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feedback form data and visualization update along with a simplified savings prediction model. #265

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Description

Changes made to the app.py file to add code to store the feedback submitted in a database rather than a .csv file. Still needs enabling. Also, changes made to the visualization of the feedback form fake data, generated using mockaroo. Also added sankey chart to show relationship between two variables in feedback form but needs enabling as there is a package version error. A simplified random forest prediction model that saves a local model for prediction and visualization is built but still needs more work to generate visualizations.

Fixes # (issue)

Type of change

Please delete options that are not relevant.

  • Bug fix (non-breaking change which fixes an issue)
  • New feature (non-breaking change which adds functionality)
  • Breaking change (fix or feature that would cause existing functionality to not work as expected)
  • This change requires a documentation update

How Has This Been Tested?

On my local machine by navigating to the URL's feedback_visualizations and feedback forms.

Testing Checklist

  • Tested in latest Chrome
  • Tested in latest Safari
  • Tested in latest Firefox

Checklist

  • My code follows the style guidelines of this project
  • I have performed a self-review of my own code
  • I have commented my code in hard-to-understand areas
  • I have made corresponding changes to the documentation
  • My changes generate no new warnings
  • I have requested a review from NAMES HERE on the Pull Request

Notes for PRs

Adding fake generated data for feedback form, generating visualizations and creating a simplified random forest model for savings prediction.
saving into database is temporarily disabled, generate_sankey function is currently disabled as it's raising a package error when trying to convert the plotly sankey diagram into a png to display in the feedback_visualizations and currently working on improving simplified RF model to make some prediction visualizations.
Screenshot 2024-05-12 082526

…yers including bert model

Signed-off-by: nsriharshavardhan <[email protected]>
Signed-off-by: nsriharshavardhan <[email protected]>
The last dataset being used did not represent the data or the sentiments that are precisely required in applying the sentiment analysis yielding lower accuracy. This new BERT model implemented using pytorch rectifies this problem and increases the accuracy to 78%.

Signed-off-by: nsriharshavardhan <[email protected]>
… files, cleaned and documented

Signed-off-by: nsriharshavardhan <[email protected]>
Signed-off-by: nsriharshavardhan <[email protected]>
Signed-off-by: nsriharshavardhan <[email protected]>
This still needs more work.

Signed-off-by: nsriharshavardhan <[email protected]>
Signed-off-by: nsriharshavardhan <[email protected]>
@nsriharshavardhan nsriharshavardhan marked this pull request as ready for review May 11, 2024 22:34
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