mlproject

A simple machine learning project that trains and evaluates models to predict student performance using the provided Students Performance dataset.

Dataset

• File: data/StudentsPerformance.csv (also available in notebook/data/)
• Short description: student demographics and exam scores (math, reading, writing).

Quick start

1. Install dependencies:

   pip install -r requirements.txt

2. Reproduce experiments or train models:

   • Open and run the notebooks in notebook/ (recommended for step-by-step work), or
   • Use the training pipeline in src/ (see src/components/model_trainer.py and pipeline modules).

3. Use Docker (optional): common commands are in the Makefile (e.g., make build, make up).

Project structure

• data/ — dataset files
• notebook/ — EDA and model training notebooks
• src/ — code for ingestion, transformation, and model training
• artifacts/ — generated models and outputs
• app.py — small app / entrypoint

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Keep it simple and reproducible. For more details, open the notebooks or check src/.