sanchana08

sanchana08 / SMS-Spam-Detection

Public

Compares 6 ML classifiers (Naive Bayes, Logistic Regression, KNN, Decision Tree, SVM, Random Forest) to detect spam messages. Uses TF-IDF vectorization and SMOTE for class balancing. Best model (SVM, ~98.2% accuracy) is saved for inference via CLI.

15
0
100% credibility
Found Apr 14, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

A project that teaches a computer to identify spam text messages versus safe ones using example data, then lets you check new messages quickly.

How It Works

1
🔍 Discover the Spam Checker

You stumble upon this handy project online that can spot spam in text messages like the ones you get on your phone.

2
📥 Gather the Files

Download the project files and a collection of real example messages labeled as spam or safe.

3
🛠️ Set Up Your Computer

Get your computer ready by adding the simple tools it needs to run the checker smoothly.

4
🚀 Train the Detector

Run the learning session with the examples, and watch it build a smart tool that remembers how to spot spam.

5
💬 Test a Message

Type in any text message you want to check, like a suspicious offer or a friend’s invite.

✅ See the Verdict

Right away, it tells you if the message is spam or safe, helping you avoid junk and stay secure.

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Star Growth

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AI-Generated Review

What is SMS-Spam-Detection?

This SMS spam detection GitHub project compares six machine learning classifiers—Naive Bayes, Logistic Regression, KNN, Decision Tree, SVM, and Random Forest—to spot spam in text messages. It processes the SMS Spam Collection dataset, handles class imbalance with balancing techniques and TF-IDF vectorization, then saves the best model (SVM at 98.2% accuracy) for inference. Python-based with Jupyter Notebooks, users get a CLI command like `python predict.py "free prize claim now"` that outputs SPAM or HAM instantly.

Why is it gaining traction?

It benchmarks classifiers transparently on a real-world imbalanced dataset, revealing winners like SVM without fluff. The CLI hooks developers for quick tests in SMS spam detection using machine learning workflows. Stands out versus bare notebooks by packaging the best model ready for experiments or baselines.

Who should use this?

ML beginners building SMS spam detection projects or reports. Backend devs prototyping filters for messaging apps or SMS gateways. Researchers comparing Bayes, SVM, and tree-based classifiers on spam data.

Verdict

Grab it for learning or kickstarting an SMS spam detection using machine learning GitHub project—CLI and docs make it approachable. 1.0% credibility score and 15 stars mean it's immature; treat as educational, not production-ready.

(178 words)

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