I Built an ML-Powered Email Validation API
The article discusses the development of an ML-powered email validation API designed to identify disposable emails. It utilizes an XGBoost model to assess the legitimacy of email addresses based on various features. The API combines traditional validation methods with machine learning to improve accuracy in distinguishing between valid and disposable emails.
- ▪The API uses an XGBoost model to catch auto-generated disposable emails that traditional methods may miss.
- ▪It supports batch validation of up to 30 emails per request and excludes SMTP validation to reduce latency.
- ▪The model analyzes features such as digit count, length, and consonant/vowel ratios to predict email legitimacy.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3939733) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ozhaya Posted on May 19 I Built an ML-Powered Email Validation API #python #ai #api #machinelearning I built an ML model using XGBoost to catch auto-generated disposable emails when blacklists can't keep up. Most validators rely on MX records, SMTP checks, or blacklists - disposable emails have real mailboxes so MX and SMTP return valid. That's why I added an ML model to determine the risk of accepting an email based on the username and domain.
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