{"id":19867054,"url":"https://github.com/azurespheredev/display-advertising-challenge","last_synced_at":"2026-02-08T10:32:55.320Z","repository":{"id":235633537,"uuid":"777023939","full_name":"azurespheredev/display-advertising-challenge","owner":"azurespheredev","description":"Since the data are highly sparse, the basic methodology is to use logistic regression with appropriate quadratic/polynomial feature generation and regularization to make sophisticated and over-fitting-tractable models.","archived":false,"fork":false,"pushed_at":"2024-10-27T03:17:24.000Z","size":14,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-07-26T09:12:24.712Z","etag":null,"topics":["java","machine-learning","powershell","python"],"latest_commit_sha":null,"homepage":"","language":"Java","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/azurespheredev.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":"2024-03-25T03:18:30.000Z","updated_at":"2024-10-27T03:17:27.000Z","dependencies_parsed_at":"2024-04-24T03:49:47.267Z","dependency_job_id":"1cfe07a0-3c9a-4104-92be-33e11118f7e5","html_url":"https://github.com/azurespheredev/display-advertising-challenge","commit_stats":null,"previous_names":["azuresphere7/display-advertising-challenge","blitzsprinter/display-advertising-challenge","azurespheredev/display-advertising-challenge"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/azurespheredev/display-advertising-challenge","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/azurespheredev%2Fdisplay-advertising-challenge","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/azurespheredev%2Fdisplay-advertising-challenge/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/azurespheredev%2Fdisplay-advertising-challenge/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/azurespheredev%2Fdisplay-advertising-challenge/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/azurespheredev","download_url":"https://codeload.github.com/azurespheredev/display-advertising-challenge/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/azurespheredev%2Fdisplay-advertising-challenge/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29227739,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-08T09:43:19.170Z","status":"ssl_error","status_checked_at":"2026-02-08T09:42:55.556Z","response_time":57,"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":["java","machine-learning","powershell","python"],"created_at":"2024-11-12T15:28:12.560Z","updated_at":"2026-02-08T10:32:55.306Z","avatar_url":"https://github.com/azurespheredev.png","language":"Java","funding_links":[],"categories":[],"sub_categories":[],"readme":"Display Advertising Challenge\n=============================\n\nDescription\n-----------\nThis is the code was written for the [Kaggle Criteo Competition of CTR prediction](https://www.kaggle.com/c/criteo-display-ad-challenge). \n\nSince the data are highly sparse, the basic methodology is to use logistic regression with appropriate quadratic/polynomial feature generation and regularization to make sophisticated and over-fitting-tractable models. [Vowpal Wabbit](https://github.com/JohnLangford/vowpal_wabbit) is the major machine learning software used for this project. Since the data size is challenging in terms of my personal workstation (a single quad-core CPU), the techniques of feature selection and model training are selected based on the trade off between performance and CPU/RAM resource limit.\n\nDependencies and requirements\n-----------------------------\nPlease note that the code was written for my personal learning and practice in new features of Java 8 and Python 3.4 in Ubuntu 14.04. The code cannot be run in early versions of these two languages or other OSs. Compatibility is not considered here.\n\n* Java 8\n* Python 3.4\n* Maven 3\n* Redis 2.8\n* Pandas 0.14\n* Vowpal Wabbit 7.7\n* Java-based open source projects: (Maven will install them automatically)\n  - guava 17.0\n  - jedis 2.5.1\n  - commons-lang3 3.3.2\n \n\nHow to run\n----------\n- Copy train and test data file (train.csv, test.csv) to data folder\n- Compile the Java code by\n```\n$ cd display-ad-java\n$ mvn package # or mvn install\n```\n- Make sure a redis instance running at localhost:6379\n- Set the path of binary vw (VW_BIN) in run.sh, such as \n```\nexport VW_BIN=/path/to/vw/binary\n```\n- Finally, \n```\n$ cd work\n$ ../run.sh\n```\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fazurespheredev%2Fdisplay-advertising-challenge","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fazurespheredev%2Fdisplay-advertising-challenge","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fazurespheredev%2Fdisplay-advertising-challenge/lists"}