{"id":25208290,"url":"https://github.com/jibbs1703/classify-text-models","last_synced_at":"2025-04-05T04:19:45.925Z","repository":{"id":235867979,"uuid":"780929648","full_name":"jibbs1703/Classify-Text-Models","owner":"jibbs1703","description":"This repository contains projects that classify texts using a variety of machine learning and deep learning models. 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The projects demonstrate real-world use-cases of Natural Language Processing. The tasks completed cover disaster tweet classification, spam message detection, and fake news recognition.\n\n## Projects\n\n### **Disaster Tweets Classification**\n- **Dataset**: Disaster tweets from [Kaggle](https://www.kaggle.com/competitions/nlp-getting-started).\n- **Achievement**: F1 accuracy scores: Logistic Regression (79.25%), Complement Naive Bayes (78.42%).\n- **Process**: Developed supervised machine learning models to classify tweets as disaster-related (1) or non-disaster-related (0).\n\n### **Fake News Recognition**\n- **Dataset**: Fake and real news articles from [Kaggle](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset/data).\n- **Achievement**: Logistic Regression Cross Validation Model achieved 99.51% F1 score.\n- **Process**: Developed models to classify news articles as true (1) or fake (0).\n\n### **Spam Message Detection**\n- **Dataset**: Spam email messages from [Kaggle](https://www.kaggle.com/datasets/ashfakyeafi/spam-email-classification).\n- **Achievement**: Logistic Regression Cross Validation Model achieved 94.37% F1 score.\n- **Process**: Developed models to classify emails as spam (1) or ham (0).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjibbs1703%2Fclassify-text-models","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjibbs1703%2Fclassify-text-models","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjibbs1703%2Fclassify-text-models/lists"}