Lebenserwartungs-Rechner-Projekt ⏳💓

This project is a Flask-based web application designed to predict an individual's remaining life expectancy using different machine learning models. It utilizes a feedforward neural network and XGBoost for predictions.

Features ✨

• Web Interface: Built with Flask, the application provides an easy-to-use interface for users to input their data and receive life expectancy predictions.
• Advanced Machine Learning Models: Incorporates a feedforward neural network and XGBoost model to predict life expectancy.
• Data Analysis and Model Training: Extensive Exploratory Data Analysis (EDA) cleansing, analysis, Featrue Selection & Engneering, model training and XAI, performed in Jupyter Notebooks. Also including Random Forest and Linear Regression models.

Technical Stack 🔬

• Flask: For creating the web application.
• Pandas & NumPy: For data manipulation and analysis.
• PyTorch: For building and training the neural network model.
• XGBoost: For building the gradient boosting model.
• Joblib: For model serialization and deserialization.
• Scikit-learn: For various machine learning utilities.
• Seaborn & Matplotlib: For data visualization.
• Category Encoders: For encoding categorical variables.
• SHAP: For model interpretability.

Installation ⌨️

To set up this project, you need to have Python installed on your system. After cloning the repository, install the required dependencies:

pip install -r requirements.txt

Usage 🔧

To run the Flask application, execute:

python app.py

Navigate to http://localhost:5000 in your web browser to access the application.

File Structure 📁

• app.py: The Flask application.
• final.ipynb: Jupyter notebook containing EDA, transformation, cleansing, analysis, Featrue Selection & Engneering, model traing and evaluation aswell as XAI.

License ⚖️

This project is licensed under the MIT License.