https://github.com/wlopezm-unal/titanic_ship-streamlit
Machine Learning model, where using titanic ship data and see if is be able to predict if a passager was salved or died. This apply use Machien learning (Random Forest, gassianNB and Logistic Regressión) . Further, using streamlit together to FastApi be able to see the predict result
https://github.com/wlopezm-unal/titanic_ship-streamlit
docker machine-learning optuna scikit-learn streamlit supervised-machine-learning
Last synced: 3 months ago
JSON representation
Machine Learning model, where using titanic ship data and see if is be able to predict if a passager was salved or died. This apply use Machien learning (Random Forest, gassianNB and Logistic Regressión) . Further, using streamlit together to FastApi be able to see the predict result
- Host: GitHub
- URL: https://github.com/wlopezm-unal/titanic_ship-streamlit
- Owner: wlopezm-unal
- License: apache-2.0
- Created: 2024-02-09T02:48:51.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-26T18:25:45.000Z (8 months ago)
- Last Synced: 2025-01-08T03:43:31.714Z (4 months ago)
- Topics: docker, machine-learning, optuna, scikit-learn, streamlit, supervised-machine-learning
- Language: Python
- Homepage:
- Size: 2.91 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Spaceship Titinic/streamlit/Docker
Machine Learning model, where using titanic ship data and see if is be able to predict if a passager was salved or died. 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
Yyou can find out everything about the project spaceship titinic like data base in the next link: https://www.kaggle.com/competitions/spaceship-titanic/data
---------------------------------------------------------------
Sofware and Tools Requeriments
1. [GitHub Account] (https://github.com)
2. [VS Code IDE] (http://code.visualstudio.com/)
3. Stremlit
4. Python, Pandas, scikit-learn
5. Docker Installation
---------------------------------------------------------------
# System Setup
1. Lauch docker compose docker compose up -d
2. Deployment streamlit : streamlit run main.py
--------------------------------------------------------------
Image of Results:
* 
* 