{"id":23009033,"url":"https://github.com/evaveli/supervised-learning","last_synced_at":"2025-04-02T16:12:33.031Z","repository":{"id":262698201,"uuid":"888072563","full_name":"evaveli/Supervised-Learning","owner":"evaveli","description":"Introduction to Machine Learning, Supervised Learning project. 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Manual Virtual Environment Setup\n\n1. Open the project `work2` in PyCharm\n2. Open the terminal in PyCharm (View \u003e Tool Windows \u003e Terminal)\n3. Optional: Verify current location being `work2` by `pwd`\n4. Optional: Navigate to `work2` with `cd`\n5. Create a virtual environment:\n\n   ```bash\n   # Windows\n   py -3.9 -m venv venv\n\n   # macOS/Linux\n   python3.9 -m venv venv\n   ```\n\n6. Activate the virtual environment:\n\n   ```bash\n   # Windows\n   venv\\Scripts\\activate\n\n   # macOS/Linux\n   source venv/bin/activate\n   ```\n\nIn front of the input line in the terminal it should now say `(venv)`\n\n### 2. Installing Dependencies\n\nWith the virtual environment activated:\n\n```bash\npip install -r requirements.txt\n```\n\nFrom here you can directly jump to [Run `app.py`](#3-run-apppy)\n\n### 3. Close Virtual Environment\n\nWith the virtual environment activated:\n\n```bash\ndeactivate\n```\n\nThe `(venv)` in front of the terminal should be gone\n\n## Project Execution instructions\n\n### 1. Make sure to be in the root directory `work2`\n\nFor this, just follow the optional steps 3 and 4 from the Manual Virtual Environment Setup\n\n### 2. Activate the Virtual Environment\n\n```bash\n# Windows\nvenv\\Scripts\\activate\n\n# macOS/Linux\nsource venv/bin/activate\n```\n\nIn front of the input line in the terminal it should now say `(venv)`\n\n### 3. Run `app.py`\n\n```bash\npython app.py\n```\n\nThe first execution takes more time than usual due to the initial compilation of the whole project. Once compiled, it prompts the user to provide an input. The user must decide whether to use the `hepatitis` or the `pen-based` dataset for the analysis. By simply pressing enter, the hepatitis dataset will be selected by default.\n\nNow the entire project pipeline will execute, including data preprocessing, KNN and SVM analyses, various reduction techniques, and final report generation. Progress is displayed in the console, but due to frequent calculations and multithreading, following along in real-time may be difficult. It is recommended to refer to the final reports for evaluation. The program completes once the `nemenyi test report` is generated.\n\nFor deeper insights please consider reading the report of the project.\n\n## Project Structure\n\n```\nwork2/\n├── classifiers/               # SVM and KNN classifiers\n├── csv-results/               # Performance metrics and results\n├── datasetsCBR/               # Dataset files\n├── metrics/                   # Performance metric calculations\n├── preprocessing/             # Data preprocessing scripts\n├── reduction_techniques/      # Instance reduction algorithms\n├── reporting/                 # Reporting and analysis scripts\n├── reports/                   # Generated reports\n├── venv/                      # Virtual environment\n├── app.py                     # Main application script\n├── README.md                  # This file\n├── requirements.txt           # Dependencies\n└── utils.py                   # Utility functions\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fevaveli%2Fsupervised-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fevaveli%2Fsupervised-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fevaveli%2Fsupervised-learning/lists"}