{"id":21056635,"url":"https://github.com/xprithvi/random-forest-regressor","last_synced_at":"2025-03-14T00:20:46.832Z","repository":{"id":195999179,"uuid":"694124990","full_name":"xPrithvi/Random-Forest-Regressor","owner":"xPrithvi","description":"This Jupyter notebook serves as a machine learning template to quickly make predictions and analyse feature importance in a dataset. ","archived":false,"fork":false,"pushed_at":"2023-09-20T19:06:37.000Z","size":2464,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-20T19:24:53.854Z","etag":null,"topics":["data-science","feature-extraction","machine-learning","random-forest","random-forest-regression","scikit-learn"],"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/xPrithvi.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}},"created_at":"2023-09-20T11:32:20.000Z","updated_at":"2023-09-20T11:35:21.000Z","dependencies_parsed_at":"2023-09-21T00:05:02.900Z","dependency_job_id":"be35d653-1879-4d39-9f16-6aebfe61e4a1","html_url":"https://github.com/xPrithvi/Random-Forest-Regressor","commit_stats":null,"previous_names":["xprithvi/random-forest-regressor"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xPrithvi%2FRandom-Forest-Regressor","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xPrithvi%2FRandom-Forest-Regressor/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xPrithvi%2FRandom-Forest-Regressor/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/xPrithvi%2FRandom-Forest-Regressor/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/xPrithvi","download_url":"https://codeload.github.com/xPrithvi/Random-Forest-Regressor/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243500742,"owners_count":20300777,"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":["data-science","feature-extraction","machine-learning","random-forest","random-forest-regression","scikit-learn"],"created_at":"2024-11-19T16:52:25.511Z","updated_at":"2025-03-14T00:20:46.757Z","avatar_url":"https://github.com/xPrithvi.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Random Forest Regressor\nThis Jupyter notebook serves as part of the data science pipeline by providing a quick and easy framework to\nperform feature enginnering, model training and feature importance analysis for data exploration. In this particular notebook,\nSci-Kit Learn's RandomForestRegressor was trained on information regarding [housing in Perth](https://www.kaggle.com/datasets/syuzai/perth-house-prices) to\nnumerically predict house prices based on floor space, suburb, number of bedrooms, etc. Feature importance analysis was performed using \nbuilt-in methods that calculate importance by node impurity. However, SHAP was also used to provide a more robust and in-depth analysis\nvia Shapley values.\n\n## Features\n\n- Model saving and loading.\n- Hyperparameter tuning via Bayesian optimization.\n- Feature importance analysis using tree node impurity and Shapley values.\n\n## Future Improvements\n\n- Custom user input to the model (involves writting a custom data encoder instead of using pandas.get_dummies()).\n- Reducing the disk size of saved models.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxprithvi%2Frandom-forest-regressor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fxprithvi%2Frandom-forest-regressor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxprithvi%2Frandom-forest-regressor/lists"}