Stress_Detection_Prediction with PSS CODE

(Research Project) "A "Streamlit-based application for predicting Perceived Stress Scale (PSS) scores using machine learning models."

PSS Score Prediction

This project is a machine learning application designed to predict Perceived Stress Scale (PSS) scores using various behavioral, psychological, and lifestyle features.

Overview

• Predict your stress levels based on factors like personality, sleep, and activity data.
• Streamlit-powered user interface for easy input and real-time predictions.

Features

• Machine Learning Model: Trained on a dataset including personality traits, sleep metrics, and mobility data to accurately predict PSS scores.
• Interactive UI: User-friendly interface built with Streamlit for effortless data entry and stress prediction.
• Customizable Predictions: Input key features like Openness, Conscientiousness, sleep duration, and more for personalized predictions.
• API Ready: Integrates seamlessly with larger systems or functions as a personal stress tracking application.

Dataset

The project utilizes a dataset encompassing the following features:

• Personality Traits: Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism
• Sleep Metrics: Sleep time, Wake time, Sleep duration
• Other Features: Skin conductance, Mobility radius, and others

Requirements

• Python 3.7+
• Libraries: pandas, numpy, scikit-learn, joblib, streamlit

Installation

1. Clone the repository:

   git clone [https://github.com/yourusername/pss-score-prediction.git](https://github.com/yourusername/pss-score-prediction.git)
   cd pss-score-prediction

2. Install required dependencies:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run app.py

Usage

1. Open the app in your browser (URL: http://localhost:8501).
2. Enter feature values using the provided sliders or input boxes.
3. Click the "Predict" button to see your predicted PSS score.

Model Information

The machine learning model was chosen after evaluating multiple algorithms. The best-performing model was trained and saved using joblib. It predicts PSS scores based on user-provided data.

Demo

A demo of the application is available. (Link to be added if available)

Author

Created by Ranjan.

License

This project is licensed under the MIT License.