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Email/SMS Detector

Created by: techwizard
1 devlog
1h 50m 46s

An intelligent Spam Classifier developed using Python, Scikit-Learn, and NLTK. The application utilizes Natural Language Processing (NLP) to analyze text patterns and a Naive Bayes model to accurately predict whether a message is 'Spam' or 'Not Sp…

An intelligent Spam Classifier developed using Python, Scikit-Learn, and NLTK. The application utilizes Natural Language Processing (NLP) to analyze text patterns and a Naive Bayes model to accurately predict whether a message is ‘Spam’ or ‘Not Spam’.”

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techwizard
techwizard shipped Email/SMS Detector
3 months ago

Shipped this project!

I created an AI-powered Spam Classifier to filter out junk messages. It works by converting text into numerical vectors and feeding them into a trained machine learning model to predict if a message is “Spam” or “Ham.”

Key takeaways:

Model Persistence: Learned that saving an empty model doesn’t work (oops!).
Deployment: Mastered the art of configuring environment variables on Render.
NLP: Got hands-on experience with tokenization and stemming.
It feels great to see it running live after solving those crushing errors!

techwizard
techwizard worked on Email/SMS Detector
3 months ago
1h 50m logged

I’m excited to share that I’ve successfully deployed my latest Machine Learning project: an SMS & Email Spam Detector.

💡 The Problem: We all get annoying spam messages. I wanted to build a model that could filter them out in real-time using Natural Language Processing (NLP).

🛠️ The Tech Stack:

Python & Scikit-Learn: For building the model (tested Naive Bayes, SVM, and Voting Classifiers).

NLTK: For text preprocessing (tokenization, stemming, stopword removal).

Streamlit: For creating the interactive web interface.

Render: For cloud deployment.

📉 The Challenges: It wasn’t a straight path! I spent hours debugging NotFittedErrors, fixing sparse vs. dense matrix mismatches, and resolving dependency conflicts during deployment. But getting that green “Deploy Succeeded” checkmark was worth it.
#MachineLearning #Python #Streamlit #NLP #DataScience #OpenSource #Coding

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