Python Enthusiast | Django Rest Framework | Angular | PostGre SQLA health care web app that allows different users having different groups assigned, for performing various functionalities i.e uploading hospital information, data manipulation, upload presciptions, add diagnosis etc. I mostly introduced new features for the app. Given below:1. Metrics dashboard analytics for users added, current devices assigned to patients, active hospitals etc. I fetched this for timeframes including Daily, Monthly, Weekly and Quaterly.2. Optimized the file uploading queries as it took too much time to fetch response from the backend because the file had to be parsed and should be saved into the DB. Reduced the time span for uploading the file on app.3. Implemented a change log fuctionality in which every Create, Update and Delete fuctionality that is performed is kept in a specific table with track of data what was Created, Updated or Deleted.4. Feedback feature for the app in which user can select the feedback type, his information is auto-populated in the feedback form and submit it. Submission is sent as an email to Administrators.5. Hospital logo uploading and pasting it on reports was implemented by me. I created a component in Angular to upload the log with required dimensions and image format. Validators were implemented. I uploaded the image to S3 bucket so that it is kept as private and will be fetched when user already has image saved in the bucket.Moreover, I have fixed numerous bugs in the app i.e fixing the reporting module, fixing the notification sent to users, fixed data population and fetching for a module, fixed searching filters etc. Client-succes, made app user-friendly and achieved goals as a signle team player.

A web app that does sentiment analysis on the data this is scraped from the famouse websites and after analysis it does let you know whether people are posting good, average or bad stuff about it. This app requires a query, against that query the data is scraped and Data Analytical / Machine Learning techniques are applied on that data to get the sentiments.1. Integrated User Registration Module in the whole app. A user could sign up, an activation link would be sent to him. User could login and could also reset the password if forgotten.2. Scraped data on runtime from the websites i.e Facebook, LinkedIn, CNN, BBC, etc for the team so that sentiment analysis could be applied on the data pulled.3. Cleaned and processed the whole data that was scraped fot the conveniece of the team.4. Worked with team on tha API's. Used NgRok for the posting and getting the data from the API's. Integrated API's in the frontend React.js and populated the data for better visuals.5. Coded a whole Automation Script that used the API's key tokens to scrape data. API's were restricted to pull data, could only scrape limited data in one day. My script could use 15-20 API's to fetch the data, maintained a .csv sheet to keep a record of the API's whether which could be used and which couldn't be used.

1. Built a whole project from scratch that could extract data from .pdf files. Used spacy large model to extract entities and show them to customers over frontend.
2. Designed and coded a GUI application using PyQt5 that could allow user to upload picture and get a resultant one after applying computer vision techniques on it.