{"id":24644946,"url":"https://github.com/lkethridge/machine_learning_in_business","last_synced_at":"2025-10-19T19:11:56.494Z","repository":{"id":273451924,"uuid":"919747385","full_name":"LKEthridge/Machine_Learning_in_Business","owner":"LKEthridge","description":"Machine Learning in Business project for TripleTen","archived":false,"fork":false,"pushed_at":"2025-01-21T01:11:47.000Z","size":12724,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-20T15:14:05.950Z","etag":null,"topics":["bootstrapping","business-metrics","confidence-interval","conversion","cross-validation","data-collection","data-labelling","data-sources","funnels","machine-learning","margin","net-profit-margin","operating-profit","python","return-on-investment","revenue","sklearn"],"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":"2025-01-20T23:52:20.000Z","updated_at":"2025-01-21T01:11:51.000Z","dependencies_parsed_at":"2025-01-21T02:29:06.362Z","dependency_job_id":null,"html_url":"https://github.com/LKEthridge/Machine_Learning_in_Business","commit_stats":null,"previous_names":["lkethridge/machine_learning_in_business"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LKEthridge%2FMachine_Learning_in_Business","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LKEthridge%2FMachine_Learning_in_Business/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LKEthridge%2FMachine_Learning_in_Business/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LKEthridge%2FMachine_Learning_in_Business/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LKEthridge","download_url":"https://codeload.github.com/LKEthridge/Machine_Learning_in_Business/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244637101,"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":["bootstrapping","business-metrics","confidence-interval","conversion","cross-validation","data-collection","data-labelling","data-sources","funnels","machine-learning","margin","net-profit-margin","operating-profit","python","return-on-investment","revenue","sklearn"],"created_at":"2025-01-25T14:13:41.517Z","updated_at":"2025-10-19T19:11:56.363Z","avatar_url":"https://github.com/LKEthridge.png","language":"Jupyter Notebook","readme":"# Machine_Learning_in_Business\n## *This was a Machine Learning in Business project for TripleTen. 👩🏽‍💻*\nThis project leveraged machine learning and bootstrapping to identify an optimal region among three options for fictional energy company OilyGiant’s expansion, focusing on maximizing profit and minimizing risk. Using a linear regression model and a dataset of 100,000 data points, Region 2 emerged as the best choice, with an average potential profit exceeding $4 million, a 95% confidence interval predicting positive returns, and only a 1.8% risk of loss. These findings provide a data-driven framework for OilyGiant to allocate resources effectively and maximize profitability.\n## Skills Highlighted\n🐍 Python and sklearn\n🤖 Machine Learning and Cross-Validation\n👩🏽‍💻 Data Collection and Labelling\n💰 Business Metrics: Calculating Revenue, Operating Profit, Margin, and Return on Investment\n📊 Statistical Methods: Bootstrapping and Confidence Intervals\n💿 Data Sources\n## Installation \u0026 Usage\n* This project uses pandas, numpy, train_test_split, StandardScaler, shuffle, LinearRegression, accuracy_score, mean_squared_error, and matplotlib.pyplot.  It requires python 3.11.\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flkethridge%2Fmachine_learning_in_business","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flkethridge%2Fmachine_learning_in_business","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flkethridge%2Fmachine_learning_in_business/lists"}