{"id":24644920,"url":"https://github.com/lkethridge/integrated_project_2","last_synced_at":"2026-04-18T09:37:43.797Z","repository":{"id":273727798,"uuid":"919849254","full_name":"LKEthridge/Integrated_Project_2","owner":"LKEthridge","description":"Integrated Project 2 from TripleTen","archived":false,"fork":false,"pushed_at":"2025-01-22T16:00:17.000Z","size":15695,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-20T15:14:01.310Z","etag":null,"topics":["anomaly-detection","cross-validation","data-analytics","data-cleaning-and-preprocessing","data-science","feature-engineering","gold-recovery","machine-learning","metal-purification","model-evaluation","pandas","portfolio-project","python","scikit-learn","smape","supervised-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/LKEthridge.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":"2025-01-21T06:00:30.000Z","updated_at":"2025-01-22T16:00:24.000Z","dependencies_parsed_at":"2025-01-22T16:47:57.373Z","dependency_job_id":null,"html_url":"https://github.com/LKEthridge/Integrated_Project_2","commit_stats":null,"previous_names":["lkethridge/integrated_project_2"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LKEthridge%2FIntegrated_Project_2","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LKEthridge%2FIntegrated_Project_2/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LKEthridge%2FIntegrated_Project_2/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LKEthridge%2FIntegrated_Project_2/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LKEthridge","download_url":"https://codeload.github.com/LKEthridge/Integrated_Project_2/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244637100,"owners_count":20485446,"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":["anomaly-detection","cross-validation","data-analytics","data-cleaning-and-preprocessing","data-science","feature-engineering","gold-recovery","machine-learning","metal-purification","model-evaluation","pandas","portfolio-project","python","scikit-learn","smape","supervised-learning"],"created_at":"2025-01-25T14:13:36.579Z","updated_at":"2026-04-18T09:37:43.764Z","avatar_url":"https://github.com/LKEthridge.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Integrated_Project_2\n## *This was an Integrated skill project for TripleTen. 👩🏽‍💻*\nThis project developed a machine learning solution for predicting gold recovery at the rougher and final stages of ore processing using datasets with over 80 parameters. A Multi-Output Random Forest Regression model provided the most accurate predictions during training, with Linear Regression as a viable, less computationally intensive alternative. Despite underperforming compared to constant benchmarks on the test set, the models demonstrate the potential for data-driven optimization of industrial processes.\n## Skills Highlighted\n🐍 Python\n👩🏽‍💻 Data Science\n🤖 Machine Learning\n🧪 Scikit Learn\n❌ Cross Validation\n🐼 pandas\n📊 Data Analytics\n👀 Supervised Learning\n⚙️ Feature Engineering\n💯 Model Evaluation\n🕵🏽‍♀️ Anomaly Detection\n🧼 Data Cleaning and Preprocessing\n## Installation \u0026 Usage\n* This project uses pandas, numpy, RandomForestRegressor, MultiOutputRegressor, LinearRegression, mean_squared_error, mean_absolute_error, make_scorer, matplotlib.pyplot, shuffle, StandardScaler, seaborn, SimpleImputer, cross_val_score, KFold, and RandomizedSearchCV.  It requires python 3.9.6.  There is one additional file containing the full, unsplit test set that I was unable to upload due to upload limitations.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flkethridge%2Fintegrated_project_2","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flkethridge%2Fintegrated_project_2","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flkethridge%2Fintegrated_project_2/lists"}