This project predicts the likelihood of sleep disorders based on various health and lifestyle factors. It leverages machine learning techniques to analyze sleep patterns and identify potential disorders.
- Data preprocessing and exploratory data analysis (EDA)
- Feature engineering and selection
- Machine learning model development and evaluation
- Model interpretability and visualization
- Python
- Pandas, NumPy
- Scikit-learn
- Matplotlib, Seaborn
- Jupyter Notebook
The dataset contains patient sleep-related information, including:
- Sleep Duration
- Snoring Frequency
- Daily Activity Levels
- Stress Levels
- Medical History
- Logistic Regression
- Random Forest Classifier
- Support Vector Machine (SVM)
- XGBoost
The models are evaluated using accuracy, precision, recall, and AUC-ROC scores. The best model provides reliable predictions for identifying potential sleep disorders.
📂 Sleep-Disorder-Prediction
👉 📂 data (Dataset & processed data)
👉 📂 notebooks (Jupyter Notebooks)
👉 📂 models (Trained models)
👉 📂 images (Code and Results Screenshots)
👉 📄 README.md (Project documentation)
Include images of code and results in the images folder. Example:
- Clone the repository:
git clone https://github.com/rohitinu6/Sleep-Disorder-Prediction.git
- Navigate to the project folder:
cd Sleep-Disorder-Prediction - Install dependencies:
pip install -r requirements.txt
- Run the Jupyter Notebook or Python scripts to train and test models.
- GitHub Repository: Sleep Disorder Prediction
- Portfolio: Rohit Dubey
- GitHub Profile: rohitinu6
- LinkedIn: Rohit Dubey
- Twitter/X: @rohitdubey003
Machine Learning Sleep Disorder Health Prediction Data Science Python EDA
This project is licensed under the MIT License.
💡 For any queries or collaboration opportunities, feel free to connect! 🚀