Vishwa Pardeshi's Projects
This repositories contain experiments which were designed to gain a better understanding of A/B testing and analyzing results.
The repository contains the Winning Submission for the online Hackathon at University of Washington, Seattle.
Explored models such as Logistic Regression (SMOTE), SVM, RandomForest & XGBoost to assess customersβ propensity or risk to churn for a telecom. Performed In-depth EDA & data preprocessing in Python.
This contains projects which are a part of the Deep Learning Specialization on Coursera taught by Andrew Ng.
A disaster relief web app using Flask to classify disaster related messages to alert concerned authorities. Built ELT & ML pipeline to perform multi-class text classification.
Join the GitHub Graduation Yearbook and "walk the stage" on June 5.
A recommendation engine based on user behavior and social network data, to surface content most likely to be relevant to a user on IBM Watson Studio Platform. Used collaborative filtering & matrix factorization techniques.
Contains machine learning mini-projects in Python & R
Deployed an elastic and fault-tolerant Machine Learning Price Prediction API using Kubernetes.
Tool for Portfolio Managers to track market, industry sentiment and credibility trends for S&P 500 companies
Analyzed A/B test where we moved the first gate in Cookie Cats from level 30 to level 40 to provide statistical & practical significance w.r.t player retention.
Used Apache Spark to perform analytics on music streaming app data to predict customer churn.
Contains mini-projects on NLP in Python & R.
NLP parser using NER and TDD
Covers fundamentals of OOP using Python. Includes code snippets of classes, inheritance, and polymorphism
Developed Product Basked Network using Market Basket Analysis to recommend a profitable promotion campaign for each market segment.
Welcome to the code repository for CitSci Earth
Supervised and semi-supervised text classification of quotes from Democratic Presidential Nominee for US 2020. Used Regularized Logistic Regression, XGBoost, Random Forest
Udacity Data Science Nanodegree Program