{"id":23279921,"url":"https://github.com/andrewsy1004/logistic-regression-spam-classifier","last_synced_at":"2026-05-06T06:34:25.037Z","repository":{"id":268266394,"uuid":"903813360","full_name":"Andrewsy1004/Logistic-Regression-Spam-Classifier","owner":"Andrewsy1004","description":"This project implements a spam email classifier using Logistic Regression.","archived":false,"fork":false,"pushed_at":"2024-12-15T16:23:00.000Z","size":210,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-06T13:19:01.456Z","etag":null,"topics":["numpy","pandas","scikit-learn"],"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/Andrewsy1004.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":"2024-12-15T16:15:43.000Z","updated_at":"2024-12-15T16:23:03.000Z","dependencies_parsed_at":"2024-12-15T17:37:30.884Z","dependency_job_id":null,"html_url":"https://github.com/Andrewsy1004/Logistic-Regression-Spam-Classifier","commit_stats":null,"previous_names":["andrewsy1004/logistic-regression-spam-classifier"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Andrewsy1004/Logistic-Regression-Spam-Classifier","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Andrewsy1004%2FLogistic-Regression-Spam-Classifier","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Andrewsy1004%2FLogistic-Regression-Spam-Classifier/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Andrewsy1004%2FLogistic-Regression-Spam-Classifier/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Andrewsy1004%2FLogistic-Regression-Spam-Classifier/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Andrewsy1004","download_url":"https://codeload.github.com/Andrewsy1004/Logistic-Regression-Spam-Classifier/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Andrewsy1004%2FLogistic-Regression-Spam-Classifier/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279005944,"owners_count":26083987,"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":"2025-10-10T02:00:06.843Z","response_time":62,"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":["numpy","pandas","scikit-learn"],"created_at":"2024-12-19T23:19:41.407Z","updated_at":"2025-10-11T01:47:48.864Z","avatar_url":"https://github.com/Andrewsy1004.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📬 Logistic Regression Spam Classifier\n\nThis project implements a **Spam Email Classifier** using **Logistic Regression**, trained on a dataset of SMS messages. The model distinguishes between **ham** (non-spam) and **spam** messages. This project demonstrates how to process text data, apply machine learning, and evaluate model performance. \n\n## 🚀 Features:\n- **Text Preprocessing**: The text data is cleaned and transformed using **TF-IDF Vectorization**, which converts the raw text into numerical feature vectors.\n- **Model Training**: A **Logistic Regression** model is trained to classify SMS messages as either \"ham\" or \"spam\".\n- **Model Evaluation**: Performance metrics such as **accuracy**, **precision**, **recall**, and **F1-score** are used to evaluate the model's effectiveness.\n\n## 📊 Steps:\n1. **Data Preprocessing**:\n   - The dataset is cleaned by removing stop words and converting all text to lowercase.\n   - The text is transformed into numerical features using the **TF-IDF** vectorizer.\n   \n2. **Training**:\n   - The **Logistic Regression** model is trained on the processed data.\n   \n3. **Evaluation**:\n   - The model is evaluated on both training and test datasets using multiple performance metrics (accuracy, precision, recall, F1-score).\n   \n## 📋 Dependencies:\n- `pandas`: For data manipulation and handling.\n- `numpy`: For numerical operations.\n- `scikit-learn`: For machine learning models, including logistic regression and vectorization.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandrewsy1004%2Flogistic-regression-spam-classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fandrewsy1004%2Flogistic-regression-spam-classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandrewsy1004%2Flogistic-regression-spam-classifier/lists"}