I Built an ML-Powered Email Validation API

1. 1Build an ML model using XGBoost to classify emails as valid or disposable
2. 2Train the model on a dataset of labeled emails
3. 3Deploy the model as a RESTful API using a framework like Flask or Django
4. 4Test the API with a sample dataset of emails
5. 5Configure the API to handle requests and return validation results

Developers and data scientists on a team can benefit from this API to improve email validation, while product managers can use it to enhance customer engagement and reduce bounce rates

💡 XGBoost can be used to build an effective ML model for catching auto-generated disposable emails

🚀 Boost email deliverability with ML-powered validation API! 📧