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https://github.com/md-emon-hasan/ml-project-titanic-survival-prediction
🚢 Titanic survival outcomes using various classification. It includes comprehensive approach to data preprocessing, training, and evaluation.
https://github.com/md-emon-hasan/ml-project-titanic-survival-prediction
data-science deployment falsk machine-learning titanic-kaggle titanic-survival titanic-survival-prediction
Last synced: about 2 months ago
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🚢 Titanic survival outcomes using various classification. It includes comprehensive approach to data preprocessing, training, and evaluation.
- Host: GitHub
- URL: https://github.com/md-emon-hasan/ml-project-titanic-survival-prediction
- Owner: Md-Emon-Hasan
- License: mit
- Created: 2024-08-18T13:55:32.000Z (5 months ago)
- Default Branch: master
- Last Pushed: 2024-08-18T14:20:52.000Z (5 months ago)
- Last Synced: 2024-08-18T15:26:46.406Z (5 months ago)
- Topics: data-science, deployment, falsk, machine-learning, titanic-kaggle, titanic-survival, titanic-survival-prediction
- Language: Jupyter Notebook
- Homepage: https://ml-project-titanic-survival-prediction.onrender.com
- Size: 60.5 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🚢 Titanic Survival Prediction
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.
## 📋 Contents
- [Introduction](#introduction)
- [Topics Covered](#topics-covered)
- [Getting Started](#getting-started)
- [Live Demo](#live-demo)
- [Best Practices](#best-practices)
- [FAQ](#faq)
- [Troubleshooting](#troubleshooting)
- [Contributing](#contributing)
- [Additional Resources](#additional-resources)
- [Challenges Faced](#challenges-faced)
- [Lessons Learned](#lessons-learned)
- [Why I Created This Repository](#why-i-created-this-repository)
- [License](#license)
- [Contact](#contact)
---
## 📖 Introduction
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.
---
## 🔍 Topics Covered
- **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.
---
## 🚀 Getting Started
To get started with this project, follow these steps:
1. **Clone the repository:**
```bash
git clone https://github.com/Md-Emon-Hasan/ML-Project-Titanic-Survival-Prediction.git
```
2. **Navigate to the project directory:**
```bash
cd ML-Project-Titanic-Survival-Prediction
```
3. **Create a virtual environment and activate it:**
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
4. **Install the dependencies:**
```bash
pip install -r requirements.txt
```
5. **Run the application:**
```bash
python app.py
```
6. **Open your browser and visit:**
```
http://127.0.0.1:5000/
```
---
## 🎉 Live Demo
Check out the live version of the Titanic Survival Predictor app [here](https://ml-project-titanic-survival-prediction.onrender.com/).
---
## 🌟 Best Practices
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.
---
## ❓ FAQ
**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](#contributing) section for details on contributing.
**Q: Where can I learn more about machine learning?**
A: Explore [Scikit-learn Documentation](https://scikit-learn.org/stable/user_guide.html) and [Kaggle](https://www.kaggle.com/learn/overview) 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.
---
## 🛠️ Troubleshooting
Common issues and solutions:
- **Issue: Flask App Not Starting**
*Solution:* Ensure all dependencies are installed and the virtual environment is activated.
- **Issue: Model Not Loading**
*Solution:* Check the model file path and verify it's not corrupted.
- **Issue: Inaccurate Predictions**
*Solution:* Verify the input features are correctly formatted and ensure the model is properly trained.
---
## 🤝 Contributing
Contributions are welcome! Here's how you can contribute:
1. **Fork the repository.**
2. **Create a new branch:**
```bash
git checkout -b feature/new-feature
```
3. **Make your changes:**
- Add features, fix bugs, or enhance documentation.
4. **Commit your changes:**
```bash
git commit -am 'Add a new feature or update'
```
5. **Push to the branch:**
```bash
git push origin feature/new-feature
```
6. **Submit a pull request.**
---
## 📚 Additional Resources
Explore these resources for more insights into machine learning and Flask development:
- **Flask Official Documentation:** [flask.palletsprojects.com](https://flask.palletsprojects.com/)
- **Machine Learning Tutorials:** [Kaggle](https://www.kaggle.com/learn/overview)
- **Data Science Resources:** [Towards Data Science](https://towardsdatascience.com/)
---
## 💪 Challenges Faced
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.
---
## 📚 Lessons Learned
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.
---
## 🌟 Why I Created This Repository
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.
---
## 📝 License
This repository is licensed under the [MIT License](https://opensource.org/licenses/MIT). See the [LICENSE](LICENSE) file for more details.
---
## 📬 Contact
- **Email:** [[email protected]](mailto:[email protected])
- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)
- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)
- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)
---
Feel free to adjust and expand this template based on the specifics of your Titanic Survival Prediction project.