Social Media Prediction with Machine Learning

Overview

This project aims to develop a machine learning model for predicting social media impact over students life. The system leverages a combination of Python, R programming, Flask, and custom datasets collected via Google Forms from students.

Technologies Used

• Python: For data processing, machine learning model development, and prediction.
• R Programming: For data processing, machine learning model development, and prediction.
• Flask: To create a web application that serves the machine learning model and provides a user interface.
• Google Forms: To collect custom datasets from student responses.

Project Structure

social-media-prediction/
│
├── data/
│   ├── raw/                # Raw data collected from Google Forms
│   └── processed/          # Processed data ready for analysis
│
├── notebooks/              # Jupyter notebooks for exploratory data analysis and model building
│
├── src/
│   ├── __init__.py         # Initialization file for the src module
│   ├── data_preprocessing.py  # Scripts for cleaning and preparing data
│   ├── feature_engineering.py  # Scripts for feature extraction and transformation
│   ├── model_training.py    # Scripts for training machine learning models
│   └── model_evaluation.py  # Scripts for evaluating model performance
│
├── app/
│   ├── __init__.py         # Initialization file for the Flask app
│   ├── routes.py           # Define routes for the Flask web application
│   ├── static/             # Static files (CSS, JavaScript, images)
│   └── templates/          # HTML templates for the web application
│
├── requirements.txt        # Python dependencies
├── R_requirements.txt      # R dependencies
├── README.md               # This README file
└── .gitignore              # Git ignore file

Setup

Python Environment

1. Clone the repository:

   git clone https://github.com/yourusername/social-media-prediction.git
   cd social-media-prediction

2. Create a virtual environment and activate it:

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

3. Install Python dependencies:

   pip install -r requirements.txt

R Environment

1. Install R and RStudio if not already installed.

2. Install R dependencies:

   install.packages(c('tidyverse', 'caret', 'shiny'))

Usage

Data Collection

1. Google Forms: The data is collected using Google Forms, with responses saved in CSV format. Place the CSV files in the data/raw/ directory.

Data Processing

1. Preprocess Data: Run the data preprocessing script to clean and prepare data.

   python src/data_preprocessing.py

2. Feature Engineering: Extract and transform features as needed.

   python src/feature_engineering.py

Model Training and Evaluation

1. Train Models: Train your machine learning models using the training script.

   python src/model_training.py

2. Evaluate Models: Evaluate the performance of your models.

   python src/model_evaluation.py

Web Application

1. Run Flask App: Start the Flask web application.

   python app/__init__.py

2. Access the Application: Open your web browser and go to http://127.0.0.1:5000/ to interact with the application.

Contributing

Feel free to submit issues and pull requests. For detailed instructions on how to contribute, please refer to the CONTRIBUTING.md file.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or feedback, please contact your.email@example.com.

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Thank you for checking out the Social Media Prediction project!