{"id":22751715,"url":"https://github.com/snehawk20/log_anomaly_detection","last_synced_at":"2025-09-10T05:12:53.862Z","repository":{"id":109590862,"uuid":"603596280","full_name":"snehawk20/log_anomaly_detection","owner":"snehawk20","description":"Detecting anomalous log entries","archived":false,"fork":false,"pushed_at":"2023-02-19T02:51:33.000Z","size":15731,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-05T08:51:25.280Z","etag":null,"topics":["logistic-regression","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":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/snehawk20.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":"2023-02-19T01:45:24.000Z","updated_at":"2023-02-19T02:52:28.000Z","dependencies_parsed_at":"2023-03-22T16:05:25.316Z","dependency_job_id":null,"html_url":"https://github.com/snehawk20/log_anomaly_detection","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/snehawk20%2Flog_anomaly_detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/snehawk20%2Flog_anomaly_detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/snehawk20%2Flog_anomaly_detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/snehawk20%2Flog_anomaly_detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/snehawk20","download_url":"https://codeload.github.com/snehawk20/log_anomaly_detection/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246285666,"owners_count":20752953,"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":["logistic-regression","tfidf-vectorizer"],"created_at":"2024-12-11T05:06:53.564Z","updated_at":"2025-03-30T06:42:50.518Z","avatar_url":"https://github.com/snehawk20.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Log Anomaly Detection\n\nThis solution was submitted to round 1 of Convolve, an ML/AI hackathon jointly organized by 6 IITs. \n\nIn computing, logging is the act of keeping a log of events that occur in a computer system, such as problems, errors or just information on current operations. These events may occur in the operating system or in other software. A message or log entry is recorded for each such event. Log Anomaly Detection is simply detecting anomalies in logs deposited by softwares using Machine Learning.\n\nAnomaly is anything that is different from what is usually perceived as normal - an exception. In software engineering, anomaly can be defined as occurrence of rare or unexpected events that does not fit into the normal patterns and hence a suspicious one.\n\nThe train and test set contain logs generated by software. Our task is to train a ML model on the given training data that can predict whether a given log in testing data is an anomaly or normal.\n\nPlease find the Kaggle page for the above contest [here](https://www.kaggle.com/competitions/convolve-epoch1/data).\n\n``log_detection.ipynb`` - final submission notebook. runs in `Python3`\n``train.json`` - training data\n``test.json``  - testing data\nTrain set is too large to be pushed. It can be found on Kaggle.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsnehawk20%2Flog_anomaly_detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsnehawk20%2Flog_anomaly_detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsnehawk20%2Flog_anomaly_detection/lists"}