https://github.com/lakshitalearning/spamfortress

A machine learning-based project to detect SMS spam messages with high accuracy, using the SMS Spam Collection Dataset and techniques like supervised learning, text preprocessing, and model comparison.
https://github.com/lakshitalearning/spamfortress

data-science google-colab machine-learning nlp scikit-learn sms-spam-detection

Last synced: 21 days ago
JSON representation

A machine learning-based project to detect SMS spam messages with high accuracy, using the SMS Spam Collection Dataset and techniques like supervised learning, text preprocessing, and model comparison.

• Host: GitHub
• URL: https://github.com/lakshitalearning/spamfortress
• Owner: Lakshitalearning
• Created: 2024-07-20T19:34:36.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-09-01T07:34:06.000Z (5 months ago)
• Last Synced: 2024-11-15T19:42:13.875Z (3 months ago)
• Topics: data-science, google-colab, machine-learning, nlp, scikit-learn, sms-spam-detection
• Language: Jupyter Notebook
• Homepage:
• Size: 361 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README

Awesome Lists containing this project

README

        

SMS Spam Detection Project

Overview

This project develops a machine learning-based SMS spam detection system using the SMS Spam Collection Dataset.

Dataset

The SMS Spam Collection Dataset contains 5,574 SMS messages labeled as spam or ham.

Features

- Spam Detector: A robust machine learning model for high accuracy

- Data Preprocessing: Cleaning and preprocessing for optimal performance

- Feature Extraction: Extraction of relevant features to boost performance

- Evaluation Metrics: Precision, recall, and F1-score

Techniques Used

- Supervised Learning: Training with labeled data for optimal performance

- Text Preprocessing: Tokenization, stemming, and vectorization

- Model Comparison: Comparison of multiple machine learning algorithms

Tech Stack

- Python: Backend development and data analysis

- Google Collab: Cloud-based development and collaboration

- Pandas, NumPy, and Scikit-learn: Data manipulation and machine learning tasks

Web Development Twist

Integration of web development techniques to enhance user experience and functionality

Outcome

- Highly accurate SMS spam detection system

- Hands-on experience with machine learning, text preprocessing, and Google Collab

Repository Contents

- Data: SMS Spam Collection Dataset

- Code: Python scripts for data preprocessing, feature extraction, model training, and evaluation

- Models: Trained machine learning models for spam detection