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This apply use Machien learning (Random Forest, gaussianNB and Logistic Regressión; using libreris like scikit-learn and optuna) . Further, using streamlit together to FastApi be able to see the predict result\n\nYyou can find out everything about the project spaceship titinic like data base in the next link: https://www.kaggle.com/competitions/spaceship-titanic/data\n\n---------------------------------------------------------------\nSofware and Tools Requeriments\n1. [GitHub Account] (https://github.com)\n2. [VS Code IDE] (http://code.visualstudio.com/)\n3. Stremlit\n4. Python, Pandas, scikit-learn\n5. Docker Installation\n---------------------------------------------------------------\n# System Setup\n1. Lauch docker compose docker compose up -d\n2. Deployment streamlit : streamlit run main.py\n--------------------------------------------------------------\nImage of Results:\n\n* ![image](https://github.com/wlopezm-unal/Titanic_ship-streamlit/assets/68913739/0ed4e102-41a7-4f6d-a716-eedde52900fa)\n\n* ![image](https://github.com/wlopezm-unal/Titanic_ship-streamlit/assets/68913739/8f3753d6-8267-4e1a-9f87-8b0ff5108eed)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwlopezm-unal%2Ftitanic_ship-streamlit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwlopezm-unal%2Ftitanic_ship-streamlit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwlopezm-unal%2Ftitanic_ship-streamlit/lists"}