{"id":18300479,"url":"https://github.com/comet-ml/hn-predictor","last_synced_at":"2025-04-09T09:29:38.986Z","repository":{"id":44917115,"uuid":"446966405","full_name":"comet-ml/hn-predictor","owner":"comet-ml","description":"Example Project for the Working Sessions for Standardizing the Experiment Series","archived":false,"fork":false,"pushed_at":"2022-02-17T17:38:36.000Z","size":37,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-04-03T02:54:46.614Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","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/comet-ml.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}},"created_at":"2022-01-11T20:10:30.000Z","updated_at":"2022-01-18T15:48:07.000Z","dependencies_parsed_at":"2022-09-05T11:51:43.710Z","dependency_job_id":null,"html_url":"https://github.com/comet-ml/hn-predictor","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/comet-ml%2Fhn-predictor","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/comet-ml%2Fhn-predictor/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/comet-ml%2Fhn-predictor/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/comet-ml%2Fhn-predictor/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/comet-ml","download_url":"https://codeload.github.com/comet-ml/hn-predictor/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248011979,"owners_count":21033107,"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":[],"created_at":"2024-11-05T15:12:35.353Z","updated_at":"2025-04-09T09:29:38.957Z","avatar_url":"https://github.com/comet-ml.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# hn-predictor\n\n## Objective\nThe business objective that we are trying to achieve is to maximize the traffic we receive from our company’s post on Hacker News.\n\nOur requirement is to create a model to predict the performance of a post on Hacker News.  Our model would help us optimize our post to maximize the likelihood that it will trend.\n\n## Technical Requirements\n\nHere we define the technical assumptions we are making for this project. These assumptions affect how code for running experiments should be written.\n\n### Artifacts Requirements\n\n#### Dataset Type Artifacts\n\nFor this project, all dataset type Artifacts should contain a training and validation dataset saved as pickle files. Artifact Metadata should follow this format.\n\n```\n{\n    \"filenames\": {\n        \"train\": \"\u003cname of train data pickle file\u003e\",\n        \"valid\": \"\u003cname of validation data pickle file\u003e\",\n    },\n    \"columns\": {\"\u003ccolumn name\u003e\": \"\u003cdescription of the data in the column\u003e\", ...},\n}\n```\nThe columns field should be a dictionary containing the name of the feature column as the key, and a description of the column as its value.\n\nOur Dataset Artifact fetching functions will assume this schema and will not work otherwise.\n\n### Experimentation Requirements\n\n1. When running an experiment, pass in a message (similar to a git commit message) using the command line args in order to keep the project organized\n2. When logging a prediction experiment, save the predictions as a `predictions.csv` and log them as an experiment asset\n3. Log trained model assets using `experiment.log_model()` so that they can be added to the [Model Registry](https://www.comet.ml/site/using-comet-model-registry/).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcomet-ml%2Fhn-predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcomet-ml%2Fhn-predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcomet-ml%2Fhn-predictor/lists"}