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