-
-
Notifications
You must be signed in to change notification settings - Fork 216
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
[Project Addition]: End to End Email Spam Classifier #626
Conversation
Our team will soon review your PR. Thanks @codewithpiyushh :) |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
- Project folder name should be same as the issue name with no hyphens.
- Create a README file inside the Web App folder and follow the template. Add a demonstration video of the working of the web app and add that inside the README of the web app.
- Follow the README template and update the README of the Models folder accordingly. Here is the template, https://github.com/abhisheks008/ML-Crate/blob/main/.github/readme_template.md
- For your reference, follow this project's structure Brain Tumor Detection. Look at the files pushed in the project and update the same in your project folder too.
Need these changes @codewithpiyushh
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Approved @codewithpiyushh
Hi @codewithpiyushh please share your email address for further communications. |
my email is piyushhhsinghh@gmail.com |
Pull Request for ML-Crate 💡
Issue Title: End to End Email spam classifier
Closes: #614 #issue number that will be closed through this PR
Describe the add-ons or changes you've made 📃
machine learning model that classifies emails as spam or not spam. It leverages Python and popular libraries like scikit-learn to train and evaluate various classification algorithms. The model aims to identify spam emails with high accuracy, protecting users from unwanted content.
Type of change ☑️
What sort of change have you made:
How Has This Been Tested? ⚙️
Describe how it has been tested
Describe how have you verified the changes made
Checklist: ☑️