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