{"id":25203422,"url":"https://github.com/antim21/spamsense-ai","last_synced_at":"2026-05-04T20:36:33.031Z","repository":{"id":274045149,"uuid":"921734547","full_name":"Antim21/SpamSense-AI","owner":"Antim21","description":"Classifying emails into Spam or Not Spam categories using Machine Learning techniques","archived":false,"fork":false,"pushed_at":"2025-03-15T11:13:56.000Z","size":315,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-15T12:22:19.911Z","etag":null,"topics":["machine-learning","nlp","python","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":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Antim21.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2025-01-24T14:12:56.000Z","updated_at":"2025-03-15T11:13:59.000Z","dependencies_parsed_at":"2025-03-15T18:31:18.241Z","dependency_job_id":null,"html_url":"https://github.com/Antim21/SpamSense-AI","commit_stats":null,"previous_names":["antim21/spamsense-ai"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Antim21%2FSpamSense-AI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Antim21%2FSpamSense-AI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Antim21%2FSpamSense-AI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Antim21%2FSpamSense-AI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Antim21","download_url":"https://codeload.github.com/Antim21/SpamSense-AI/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247261583,"owners_count":20910107,"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","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","nlp","python","scikit-learn"],"created_at":"2025-02-10T07:17:22.709Z","updated_at":"2026-05-04T20:36:32.956Z","avatar_url":"https://github.com/Antim21.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"               \n # SpamSense-AI       \n \nThis project focuses on classifying emails into Spam or Not Spam categories using Machine Learning techniques. It is implemented in a Jupyter Notebook and provides a step-by-step approach to building and evaluating the classification model. \n \n**Key Features**    \n  \nPreprocessing of raw email data (e.g., cleaning, tokenization, and vectorization).  \nImplementation of multiple classification algorithms like Naive Bayes, Logistic Regression, or SVM.    \nPerformance evaluation using metrics like accuracy, precision, recall, and F1-score. \nVisualization of results through plots and charts for better understanding. \n     \n**Tech Stack**  \n           \nPython  \nJupyter Notebook \n    \nLibraries Used:\n\n scikit-learn\npandas\nnumpy\nmatplotlib\nseaborn\n\n\n# How It Works\n\n**Data Preprocessing:**\n\nRemoval of special characters, stopwords, and unwanted symbols.\nConversion of text into numerical features using techniques like TF-IDF or Bag of Words.\n\n**Model Training:**\n\nMultiple models are trained on the processed data to classify emails into Spam or Not Spam categories.\n\n**Evaluation:**\n\nModels are evaluated using metrics and confusion matrix for performance analysis.\n\n**Visualization:**\n\nInsights are visualized with charts to show data distribution, feature importance, and model accuracy.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fantim21%2Fspamsense-ai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fantim21%2Fspamsense-ai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fantim21%2Fspamsense-ai/lists"}