🦴 Osteoporosis Risk Predictor

This project predicts the risk of osteoporosis using health and lifestyle factors.
It includes: - A Jupyter Notebook (Osteoporosis_Risk_Predictor.ipynb) for data exploration, feature engineering, and model training. - A Streamlit web app (Osteoporosis_Risk_app.py) that lets users interactively enter patient information and get predictions. - Saved models (.pkl files) for deployment.

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📊 Dataset

The dataset includes health and lifestyle attributes such as: - Age - Hormonal Changes - Body Weight - Calcium Intake - Vitamin D Intake - Physical Activity - Medical Conditions - Medications - Prior Fractures

The target variable is Osteoporosis (Yes/No).

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🚀 Workflow

1. Data Preprocessing: Handle missing values, encode categorical features, and scale numerical ones.
2. Exploratory Data Analysis (EDA): Visualize distributions, correlations, and feature relationships.
3. Model Training: Compare models (Logistic Regression, Decision Tree, Random Forest, SVM).
4. Evaluation: Accuracy, Precision, Recall, F1-score, Confusion Matrices.
5. Deployment: Save the trained Decision Tree model and use it in a Streamlit app.

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💻 Usage

1. Clone this repository

git clone https://github.com/your-username/osteoporosis-risk-predictor.git
cd osteoporosis-risk-predictor

2. Install dependencies

pip install -r requirements.txt

3. Run the notebook (optional, for training & analysis)

jupyter notebook Osteoporosis_Risk_Predictor.ipynb

4. Run the Streamlit app

streamlit run Osteoporosis_Risk_app.py

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🛠 Tech Stack

• Python 3.8+
• Pandas, NumPy -- data processing
• Matplotlib, Seaborn -- visualization
• Scikit-learn -- machine learning
• Streamlit -- web app interface

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📈 Results

• The Decision Tree Classifier was selected as the best-performing model.
• Important predictors: Age, Hormonal Changes, Nutrition, Medications, Medical Conditions.

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🔮 Next Steps

• Add cross-validation and ROC/AUC metrics.
• Expand the Streamlit app with probability outputs (not just binary).
• Deploy the app online (e.g., Streamlit Cloud, Heroku).

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📂 Project Structure

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├── data/                      # dataset (if public)
├── models/                    # saved .pkl models
├── notebook/                  # Jupyter Notebook (EDA + training)
├── Osteoporosis_Risk_app.py           # Streamlit App
├── requirements.txt
└── README.md

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📜 License

This project is licensed under the MIT License.