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Visual work. Added notebook and .py files #39

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Oct 16, 2020
Merged

Visual work. Added notebook and .py files #39

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a510ff6
Visual work. Added notebook and .py files
Oct 16, 2020
35df7f3
PEP8 compliance edits per Lori's comments
Oct 16, 2020
a49dc81
Fixed typo
Oct 16, 2020
ae9de35
Merge branch 'main' into feature/visualization
Jwilson1172 Oct 16, 2020
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3 changes: 2 additions & 1 deletion notebooks/README.md
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- `count_spelling_errors`: This notebook contains exploration of various spell check libraries to explore whether spell check could correct transcription errors, act as a metric for student writing, and/or increase the reliability of other metrics. For the time being, we did not see enough improvement and consistency to implement this feature.
- `transcribed_stories`: This notebook connects to the Google Cloud Vision API and transcribes the given 167 stories. Produces the `transcribed_stories.csv` which includes the Submission ID and the Transcribed Text. The `transcribe` method is used to create `transcription.py` which is used in the app.
- `transcription_confidence`: This notebook explores Google Cloud Vision API's method to return confidence levels of its transcription. Produces the `error_confidence.csv` which includes story_id, error (calculated between the api transcription and provided human transcription) and confidence for each submission. The `image_confidence` method is modified to create the `confidence_flag.py` which is used in the app.
- `clustering`: This notebook explores three different clustering methods to create groupings of users for the gamification portion of Story Squad. The currently implemented version creates groups based on the ranking of the squad scores. The other methods explored were `KMeans Clustering` and `Nearest Neighbors`. These have not been implemented in our appplication due to time constraints.
- `score_visual`: This notebook explores different visualizations to display on the parent's dashboard. Several versions were mocked up and presented to the stakeholder. `histogram.py` and `line_graph.py` are the resulting final visuals per the feedback provided by the stakeholders. Each of these `.py` files are implemented in our application at the visualization endpoint.
- `clustering`: This notebook explores three different clustering methods to create groupings of users for the gamification portion of Story Squad. The currently implemented version creates groups based on the ranking of the squad scores. The other methods explored were `KMeans Clustering` and `Nearest Neighbors`. These have not been implemented in our appplication due to time constraints.
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