This project predicts the survival status of Titanic passengers using machine learning. The repository includes code for data preprocessing, exploratory data analysis (EDA), feature engineering, and model training.
- Dataset: Titanic - Machine Learning from Disaster
- Programming Language: Python
- Libraries Used: Scikit-learn, Seaborn, Matplotlib, Pandas, Numpy, Tensorflow and Tflearn.
- 📂 Dataset/: Dataset files.
- 📓 notebook/: Jupyter notebooks.
- 📝 LICENSE: Project license.
- 📄 README.md: Project README file.
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⚙️ Hyperparameter Tuning: Experiment with hyperparameter tuning to optimize the performance of the machine learning model. Adjust parameters such as learning rates, regularization, and model-specific parameters to achieve better results.
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🔄 Ensemble Methods: Explore ensemble methods to enhance the predictive power of the model. Consider techniques like Random Forests, Gradient Boosting, or stacking multiple models to improve overall accuracy.
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🚀 Model Deployment: If applicable, consider deploying the trained model for practical use. This step involves integrating the model into a real-world environment where it can make predictions on new data.
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🤝 Contributions: Contributions and suggestions are welcome. Fork the repository, create a new branch, and submit a pull request. Feel free to open issues for bug reports or feature requests. Together, we can improve and enhance the project.
- The dataset used in this project is sourced from Kaggle.
- Special thanks to the open-source community for their valuable contributions.