{"id":19965527,"url":"https://github.com/eastonarcher/python-machine-learning","last_synced_at":"2025-07-25T11:04:27.071Z","repository":{"id":219889680,"uuid":"750161639","full_name":"EastonArcher/Python-Machine-Learning","owner":"EastonArcher","description":"⚡Explore machine learning concepts through Python scripts and Jupyter notebooks showcasing algorithms and applications","archived":false,"fork":false,"pushed_at":"2024-03-14T18:27:03.000Z","size":77,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-12T08:33:28.015Z","etag":null,"topics":["jupyter-notebook","llm","machine-learning","machine-learning-algorithms","python-machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Python","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/EastonArcher.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":"2024-01-30T05:15:13.000Z","updated_at":"2024-04-02T02:42:15.000Z","dependencies_parsed_at":"2024-02-29T01:45:15.671Z","dependency_job_id":"45b81891-c3a1-4245-9607-cd8ffd322985","html_url":"https://github.com/EastonArcher/Python-Machine-Learning","commit_stats":null,"previous_names":["eastonarcher/python-machine-learning"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EastonArcher%2FPython-Machine-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EastonArcher%2FPython-Machine-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EastonArcher%2FPython-Machine-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EastonArcher%2FPython-Machine-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/EastonArcher","download_url":"https://codeload.github.com/EastonArcher/Python-Machine-Learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241396782,"owners_count":19956408,"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":["jupyter-notebook","llm","machine-learning","machine-learning-algorithms","python-machine-learning"],"created_at":"2024-11-13T02:29:20.769Z","updated_at":"2025-03-01T17:16:57.154Z","avatar_url":"https://github.com/EastonArcher.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning Repository 📈\nThis repository contains a collection of Python scripts and Jupyter notebooks that demonstrate various machine learning algorithms, techniques, and applications. \n\n## Projects 📂\n\n### Music Recommendation 🎧\n* This script utilizes a Decision Tree Classifier to build a music recommender system. The model is trained on a dataset (music.csv) containing user information (age and gender) and their preferred music genres. The trained model generates a decision tree visualized in the music-recommender.dot file.\n\n#### ❔How to Use \n  1. Ensure you have the required dependencies installed (pandas and scikit-learn).\n  2. Run the script MusicRecommendationMachineLearning.py.\n  3. Explore the generated decision tree in the music-recommender.dot file.\n\n### Video Game Sales Predictor 🎮\n* This script predicts global video game sales using a Decision Tree Classifier. The model is trained on a dataset (vgsales.csv) containing various features related to video games. The script preprocesses the data, handles missing values, encodes categorical variables, and evaluates the model's accuracy.\n\n#### ❔How to Use \n  1. Install the required dependencies (pandas, scikit-learn).\n  2. Run the script VideoGameMachineLearning.py.\n  3. Check the accuracy of the model printed in the console.\n\n### Sports Predictions 🏀\n* This script predicts the \n\n#### ❔How to Use \n  1. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feastonarcher%2Fpython-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feastonarcher%2Fpython-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feastonarcher%2Fpython-machine-learning/lists"}