sms-spam-classification

This is a SMS Spam Detection Project with Streamlit

Email spam filter using AWS SageMaker with Lambda and CloudFormation integration

项目基于SMS Spam Collection数据集，构建并优化了逻辑回归模型进行垃圾短信自动分类。 采用TF-IDF方法进行特征提取，使用梯度下降算法训练模型，并通过5折交叉验证和网格搜索优化超参 数。最终，模型准确率达到0.96，精确度、召回率和F1值均表现优异，研究还通过ROC曲线和精确度 召回率曲线进一步评估了分类效果。该模型在垃圾短信分类任务中展现了较高的性能与实际实用价值

One of the primary methods for spam mail detection is email filtering. It involves categorize incoming emails into spam and non-spam. Machine learning algorithms can be trained to filter out spam mails based on their content and metadata.

In this project we are using LSTM to classify texts as spam or ham.

Welcome to the "SMS Spam Detector" project! This machine learning model identifies whether a given SMS is spam or not, providing a valuable tool for spam detection and filtering.

Natural Language Processing

The project leverages Naive Bayes Classifiers, a family of algorithms based on Bayes’ Theorem, which presumes independence between predictive features. This theorem is crucial for calculating the likelihood of a message being spam based on various characteristics of the data.

In this project, concepts of Natural Language Processing were used with the help of various Classification algorithms. The output will be classified as Spam or Ham.

An interactive SMS Spam Detection application using Streamlit and machine learning. This app allows users to classify messages as spam or ham and view performance metrics for different models.

Classification for SMS Spam Collection Dataset using BERT

Spam Classification using Naive Bayes Classifier

This project uses Recurrent Neural Networks (RNNs) to classify SMS messages as spam or ham (legitimate). My goal is to develop an accurate and efficient spam detection system using deep learning techniques.

SMS Spam Detection using TF-IDF Vectorization, Logistic Regression, and GridSearchCV for text classification.

SMS and Email Spam Classifier end-to-end project, deployed on Streamlit

SMS Spam detection Using Machine Learning

SMS spam detection web application using Flask, TF-IDF, Random Forest, AdaBoost, and XGBoost.

This is a web application for the classification of SMS messages created using Streamlit.

Machine Learning-Based SMS Spam Detection System using NLP, TF-IDF Vectorization, Logistic Regression, Scikit-Learn, and Streamlit for Real-Time Text Classification.

An SMS spam classifier that can classify a message into Spam or Ham