Social Media Addiction Impact Predictor

This project is a sample PHP application that demonstrates how to use the Web4/LS-W4-Mini-RF_Addiction_Impact machine learning model to predict whether social media use affects a student's academic performance.

Owner & License

• Owner: Linkspreed UG
• Domain: Web4
• AI Model: The model was developed with Web4 AI.

As per the license, Linkspreed UG must be credited as the owner when using this project or its components.

Description

The application consists of a PHP frontend and a Python backend. The PHP frontend provides a user interface to input data, which is then sent to the Python backend for prediction. The Python backend serves the LS-W4-Mini-RF_Addiction_Impact model, which is a Random Forest Classifier trained on the "Social Media Addiction vs. Relationships" dataset from Kaggle.

Architecture

• Frontend: A PHP-based web interface (public/index.php) that allows users to input their data and view the prediction.
• Backend: A Python Flask-based API (backend/app.py) that loads the scikit-learn model and provides a prediction endpoint.

Model Details

• Model Type: scikit-learn RandomForestClassifier
• Model File: LS-W4-Mini-RF_Addiction_Impact.joblib

Model Input Features

The model expects the following features:

• Gender: (e.g., 'Female', 'Male')
• Academic_Level: (e.g., 'Undergraduate', 'Graduate')
• Most_Used_Platform: (e.g., 'Instagram', 'Facebook', 'Twitter')
• Relationship_Status: (e.g., 'Single', 'In a relationship')
• Age: (e.g., 20)
• Avg_Daily_Usage_Hours: (e.g., 5.0)
• Sleep_Hours_Per_Night: (e.g., 6)
• Mental_Health_Score: (e.g., 7)
• Addicted_Score: (e.g., 8)
• Conflicts_Over_Social_Media: (e.g., 0)

Prediction Output

• 1: Social media use is predicted to have an impact on academic performance.
• 0: Social media use is not predicted to have an impact on academic performance.

Getting Started

Prerequisites

• PHP installed
• Python 3 installed
• Composer (for PHP dependencies, if any)
• pip (for Python dependencies)

Installation

1. Clone the repository:

   git clone <repository-url>
   cd <repository-name>

2. Install Python dependencies:

   pip install -r backend/requirements.txt

   Note: You need to have the LS-W4-Mini-RF_Addiction_Impact.joblib file in the backend directory.

Running the Application

1. Start the Python backend server:

   python backend/app.py

   The backend will be running on http://127.0.0.1:5000.

2. Start the PHP built-in web server:

   php -S localhost:8000 -t public

3. Access the application: Open your web browser and go to http://localhost:8000.

Limitations and Ethical Considerations

• Not a Diagnostic Tool: This model should be used as a statistical tool for trend analysis and should not be used for clinical or psychological diagnosis of addiction. The data is based on self-reported survey responses.
• Generalizability: The model was trained on a specific sample of students and may not generalize well to other populations, age groups, or time periods.
• Data Bias: The model's predictions reflect the biases present in the original dataset. The results should be interpreted with caution.