{"id":21887877,"url":"https://github.com/arjuntheprogrammer/decision-trees-random-forests-adaboost-xgboost-in-python","last_synced_at":"2025-09-03T09:40:45.726Z","repository":{"id":101774246,"uuid":"279569104","full_name":"arjuntheprogrammer/Decision-Trees-Random-Forests-AdaBoost-XGBoost-in-Python","owner":"arjuntheprogrammer","description":"Decision Trees, Random Forests, AdaBoost \u0026 XGBoost in Python Udemy Course","archived":false,"fork":false,"pushed_at":"2020-07-14T12:08:27.000Z","size":2671,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-26T20:29:50.292Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/arjuntheprogrammer.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":"2020-07-14T11:41:47.000Z","updated_at":"2024-10-06T04:33:05.000Z","dependencies_parsed_at":null,"dependency_job_id":"84985fbb-b2fb-4feb-9a90-e49d6ec7124e","html_url":"https://github.com/arjuntheprogrammer/Decision-Trees-Random-Forests-AdaBoost-XGBoost-in-Python","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/arjuntheprogrammer%2FDecision-Trees-Random-Forests-AdaBoost-XGBoost-in-Python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arjuntheprogrammer%2FDecision-Trees-Random-Forests-AdaBoost-XGBoost-in-Python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arjuntheprogrammer%2FDecision-Trees-Random-Forests-AdaBoost-XGBoost-in-Python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arjuntheprogrammer%2FDecision-Trees-Random-Forests-AdaBoost-XGBoost-in-Python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/arjuntheprogrammer","download_url":"https://codeload.github.com/arjuntheprogrammer/Decision-Trees-Random-Forests-AdaBoost-XGBoost-in-Python/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244895155,"owners_count":20527843,"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":"2024-11-28T11:13:01.078Z","updated_at":"2025-03-22T02:22:36.358Z","avatar_url":"https://github.com/arjuntheprogrammer.png","language":"Jupyter Notebook","readme":"# Decision Trees, Random Forests, AdaBoost \u0026 XGBoost in Python\n\nCourse Link: \u003chttps://www.udemy.com/course/machine-learning-advanced-decision-trees-in-python/\u003e\n\n---\n\n## Course Contents\n\n* **Section 1** - Introduction to Machine Learning\n* **Section 2** - Python basic\n* **Section 3** - Pre-processing and Simple Decision trees\n* **Section 4** - Simple Classification Tree\n* **Section 5, 6 and 7** - Ensemble technique\n\n---\n\n## Objectives\n\n* Identify the business problem which can be solved using Decision tree/ Random Forest/ XGBoost  of Machine Learning.\n* Have a clear understanding of Advanced Decision tree based algorithms such as Random Forest, Bagging, AdaBoost and XGBoost\n* Create a tree based (Decision tree, Random Forest, Bagging, AdaBoost and XGBoost) model in Python and analyze its result.\n* Confidently practice, discuss and understand Machine Learning concepts.\n\n---\n\n## Learnings\n\n* Get a solid understanding of decision tree\n* Understand the business scenarios where decision tree is applicable\n* Tune a machine learning model's hyperparameters and evaluate its performance.\n* Use Pandas DataFrames to manipulate data and make statistical computations.\n* Use decision trees to make predictions\n* Learn the advantage and disadvantages of the different algorithms\n\n---\n\n## Completion Certificate\n\n\u003chttps://www.udemy.com/certificate/UC-9c547400-61ed-4edd-8eaf-f924583f37e7/\u003e\n\n![1000](https://udemy-certificate.s3.amazonaws.com/image/UC-9c547400-61ed-4edd-8eaf-f924583f37e7.jpg)\n\n---\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farjuntheprogrammer%2Fdecision-trees-random-forests-adaboost-xgboost-in-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farjuntheprogrammer%2Fdecision-trees-random-forests-adaboost-xgboost-in-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farjuntheprogrammer%2Fdecision-trees-random-forests-adaboost-xgboost-in-python/lists"}