student-grade-prediction

🚀 Usage Guide

This section explains how to run the Student Grade Prediction pipeline, generate sample input data, and make predictions.

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📦 Installation

1. Clone the repository:

   git clone https://github.com/yourusername/student-grade-prediction.git
   cd student-grade-prediction
   

2. Install dependencies:

   pip install -r requirements.txt
   

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▶️ Training Models

Train and evaluate a model using the CLI:

# Train on math dataset
python src/main.py --dataset math --model random_forest

# Train on portuguese dataset
python src/main.py --dataset portuguese --model random_forest

• Metrics are saved to: results/metrics/*.json
• Trained models are saved to: results/models/*.pkl
• Logs are written to: results/logs/project.log

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🛠 Generating Sample Prediction Data

Use the helper script to create valid input files for prediction (they match the training schema exactly):

python src/generate_sample_data.py

This creates:

• data/new_data_math.csv → 5 rows from the math dataset (without G3)
• data/new_data.csv → 5 rows from the Portuguese dataset (without G3)

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🔍 Running Predictions

Run predictions on new data with a trained model:

# Predict with the math model
python src/predict.py --model results/models/random_forest_math.pkl --data data/new_data_math.csv --out results/predictions

# Predict with the Portuguese model
python src/predict.py --model results/models/random_forest_portuguese.pkl --data data/new_data.csv --out results/predictions

• The first 10 predictions are printed to the console.
• All predictions are saved to: results/predictions/predictions.csv

Example output:

First 10 predictions: [10.82 11.06 13.07 14.54 13.02]

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