Welcome to the Titanic Survival Prediction repository! This project utilizes machine learning techniques to predict survival outcomes of passengers on the Titanic based on various features such as age, class, and other attributes.
- Introduction
- Topics Covered
- Getting Started
- Live Demo
- Best Practices
- FAQ
- Troubleshooting
- Contributing
- Additional Resources
- Challenges Faced
- Lessons Learned
- Why I Created This Repository
- License
- Contact
This repository features a machine learning project focused on predicting Titanic survival outcomes. The project involves data preprocessing, model training, and evaluation to provide predictions based on various passenger features.
- Machine Learning Models: Applying classification models to predict survival chances.
- Data Preprocessing: Techniques for preparing Titanic data for modeling.
- Feature Engineering: Creating and selecting features to enhance model performance.
- Model Evaluation: Assessing model performance using metrics like accuracy and F1 score.
- Deployment: Integrating the model with Flask for a web-based interface.
To get started with this project, follow these steps:
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Clone the repository:
git clone https://github.com/Md-Emon-Hasan/ML-Project-Titanic-Survival-Prediction.git
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Navigate to the project directory:
cd ML-Project-Titanic-Survival-Prediction -
Create a virtual environment and activate it:
python -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
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Install the dependencies:
pip install -r requirements.txt
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Run the application:
python app.py
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Open your browser and visit:
http://127.0.0.1:5000/
Check out the live version of the Titanic Survival Predictor app here.
Recommendations for maintaining and improving this project:
- Model Updating: Regularly update the model with new data for accurate predictions.
- Error Handling: Implement comprehensive error handling for user inputs and system issues.
- Security: Ensure secure deployment with HTTPS and proper input validation.
- Documentation: Keep documentation current to support future enhancements and user understanding.
Q: What is the purpose of this project? A: This project predicts survival chances of Titanic passengers using machine learning, providing insights into passenger survival based on features.
Q: How can I contribute to this repository? A: Refer to the Contributing section for details on contributing.
Q: Where can I learn more about machine learning? A: Explore Scikit-learn Documentation and Kaggle for additional learning resources.
Q: Can I deploy this app on cloud platforms? A: Yes, you can deploy the Flask app on cloud platforms like Heroku, Render, or AWS.
Common issues and solutions:
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Issue: Flask App Not Starting Solution: Ensure all dependencies are installed and the virtual environment is activated.
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Issue: Model Not Loading Solution: Check the model file path and verify it's not corrupted.
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Issue: Inaccurate Predictions Solution: Verify the input features are correctly formatted and ensure the model is properly trained.
Contributions are welcome! Here's how you can contribute:
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Fork the repository.
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Create a new branch:
git checkout -b feature/new-feature
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Make your changes:
- Add features, fix bugs, or enhance documentation.
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Commit your changes:
git commit -am 'Add a new feature or update' -
Push to the branch:
git push origin feature/new-feature
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Submit a pull request.
Explore these resources for more insights into machine learning and Flask development:
- Flask Official Documentation: flask.palletsprojects.com
- Machine Learning Tutorials: Kaggle
- Data Science Resources: Towards Data Science
Some challenges during development:
- Handling diverse Titanic data and feature engineering.
- Ensuring accurate survival predictions and model evaluation.
- Deploying the application and managing dependencies effectively.
Key takeaways from this project:
- Practical application of machine learning for predicting survival chances.
- Importance of thorough data preprocessing and feature selection.
- Considerations for deploying and maintaining web applications.
This repository was created to demonstrate the application of machine learning for predicting Titanic survival outcomes, showcasing the entire process from data preparation to deployment.
This repository is licensed under the MIT License. See the LICENSE file for more details.
- Email: iconicemon01@gmail.com
- WhatsApp: +8801834363533
- GitHub: Md-Emon-Hasan
- LinkedIn: Md Emon Hasan
- Facebook: Md Emon Hasan
Feel free to adjust and expand this template based on the specifics of your Titanic Survival Prediction project.