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Disha Papneja

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  • 🔭 I’m a Software Developer. I deeply believes in the power of data driven software systems and how it has the potential to add immense value for a larger good and how it can completely transform a business. I try to extract value from messy data and develop softwares to drive automation. In addition to computing and AI, I'm interested in philosophy, sustainable energy, neuroscience and genetics.
  • 🤔 I’m currently open to new opportunities. I have worked for AWS, IBM, Axtria in the past.
  • 💬 Ask me about Cloud Computing, AWS, Python, Node, Typescript
  • 🌱 I’m currently learning **GCP, Deep Learning & Machine Learning Concepts **
  • 👯 I’m looking to collaborate on Data Science, Deep Learning, Machine Learning, Computer Vision, AI !. Hit me up if you have any projects/ opportunities related to these.
  • 📫 How to reach me : | Mail | Book a meeting with me
  • 😄 Pronouns: She/ Her
  • ⚡ Fun fact: I'm an avid reader of non fiction (biographies and business mostly), I love hiking and playing chess. I've started learning Ukelele. I also love to try weird food combos to make a new dish

Disha Papneja's Projects

data-mining-on-newsgroup-data icon data-mining-on-newsgroup-data

Designed a Machine Learning model which takes newsgroup dataset and performs binary classification to predict if a given document has Atheistic or Christian sentiment. Used LIME library and PySpark. Performed feature selection to improve classifier’s performance.

dining-conceirge icon dining-conceirge

A serverless, microservice-driven web application. A Dining Concierge chatbot, that sends you restaurant suggestions given a set of preferences that you provide the chatbot with through conversation.

introml icon introml

Python tutorials for introduction to machine learning

numpy icon numpy

The fundamental package for scientific computing with Python.

nyc-open-dataset-analysis icon nyc-open-dataset-analysis

Identified data types for each distinct column value on 1900 data sets. For each column, summarized semantic types present in the column, using Fuzzy Logic, Levenshtein distance. Identified & derived inference the 3 most frequent 311 complaint types by borough.

nyc-parking-violations icon nyc-parking-violations

This is an analysis on NYC Parking Violations dataset using PySpark SparkSQL and Map Reduce to find some useful insights.

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