Project Title

Influence of Social Media and other socio-demographic factors on Study Duration Prediction Web App

Overview

This web application leverages machine learning to predict the study duration based on several personal and lifestyle factors. The app takes input such as GPA scores, social media engagement, Residence, Family Education, relationship status, and more, and provides a prediction for the study duration (ranging from 2 to 8 hours).

The goal of this project is to give students an insight into how their personal and social habits affect their study time, helping them manage their time better.

Features

• User Input: Allows users to input key factors such as GPA scores, relationship status, and social media engagement.
• Study Duration Prediction: Predicts the amount of time a student should ideally spend studying based on the input data.
• Machine Learning Model: Trained using real-world data to predict study duration effectively.
• Flask Backend: Used to build the RESTful API that handles predictions.
• Elegant UI: A responsive and user-friendly frontend for ease of interaction.

Tech Stack

• Frontend: HTML, CSS, js
• Backend: Flask (Python-based web framework)
• Machine Learning: scikit-learn, pandas, numpy
• Deployment: Render (or any cloud hosting platform like Heroku or AWS)

Installation and Setup

To run this project locally:

Prerequisites

• Python 3.x
• pip (Python package manager)

Steps:

1. Clone the repository:

   git clone https://github.com/yourusername/Study-Duration-Prediction.git
   cd Study-Duration-Prediction
   

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Run the Flask app:

   python app.py
   

Model Details

The model is trained to predict the study duration based on the following input features:

• SSC GPA: Secondary school GPA (between 1.00 to 5.00)
• HSC GPA: Higher secondary school GPA (between 1.00 to 5.00)
• Social Media Engagement: Hours spent on social media (between 1 to 5 hours)
• Relationship: Whether the student is in a relationship (0 = No, 1 = Yes)
• Bad Habits: Whether the student has any bad habits (0 = No, 1 = Yes)
• External Factors: Influence of external factors (0 = No, 1 = Yes)
• Residence with Family: Whether the student lives with their family (0 = No, 1 = Yes)
• Family Education: Whether the family is educated (0 = No, 1 = Yes)
• Politics: Whether the student is involved in politics (0 = No, 1 = Yes)

Screenshots

Example Input

• SSC GPA: 5.00
• HSC GPA: 5.00
• Residence with Family: 0
• Family Education: 1
• Social Media Engagement: 3
• Relationship: 1
• Bad Habits: 0
• Politics: 0
• External Factors: 1

Example Output

• Predicted Study Duration: 7.86 hours

Usage

1/ Open the web app in your browser (http://127.0.0.1:5000/).

2/ Fill in the form with the required data points.

3/ Click Predict to receive the study duration prediction.

4/ The result will display below the form with an estimated study duration.

Deployment

You can deploy this app on platforms such as Render or Heroku. Follow their respective documentation to deploy the Flask app online.

Future Enhancements

• Model Improvement: Integrate more features and improve the model's accuracy using advanced algorithms.
• User Accounts: Allow users to sign in and track their study patterns over time.
• Visualization: Include visual graphs showing trends and correlations between social media use and study duration.

License

This project is licensed under the MIT License.

Acknowledgements

• Flask Documentation: https://flask.palletsprojects.com/
• scikit-learn Documentation: https://scikit-learn.org/

Authors

• @myself-aas

Support

For support and queries, email me.