Spam_Email_Detection #176
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Spam_Email_Detection #176
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miraj0507
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GSSOC 23
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Related Issue
Info about the related issue
GSSOC Participant
Contributor
Closes: issue #116
Describe the changes you've made
This project utilizes Natural Language Processing (NLP) techniques to develop a machine learning model for email spam detection. The model analyzes the text content of emails and classifies them as either spam or ham. It involves preprocessing the text, extracting relevant features, training a machine learning model, and evaluating its performance. The goal is to create an accurate and reliable spam detection system that can be deployed in email clients or servers to enhance email security and reduce the impact of spam.
How Has This Been Tested?
I have verified the changes made by utilizing a confusion matrix. It allowed me to compare the predicted labels with the ground truth labels, analyze performance metrics, and fine-tune the model for accurate email spam detection.
Checklist:
My code follows the guidelines of this project.
I have performed a self-review of my own code.
I have commented my code, particularly wherever it was hard to understand.
I have made corresponding changes to the documentation.
My changes generate no new warnings.
I have added tests that prove my fix is effective or that my feature works.
Any dependent changes have been merged and published in downstream modules.
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