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The program saves the inputs and scores to a `.csv` file.\n\n### **Functions:**\n- Add a new item and play five matches against others to determine its ELO rating\n- Let every existing item play a randomly selected match\n- Choose two items to decide between\n- See correlation between items' personal ELO and year of release or global score\n---\n### **Example usage:**\n```\n[1]: Add a new item\n[2]: Decide between existing items\n[3]: Choose own matchup by indexes\n[4]: Ranked mode\n[0]: Quit\n\u003e 1\nItem name:\n\u003e Get Out\nRelease year of Get Out:\n\u003e 2017\nLetterboxd rating for Get Out:\n\u003e 4.1\nPlacement matchup:\n[1]: Get Out \u003c 0.5 \u003e\n[2]: Creed \u003c 0.5 \u003e\n[0]: Tie\n\u003e \n```\netc.\n\n---\n### **Example graphs:**\n\n![ELO vs Letterboxd score](md_imgs/elo_lb.png)\n![ELO vs years](md_imgs/elo_time.png)\n---\n### **Setup**\nRequires `Python 3.x` and the following packages:\n- `pandas`\n- `plotly`\n\nInstallation: `pip install pandas plotly`\n\nThen run `main.py` to start.\n\nOpen `analysis.ipynb` and select *Run All* to generate graphs and your top 10.\n\n\n---\nChange `GENERAL_SCORER` and `GENERAL_SCORE_SCALE` in `variables.py` to select another rating system. Example:\n```\nGENERAL_SCORER = \"Rotten Tomatoes\"\nGENERAL_SCORE_SCALE = [0, 100]\n```\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjo-on%2Felo_media_ranker","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjo-on%2Felo_media_ranker","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjo-on%2Felo_media_ranker/lists"}