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Machine Learning Project: Predicting Student Performance

This machine learning project predicts student math scores based on various factors. The project includes data ingestion, transformation, model training, and prediction. Additionally, there is a web application for user interaction.

Project Structure

mlproject (Parent folder for the project)

  • artifacts: Folder containing saved models, preprocessors, and other relevant artifacts.

  • notebook: Folder for Jupyter notebooks.

  • src: Source code folder.

    • components: Subfolder containing core components of the project.

      • data_ingestion.py: Handles loading and splitting the dataset.
      • data_transformation.py: Applies preprocessing to the input data.
      • model_training.py: Manages the training and evaluation of machine learning models.

    • pipeline: Subfolder containing the prediction pipeline.

      • predict_pipeline.py: Loads the trained model and preprocessor for making predictions.

    • utils.py: Contains utility functions used across the project, including saving and loading objects and model evaluation.

  • templates: HTML templates for the web application.

  • app.py: Main Flask application for user interaction.

  • README.md: Project overview, usage instructions, and information about the structure.

  • requirements.txt: List of dependencies for the project.

  • setup.py: Setup script for packaging the project.

How to Use

  1. Clone the repository:

    git clone https://github.com/EkeminiUmanah/mlproject.git
cd mlproject

  2. Install dependencies:

    pip install -r requirements.txt

  3. Run the Flask application:

    python app.py