Sleep-Health-Project

Sleep Quality Prediction with Machine Learning

Overview

Sleep quality is a crucial aspect of a healthy lifestyle, yet many individuals struggle with sleep disorders. This project aims to leverage artificial intelligence and machine learning models to predict the likelihood of a person having a sleep disorder based on various factors.

Dataset

The dataset used for training and testing the model includes the following features:

• Person ID: An identifier for each individual.
• Gender: The gender of the person (Male/Female).
• Age: The age of the person in years.
• Occupation: The occupation or profession of the person.
• Sleep Duration (hours): The number of hours the person sleeps per day.
• Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.
• Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.
• Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.
• BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).
• Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.
• Heart Rate (bpm): The resting heart rate of the person in beats per minute.
• Daily Steps: The number of steps the person takes per day.
• Sleep Disorder: The presence or absence of a sleep disorder in the person (None, Insomnia, Sleep Apnea).

Machine Learning Model

We have employed a machine learning model that has been trained on a set of training data and tested on a separate set of test data. The model predicts whether a person has a sleep disorder based on the input features.

Usage

To use the model for predicting sleep disorders, follow these steps:

1. Clone the Repository:

   git clone https://github.com/yourusername/sleep-quality-prediction.git
   cd sleep-quality-prediction

To replicate or explore this project:

Clone this repository. Ensure the required libraries are installed using the provided requirements.txt file. Run the Python script or Jupyter Notebook to execute the code. Contributions Contributions to enhance the visualization techniques, optimize code, or add more comprehensive analyses are welcome. Please fork the repository, make your changes, and create a pull request.

Contact Information For any queries or suggestions regarding this project, feel free to reach out:

✉️ Email: [email protected] 🔗 LinkedIn: workwithabhinav Happy visualizing! 📊✨