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readme updates

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  1. README.md +6 -10
  2. reports/figures/.gitkeep +0 -0
README.md CHANGED
@@ -23,26 +23,20 @@ Project Organization
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  β”‚Β Β  β”œβ”€β”€ processed <- The final, canonical data sets for modeling.
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  β”‚Β Β  └── raw <- The original, immutable data dump.
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  β”‚
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- β”œβ”€β”€ eval.dvc <- The end of the data pipeline - evaluates the trained model on the test dataset.
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- β”‚
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  β”œβ”€β”€ models <- Trained and serialized models, model predictions, or model summaries
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  β”‚
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  β”œβ”€β”€ notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
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  β”‚ the creator's initials, and a short `-` delimited description, e.g.
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  β”‚ `1.0-jqp-initial-data-exploration`.
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  β”‚
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- β”œβ”€β”€ process_data.dvc <- Process the raw data and prepare it for training.
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- β”œβ”€β”€ raw_data.dvc <- Keeps the raw data versioned.
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  β”‚
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  β”œβ”€β”€ references <- Data dictionaries, manuals, and all other explanatory materials.
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  β”‚
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  β”œβ”€β”€ reports <- Generated analysis as HTML, PDF, LaTeX, etc.
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- β”‚Β Β  └── figures <- Generated graphics and figures to be used in reporting
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  β”‚Β Β  └── metrics.txt <- Relevant metrics after evaluating the model.
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  β”‚Β Β  └── training_metrics.txt <- Relevant metrics from training the model.
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  β”‚
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- β”œβ”€β”€ requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
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- β”‚ generated with `pip freeze > requirements.txt`
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  β”‚
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  β”œβ”€β”€ setup.py <- makes project pip installable (pip install -e .) so src can be imported
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  β”œβ”€β”€ src <- Source code for use in this project.
@@ -50,17 +44,19 @@ Project Organization
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  β”‚ β”‚
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  β”‚Β Β  β”œβ”€β”€ data <- Scripts to download or generate data
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  β”‚Β Β  β”‚Β Β  └── make_dataset.py
 
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  β”‚ β”‚
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- β”‚Β Β  β”œβ”€β”€ models <- Scripts to train models and then use trained models to make
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- β”‚ β”‚ β”‚ predictions
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  β”‚Β Β  β”‚Β Β  β”œβ”€β”€ predict_model.py
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  β”‚Β Β  β”‚Β Β  └── train_model.py
 
 
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  β”‚ β”‚
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  β”‚Β Β  └── visualization <- Scripts to create exploratory and results oriented visualizations
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  β”‚Β Β  └── visualize.py
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  β”‚
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  β”œβ”€β”€ tox.ini <- tox file with settings for running tox; see tox.testrun.org
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- └── train.dvc <- Traing a model on the processed data.
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  --------
 
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  β”‚Β Β  β”œβ”€β”€ processed <- The final, canonical data sets for modeling.
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  β”‚Β Β  └── raw <- The original, immutable data dump.
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  β”‚
 
 
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  β”œβ”€β”€ models <- Trained and serialized models, model predictions, or model summaries
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  β”‚
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  β”œβ”€β”€ notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
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  β”‚ the creator's initials, and a short `-` delimited description, e.g.
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  β”‚ `1.0-jqp-initial-data-exploration`.
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  β”‚
 
 
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  β”‚
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  β”œβ”€β”€ references <- Data dictionaries, manuals, and all other explanatory materials.
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  β”‚
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  β”œβ”€β”€ reports <- Generated analysis as HTML, PDF, LaTeX, etc.
 
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  β”‚Β Β  └── metrics.txt <- Relevant metrics after evaluating the model.
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  β”‚Β Β  └── training_metrics.txt <- Relevant metrics from training the model.
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  β”‚
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+ β”œβ”€β”€ requirements.txt <- The requirements file for reproducing the analysis environment
 
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  β”‚
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  β”œβ”€β”€ setup.py <- makes project pip installable (pip install -e .) so src can be imported
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  β”œβ”€β”€ src <- Source code for use in this project.
 
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  β”‚ β”‚
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  β”‚Β Β  β”œβ”€β”€ data <- Scripts to download or generate data
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  β”‚Β Β  β”‚Β Β  └── make_dataset.py
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+ β”‚Β Β  β”‚Β Β  └── process_data.py
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  β”‚ β”‚
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+ β”‚Β Β  β”œβ”€β”€ models <- Scripts to train models
 
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  β”‚Β Β  β”‚Β Β  β”œβ”€β”€ predict_model.py
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  β”‚Β Β  β”‚Β Β  └── train_model.py
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+ β”‚Β Β  β”‚Β Β  └── evaluate_model.py
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+ β”‚Β Β  β”‚Β Β  └── model.py
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  β”‚ β”‚
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  β”‚Β Β  └── visualization <- Scripts to create exploratory and results oriented visualizations
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  β”‚Β Β  └── visualize.py
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  β”‚
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  β”œβ”€β”€ tox.ini <- tox file with settings for running tox; see tox.testrun.org
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+ └── data.dvc <- Traing a model on the processed data.
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  --------
reports/figures/.gitkeep DELETED
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