Spam Detection using Machine Learning & Web App 🛡️

A comprehensive collection of machine learning and deep learning models for spam detection, deployed as a Flask web application with real-time prediction.

Features

✅ Machine Learning Models

• Naive Bayes

• Support Vector Machines (SVM) (99.11% Max in Machine Learning Models)

• Random Forest

• Logistic Regression

• AdaBoost

• XGBoost

• Ensemble Learning

✅ Deep Learning Models

• LSTM

• BERT (99.79% Max in Deep Learning Models)

✅ Web Application

• Real-time spam prediction

• User-friendly Flask interface

• Contact form with email notifications

• Responsive frontend

✅ Technical Highlights

• Cross-validation for model evaluation

• Customizable preprocessing (TF-IDF, lemmatization, etc.)

Web Application Interface

Conclusion:

This repository provides a comprehensive collection of machine learning models for spam detection, including both traditional algorithms and deep learning models. The models can be used for email or text-based spam detection in various applications. Additionally, the repository supports different data preprocessing techniques and includes cross-validation for model evaluation.