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andwati
ed2ff5b359
- Created `streamlit_app.py` for a user-friendly dashboard to predict student outcomes using a Random Forest model. - Added `requirements.txt` to specify necessary Python packages for the application. - Implemented `train_and_save_model.py` to train the Random Forest model and save it along with preprocessing information. - Included detailed user input forms for various student characteristics and academic performance metrics. - Added custom styling for better UI/UX in the Streamlit app. - Integrated prediction results display with probability metrics and model performance insig
43 lines
413 B
Plaintext
43 lines
413 B
Plaintext
# Data files
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/data/
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*.csv
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# Jupyter Notebook checkpoints
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.ipynb_checkpoints/
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# Python cache
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__pycache__/
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*.pyc
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# Environment files
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.env
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.venv/
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venv/
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env/
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*.env
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# VSCode settings
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.vscode/
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# MacOS system files
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.DS_Store
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# Logs and outputs
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*.log
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*.out
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# Model artifacts
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*.pkl
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*.joblib
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*.h5
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# Reports and HTML exports
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*.html
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*.pdf
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# Temporary files
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*.tmp
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*.bak
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# Copilot instructions
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copilot-instructions.md |