Jupyter Notebook 97.93%
flask html jinja naive-bayes-algorithm pickle pthon

email_spam_verifier_99's Introduction

Hi there ๐Ÿ‘‹

๐Ÿ’ซ About Me:

๐Ÿ”ญ Iโ€™m currently working in Oracle Cloud - Tech Cloud Analytics team as an Engineer.
๐Ÿ‘ฉโ€๐Ÿ’ป๐ŸŸก Gold Medalist in BTech Honors in Computer Science and Engineering.
๐Ÿค Iโ€™m looking for a Data Scientist / Machine Learning Engineer role.
๐ŸŒฑ Interested in the newest innovations in Data Science Field.
๐Ÿ“ซ How to reach me: [email protected]
โšก Fun Fact - I like bursting my ears with music while I'm working.
๐ŸŒ Check out my Website!

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LinkedIn Kaggle TechGig Medium Leetcode

๐Ÿ’ป Tech Stack:

MySQL scikit-learn SciPy NumPyC++ C PHP HTML5 Java Python Bootstrap jQuery

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