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README.md
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# {{DSIP.project}}
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Inspired by Serj Smorod DS in Production lecture on project structure and best practices for data science.
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## Project Structure
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- `app/`: Helper scripts and utilities.
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- `models/`: Trained models for the current experiment.
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- `archived_experiments/`: Archived experiments and their outputs.
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- `data/`: Input datasets and preprocessed data.
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- `results/`: Outputs like predictions, charts, and analysis results.
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- `notebooks/`: Jupyter notebooks for exploration and experimentation.
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- `tests/`: Unit tests to ensure code quality.
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Core scripts include:
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- `preprocess.py`: Handles data preprocessing tasks.
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- `train.py`: A script to train machine learning models.
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- `predict.py`: Generates predictions using trained models.
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- `result.py`: Analyzes results and generates metrics/charts.
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- `tasks.py`: Automates workflows using `invoke`.
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