{"id":15157777,"url":"https://github.com/sckonung/crab-age-regression","last_synced_at":"2026-02-17T07:39:44.574Z","repository":{"id":254442378,"uuid":"846549089","full_name":"scKonung/Crab-Age-Regression","owner":"scKonung","description":"ML model for regression with a crab age dataset Competition in Kaggle","archived":false,"fork":false,"pushed_at":"2024-08-23T13:19:58.000Z","size":9,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-13T17:18:04.767Z","etag":null,"topics":["keras","machine-learning","pandas","python","scikit-learn","tensorflow"],"latest_commit_sha":null,"homepage":"https://www.kaggle.com/competitions/playground-series-s3e16/overview","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/scKonung.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":"2024-08-23T12:52:24.000Z","updated_at":"2024-08-23T13:21:57.000Z","dependencies_parsed_at":"2024-08-23T14:17:44.486Z","dependency_job_id":null,"html_url":"https://github.com/scKonung/Crab-Age-Regression","commit_stats":null,"previous_names":["sckonung/crab-age-regression"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scKonung%2FCrab-Age-Regression","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scKonung%2FCrab-Age-Regression/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scKonung%2FCrab-Age-Regression/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scKonung%2FCrab-Age-Regression/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/scKonung","download_url":"https://codeload.github.com/scKonung/Crab-Age-Regression/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247675631,"owners_count":20977376,"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":["keras","machine-learning","pandas","python","scikit-learn","tensorflow"],"created_at":"2024-09-26T20:03:35.064Z","updated_at":"2025-10-15T00:08:54.224Z","avatar_url":"https://github.com/scKonung.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Crab Age Prediction Model\n\nThis project marks my debut in Kaggle competitions, where I developed a straightforward model to predict the age of crabs.\n\n## Dataset Overview\n\nThe dataset I utilized for this model is sourced from [this dataset](https://www.kaggle.com/datasets/sidhus/crab-age-prediction). To enhance the model's input, I applied One Hot Encoding to the 'Sex' column, creating individual columns for each sex category.\u003cbr\u003e\n\n**Original Data Snapshot:**\u003cbr\u003e\n![Original Data](https://github.com/user-attachments/assets/b5f722de-80a5-4a35-b1ac-31a017afa7f8)\u003cbr\u003e\n\n**Encoded Data Snapshot:**\u003cbr\u003e\n![Encoded Data](https://github.com/user-attachments/assets/3bbf0b97-d16f-4999-add3-3eb5dc1d91ef)\u003cbr\u003e\n\n## Model Overview\n\nThe model architecture I employed consists of three Dense layers, with the first two layers having 32 output units each, and the final layer outputting a single value for age prediction. I utilized ReLU activation functions throughout.\n\n## Model Training and Optimization\n\nFor training optimization, I chose the RMSprop optimizer and Mean Squared Error (MSE) as the loss function. During the competition phase, I trained the model over 100 epochs with a batch size of 32.\n\nThis approach allowed me to create an effective baseline model for predicting crab ages, leveraging straightforward yet powerful techniques to tackle the problem at hand.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsckonung%2Fcrab-age-regression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsckonung%2Fcrab-age-regression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsckonung%2Fcrab-age-regression/lists"}