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End-to-end Machine Learning project for Titanic survival prediction using ensemble models (Logistic Regression, Random Forest, KNN, XGBoost) deployed with Streamlit.

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jmeza-data/Titanic_ML_Proyect_-

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📈 Future Improvements

  • Hyperparameter optimization (GridSearch / RandomSearch)
  • Model explainability with SHAP
  • Probability calibration
  • CI/CD integration
  • Docker deployment

👤 Author

Jhoan Sebastian Meza Garcia
Economist & Data Scientist
Machine Learning | Data Analytics | Predictive Modeling


⭐ If you find this project useful, feel free to star the repository!

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End-to-end Machine Learning project for Titanic survival prediction using ensemble models (Logistic Regression, Random Forest, KNN, XGBoost) deployed with Streamlit.

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