{"id":18929072,"url":"https://github.com/tushard48/sms-spam-detection","last_synced_at":"2026-05-19T14:34:01.499Z","repository":{"id":251191925,"uuid":"836679253","full_name":"TusharD48/SMS-Spam-Detection","owner":"TusharD48","description":"This repository contains code and models for identifying spam SMS messages. It utilizes machine learning techniques to classify messages as spam or ham (non-spam).","archived":false,"fork":false,"pushed_at":"2024-08-01T11:53:48.000Z","size":1059,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-01T11:46:43.952Z","etag":null,"topics":["machine-learning","spam-detection","streamlit","tfidf-vectorizer"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/TusharD48.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-08-01T10:33:45.000Z","updated_at":"2024-08-01T18:09:20.000Z","dependencies_parsed_at":"2024-08-01T12:01:25.166Z","dependency_job_id":"26596783-1986-4353-9ec7-593afd310651","html_url":"https://github.com/TusharD48/SMS-Spam-Detection","commit_stats":null,"previous_names":["tushard48/sms-spam-detection"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/TusharD48/SMS-Spam-Detection","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TusharD48%2FSMS-Spam-Detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TusharD48%2FSMS-Spam-Detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TusharD48%2FSMS-Spam-Detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TusharD48%2FSMS-Spam-Detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/TusharD48","download_url":"https://codeload.github.com/TusharD48/SMS-Spam-Detection/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TusharD48%2FSMS-Spam-Detection/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":275777503,"owners_count":25526884,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-18T02:00:09.552Z","response_time":77,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning","spam-detection","streamlit","tfidf-vectorizer"],"created_at":"2024-11-08T11:29:41.682Z","updated_at":"2025-09-18T13:41:10.091Z","avatar_url":"https://github.com/TusharD48.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SMS-Spam-Detection\n[![MasterHead](https://editor.analyticsvidhya.com/uploads/32086heading.jpeg)](https://ww38.rishavchanda.io/)\n\n# Table of Content\n-  Introduction\n- Project Structure\n- Installation\n- Usage\n- Dataset\n- Model\n- Results\n- Contributing\n- License\n- Contact\n\n# Introduction\nThis project aims to build a machine learning model to detect spam messages in SMS. Spam detection is crucial for filtering out unwanted and potentially harmful messages, enhancing user experience, and improving communication security.\n\n# Project Structure\nThe project directory is structured as follows:\n```\nsms-spam-detection/\n├── data/\n│   ├── raw/\n│   ├── processed/\n├── notebooks/\n│   ├── sms-spam-detection.ipynb\n├── src/\n│   ├── data_preprocessing.py\n│   ├── model_training.py\n│   ├── spam_detection.py\n├── models/\n├── results/\n├── README.md\n\n```\n\n## Installation\n\n```\npython -m venv venv\nsource venv/bin/activate  # On Windows, use `venv\\Scripts\\activate`\npip install pandas numpy scikit-learn nltk xgboost\n```\n\n# Model and Technique\nThis project utilizes several machine learning techniques and models to perform spam detection:\n\n- Logistic Regression\n- Support Vector Classifier (SVC)\n- Multinomial Naive Bayes (MultinomialNB)\n- Decision Tree Classifier\n- K-Neighbors Classifier (KNeighborsClassifier)\n- Random Forest Classifier\n- AdaBoost Classifier\n- Bagging Classifier\n- Extra Trees Classifier\n- Gradient Boosting Classifier\n- XGBoost Classifier\n\nResults\nThe performance of the spam detection model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The results are stored in the results/ directory. Here is a summary of the model's performance:\n\n| Algorithm | Accuracy | Precision |\n|-----------|----------|-----------|\n| KN        | 0.904255 | 1.000000  |\n| NB        | 0.972921 | 0.991597  |\n| ETC       | 0.977756 | 0.984127  |\n| RF        | 0.971954 | 0.975410  |\n| GBDT      | 0.946809 | 0.968750  |\n| SVC       | 0.976789 | 0.954887  |\n| xgb       | 0.968085 | 0.937500  |\n| BgC       | 0.962282 | 0.884058  |\n| LR        | 0.946809 | 0.868852  |\n| AdaBoost  | 0.950677 | 0.867188  |\n| DT        | 0.933269 | 0.827586  |\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftushard48%2Fsms-spam-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftushard48%2Fsms-spam-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftushard48%2Fsms-spam-detection/lists"}