{"id":27899528,"url":"https://github.com/tansexe/ad-lab","last_synced_at":"2025-08-25T12:27:41.187Z","repository":{"id":272396629,"uuid":"916454789","full_name":"tansexe/AD-Lab","owner":"tansexe","description":"Basics of Data Analysis \u0026 ML","archived":false,"fork":false,"pushed_at":"2025-03-25T14:36:02.000Z","size":5293,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-25T15:41:42.584Z","etag":null,"topics":["data-visualization","eda","ml"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tansexe.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":"2025-01-14T06:01:13.000Z","updated_at":"2025-03-25T14:36:06.000Z","dependencies_parsed_at":"2025-01-14T07:19:04.471Z","dependency_job_id":"8c598f57-14a7-4edd-a0a9-77523496dcab","html_url":"https://github.com/tansexe/AD-Lab","commit_stats":null,"previous_names":["tansexe/ad-lab"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tansexe%2FAD-Lab","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tansexe%2FAD-Lab/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tansexe%2FAD-Lab/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tansexe%2FAD-Lab/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tansexe","download_url":"https://codeload.github.com/tansexe/AD-Lab/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252563170,"owners_count":21768414,"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":["data-visualization","eda","ml"],"created_at":"2025-05-05T19:35:49.367Z","updated_at":"2025-05-05T19:35:49.916Z","avatar_url":"https://github.com/tansexe.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Applications Development Lab\n\nThis project is part of the **Applications Development Lab** course in the 6th semester. The project explores data analysis, correlation identification, application of machine learning models, and the creation of an end-to-end machine learning pipeline.\n\n## Project Overview\n\nThe main objective of this project is to analyze a dataset, explore different correlations, try various machine learning models, and develop a machine learning pipeline to streamline the entire process.\n\n### Key Highlights:\n- **Data Analysis**: Performed exploratory data analysis (EDA) to understand the dataset and its underlying patterns.\n- **Correlation Analysis**: Investigated correlations between various features of the dataset and visualized the findings.\n- **Machine Learning Models**: Tried several machine learning models to predict target variables and evaluated their performance.\n- **ML Pipeline**: Developed a machine learning pipeline to automate the process from data preprocessing to model evaluation.\n\n## Installation\n\nTo run this project locally, you need to have the following libraries installed:\n\n- Python 3.x\n- Pandas\n- Numpy\n- Matplotlib\n- Seaborn\n- Scikit-learn\n\nYou can install the required libraries using pip:\n\n```bash\npip install -r requirements.txt\n```\n\n## Usage\n\n1. **Data Preprocessing**: \n   - The dataset is loaded and cleaned.\n   - Missing values are handled, and categorical variables are encoded.\n   \n2. **Exploratory Data Analysis (EDA)**:\n   - Visualizations are created to explore the data and understand the relationships between features.\n   \n3. **Modeling**:\n   - Various machine learning models, such as Linear Regression, Decision Trees, and Random Forest, are tested.\n   \n4. **Machine Learning Pipeline**:\n   - A pipeline is created to automate data preprocessing, model training, and evaluation.\n\n## Evaluation\n\nThe performance of the models is evaluated using appropriate metrics such as accuracy, precision, recall, and F1-score.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Acknowledgments\n\n- Inspired by the concepts covered in the Applications Development Lab curriculum.\n- Special thanks to the course instructors for their support and guidance.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftansexe%2Fad-lab","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftansexe%2Fad-lab","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftansexe%2Fad-lab/lists"}