{"id":15009523,"url":"https://github.com/saradindusengupta/email_spam_filter","last_synced_at":"2026-04-04T00:04:17.228Z","repository":{"id":81657792,"uuid":"107645490","full_name":"saradindusengupta/email_spam_filter","owner":"saradindusengupta","description":"This model mainly uses deep neural network to train and classify between spam and ham messages and compare them with SVM and random forest.","archived":false,"fork":false,"pushed_at":"2017-10-22T08:35:07.000Z","size":210,"stargazers_count":3,"open_issues_count":1,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-12T14:46:31.634Z","etag":null,"topics":["deep-learning","keras-neural-networks","python-3-5","sklearn","tensorflow"],"latest_commit_sha":null,"homepage":null,"language":"Python","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/saradindusengupta.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":"2017-10-20T07:21:26.000Z","updated_at":"2021-03-18T15:46:45.000Z","dependencies_parsed_at":null,"dependency_job_id":"6c15a3ef-5978-4112-adc0-16b882fdd8a9","html_url":"https://github.com/saradindusengupta/email_spam_filter","commit_stats":{"total_commits":6,"total_committers":1,"mean_commits":6.0,"dds":0.0,"last_synced_commit":"c4a9c785c84718c582cc60d831dd1ee105780c26"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/saradindusengupta/email_spam_filter","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saradindusengupta%2Femail_spam_filter","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saradindusengupta%2Femail_spam_filter/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saradindusengupta%2Femail_spam_filter/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saradindusengupta%2Femail_spam_filter/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/saradindusengupta","download_url":"https://codeload.github.com/saradindusengupta/email_spam_filter/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saradindusengupta%2Femail_spam_filter/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31382355,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-03T23:20:52.058Z","status":"ssl_error","status_checked_at":"2026-04-03T23:20:51.675Z","response_time":107,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["deep-learning","keras-neural-networks","python-3-5","sklearn","tensorflow"],"created_at":"2024-09-24T19:26:06.480Z","updated_at":"2026-04-04T00:04:17.190Z","avatar_url":"https://github.com/saradindusengupta.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Email_Spam_Filter\nThis project uses deep neural network model to classify spam messages  and compare the performance with other machine learning model such as Xgboost ,SVM and random forest. This project uses the Enron dataset available here : http://www2.aueb.gr/users/ion/data/enron-spam/.\nThis approach is combines unsupervised learning with Supervised learning. We will generate the features using TF-IDF algorithm and then use this to features to train Models on labeled enron data.\n\n# Model trained and evaluated :\n\nDeep Learning model trained using keras and tensorflow\nSVM\nRandom Forest\nXGboost\nDeep learning model performs very well on this dataset\n\n# Dependencies :\n\nLanguage - Python 3.5\n\nkeras : https://keras.io/\n\ntensorflow : https://www.tensorflow.org/\n\nsklearn: http://scikit-learn.org/stable\n\nnumpy : http://www.numpy.org/\n\npickle: https://docs.python.org/2/library/pickle.html\n\nseaborn: https://seaborn.pydata.org/\n\nTo run the model make sure the above dependencies are met and the dataset from [http://www2.aueb.gr/users/ion/data/enron-spam/] is at /home destination. \nFor linux environment use the chmod to grant execution permission\n``` \n sudo chmod 777 spam-filter_classifier.py \n```\nThen type \n```\n python3 spam-filter_classifier.py     // for Python 3 developemnt\n```\nThe finding and analysis are available at spam-filter_classifier.pdf.\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaradindusengupta%2Femail_spam_filter","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaradindusengupta%2Femail_spam_filter","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaradindusengupta%2Femail_spam_filter/lists"}