Built a Sentiment Analysis Web App – My First Full-Stack ML Project
Elchin Nasirov developed a Sentiment Analysis Web App as his first full-stack machine learning project. The app predicts whether a given text is positive or negative using a Random Forest model. Nasirov shares insights on the challenges faced and the importance of a well-rounded approach to machine learning projects.
- ▪The web app features a React frontend and a Flask backend.
- ▪It uses a Random Forest model with TF-IDF for text processing.
- ▪Nasirov learned about the significance of model training data and deployment in machine learning.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 362090) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Elchin Nasirov Posted on May 27 Built a Sentiment Analysis Web App – My First Full-Stack ML Project #machinelearning #webdev #programming #react Hey dev.to 👋 After spending a month learning Machine Learning through Andrew Ng’s specialization, I wanted to build something real — not just notebooks. So I created a Sentiment Analysis Web App — a full-stack project that takes any text and predicts whether it's Positive or Negative.
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