{"id":22013056,"url":"https://github.com/ngangawairimu/decision-tree-model","last_synced_at":"2026-05-11T07:49:29.636Z","repository":{"id":238071704,"uuid":"795810859","full_name":"ngangawairimu/Decision-tree-model","owner":"ngangawairimu","description":"Analyzed global population data using decision tree regression, calculating growth rates and evaluating model performance with RMSLE for insights.","archived":false,"fork":false,"pushed_at":"2024-10-29T06:51:37.000Z","size":11,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-23T08:23:39.751Z","etag":null,"topics":["matplotlib","python","sckiit-learn","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/ngangawairimu.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-05-04T05:57:37.000Z","updated_at":"2024-10-29T06:58:01.000Z","dependencies_parsed_at":"2024-05-04T06:34:23.298Z","dependency_job_id":"00ce6998-5266-4363-a708-cac2ce935cc4","html_url":"https://github.com/ngangawairimu/Decision-tree-model","commit_stats":null,"previous_names":["ngangawairimu/decision-tree-model"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ngangawairimu/Decision-tree-model","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ngangawairimu%2FDecision-tree-model","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ngangawairimu%2FDecision-tree-model/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ngangawairimu%2FDecision-tree-model/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ngangawairimu%2FDecision-tree-model/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ngangawairimu","download_url":"https://codeload.github.com/ngangawairimu/Decision-tree-model/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ngangawairimu%2FDecision-tree-model/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261808050,"owners_count":23212690,"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":["matplotlib","python","sckiit-learn","seaborn"],"created_at":"2024-11-30T03:16:49.880Z","updated_at":"2026-05-11T07:49:24.599Z","avatar_url":"https://github.com/ngangawairimu.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"### Decision Tree Regression Analysis on Population Data\nObjective: To analyze population data from various countries using decision tree regression, aiming to predict future population growth based on historical trends.\n\n\n## Expected Outcomes:\n- Trained Model: A trained Decision Tree regression model capable of predicting population growth rates.\n\n- Predictions: The model should provide population growth predictions for the year 2017 (or any specified year).\n\n- Model Performance: An RMSLE score that helps quantify how close the model's predictions are to the actual growth rates. A lower RMSLE indicates better predictive performance.\n\n#### Model Training and Evaluation:\n\nSplit the dataset into training and testing sets to ensure a robust evaluation of the model's performance.\nTrain a decision tree regression model using the training set.\nEvaluate the model's performance using Root Mean Squared Logarithmic Error (RMSLE) to understand its accuracy.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fngangawairimu%2Fdecision-tree-model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fngangawairimu%2Fdecision-tree-model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fngangawairimu%2Fdecision-tree-model/lists"}