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+# Stress Prediction / Human Stress Prediction
+
+This project aims to predict human stress levels based on various input features using machine learning techniques. It leverages data from various sources, including physiological, behavioral, and environmental factors, to predict stress levels and provide actionable insights for stress management.
+
+## Table of Contents
+- [Overview](#overview)
+- [Technologies Used](#technologies-used)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Contributing](#contributing)
+- [License](#license)
+- [Acknowledgements](#acknowledgements)
+
+## Overview
+Stress is a natural response of the human body to various stimuli, but chronic stress can have negative impacts on mental and physical health. The goal of this project is to predict stress levels based on various features and data points, helping individuals and organizations take proactive measures to manage stress.
+
+![image](https://github.com/user-attachments/assets/aaf2fb73-cb6b-4aa5-8a46-93742a8c6fec)
+
+
+This project uses machine learning algorithms to analyze data and predict whether an individual is stressed, mildly stressed, or relaxed based on factors such as:
+- Heart rate
+- Sleep patterns
+- Physical activity levels
+- Environmental factors (noise, temperature, etc.)
+- Behavioral indicators (social interactions, work environment)
+
+![image](https://github.com/user-attachments/assets/e9a1efce-dad5-4f6a-b53f-0f7425ba03a0)
+
+
+## Technologies Used
+- **Python** – Primary programming language
+- **Scikit-learn** – Machine learning library for building models
+- **TensorFlow/Keras** (Optional) – For deep learning models
+- **Pandas** – Data manipulation and analysis
+- **NumPy** – Numerical computations
+- **Matplotlib / Seaborn** – Data visualization
+- **Flask/Django** (Optional) – For creating a web app interface
+
+## Installation
+To get started, clone the repository and install the required dependencies.
+
+### Step 1: Clone the repository
+```bash
+git clone https://github.com/Pratik-Tech-Wizard/stress-prediction.git
+cd stress-prediction
+```
+### Step 2: Install dependencies
+Install all necessary Python dependencies by running:
+```bash
+pip install -r requirements.txt
+```
+## Usage
+
+After setting up the project, you can run the model and input features to predict stress levels.
+
+### Step 1: Train a Model
+Use the training data (if available) to train the model:
+
+```bash
+python train_model.py
+```
+This will train the machine learning model using the provided data and save the trained model for future predictions.
+
+### Step 2: Run Predictions
+Once the model is trained, you can use it to predict stress levels for new data:
+
+```bash
+python predict_stress.py --input_data "path_to_input_file"
+```
+Make sure to replace "path_to_input_file" with the actual path to your input data file (e.g., CSV, JSON). This file should contain the relevant features (e.g., heart rate, sleep data, etc.) for the prediction.
+
+### Step 3: Web Interface (Optional)
+If you have built a web interface using Flask or Django, you can run the server to make predictions via a web app:
+
+```bash
+python app.py
+```
+This will start the server, and you can access the web interface at http://localhost:5000 (or any other configured port) in your browser.
+
+## Contributing
+
+We welcome contributions to improve this project! Here's how you can get involved:
+
+1. Fork the repository to create your own copy of the project.
+2. Clone your forked repo to your local machine:
+```bash
+git clone https://github.com/your-username/stress-prediction.git
+```
+3. Create a new branch to work on your feature or bug fix:
+```bash
+git checkout -b feature-name
+```
+4. Make your changes to the code.
+Commit your changes:
+```bash
+git commit -m "Added new feature"
+```
+5. Push to your forked repo:
+```bash
+git push origin feature-name
+```
+7. Open a Pull Request to the main repository.
+We will review your changes and merge them if they meet the project’s standards.
+
+
+### Key Highlights:
+- **Usage Section**:
+  - Clearly breaks down the steps for training the model, running predictions, and optionally setting up a web interface.
+  - Each step includes formatted bash commands to be copied and run easily by the user.
+  
+- **Contributing Section**:
+  - A structured guide on how to contribute, including all necessary Git commands for forking, cloning, branching, committing, and pushing changes.
+  - Simple, easy-to-follow instructions for potential contributors.
+
+This should provide clear guidance for setting up and contributing to the project. You can copy this directly into your `README.md` file. Let me know if you'd like further modifications!
+
+### License
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+### Acknowledgements
+- **Dataset**: This project uses the [Human Stress Dataset](https://link-to-dataset.com) from [source]. Alternatively, if you have custom data:
+  - The dataset for this project was custom collected, based on surveys and sensor data such as heart rate, sleep patterns, and activity levels. The dataset includes multiple features that are relevant to stress prediction.
+  - ![image](https://github.com/user-attachments/assets/c3952be7-5668-414e-9865-746e3b40d35a)
+
+- **Libraries**: The project uses various Python libraries, including [Scikit-learn](https://scikit-learn.org/), [TensorFlow](https://www.tensorflow.org/), and others.
+- **Inspiration**: Special thanks to [mention any inspiration or related work that helped shape your project].