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Portfolio of Ms. Vishva Shah

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About Me

Vishva is currently a Machine Learning Associate at PwC - Forensic Technology. Accomplished ML Engineer with a stellar track record in crafting cutting-edge frameworks tailored for large-scale institutional needs. With over 3 years of expertise, adept at architecting end-to-end ML pipelines and deploying state-of-the-art statistical models for robust fraud detection. Proficient in harnessing the power of SQL and Python to wrangle vast datasets, delivering actionable insights that drive strategic decision-making.

Education Timeline

2019 - 2021

MS in Business Analytics - Major: Data Science

GPA : 3.87/4.0; Major GPA: 4.0/4.0
University of Illinois at Chicago
Machine Learning:
Data mining
Advanced Predictive Modelling - Deep Learning
Machine Learning Deployment
Programming:
Programming for Data Sceince
Database:
Advanced Business Database Technology
Statistics:
Advanced Business Analytics Statistics
Business forecasting using Time Series
Multivariate Analysis
Analytics:
Analytics for Big Data
Social Media and Network Analytics
Business Data Visualizations

2018 - 2020

Master of Commerce and Economics

CGPA: 5.0/7.0
University of Mumbai
Courses:
Strategic Management,
Economics of Global Trade and Finance,
Accountancy Paper-I, Advanced Financial Accounting Paper-II, Advanced Cost Accounting.

2014 - 2018

Associate's Degree, Actuarial Science

9 Papers cleared
Society of Actuaries (USA)
Exams cleared
VEE* Mathematical Statistics
VEE* Economics
VEE* Accounting and Finance
Exam P–Probability
Exam FM–Financial Mathematics
Exam IFM–Investment and Financial Markets
Exam LTAM–Long-Term Actuarial Mathematics
Exam STAM–Short-Term Actuarial Mathematics

2013 - 2016

Bachelor of Commerce and Economics

CGPA: 5.7/7.0
University of Mumbai
Narsee Monjee College of Commerce and Economics
Courses:
Business Mathematics and Statistics,
Business Laws,
Business Organization and Management,
E-Commerce,
Business Communication,
Computer Applications in Business.

Skills

She has strong foundations in statistics and mathematics. She has hands-on experience in building Machine Learning as well as Deep Learning models.

Machine Learning
Data Mining, Decision trees, Cluster analysis, Probability networks, Ensemble methods, Random forest, Gradient Boosting, SVM, KNN
Deep Learning
Custom built neural network models using CNN for image-recognition and RNN,LSTM for captioning
Feature-Engineering
Conducted using PCA, SVD, variable importance from random forest, L1 and L2 norms
Model Evaluation
Tuning of hyper-paameters using K-fold validation. Evaluating using confusion matrix, ROC curve, precision, recall, F1-score and conducting error-analysis for miss-classified instances.

Some technologies I have experience working with :

Work Experience

Aug 2020 - May 2021

UIC - Claris Health

Data Science Graduate Assistant
• Detected fraud in opioids oversupply with Auto-encoders and PCA in Python, eventually saving over ~$10 million in claims. • Architected deep learning LSTM algorithm for anomaly detection in complex claims data sets to mitigate claim risk by ~25%. • Crafted Fraud, Waste & Abuse detection system in healthcare data in PostgreSQL preventing fraud risk and mitigating measures.

May 2020 - July 2020

UIC

Data Science Research Assistant (Volunteer)
• Applying NLP for information retrieval in Python to learn the intent of the input sentence and predict sense of sentence. • Implemented state-of-the-art Flair model to validate the correctness based on contextual embedding and familiarity coefficients.

Jan 2018 - July 2018

ICICI Prudential Life Insurance Company Ltd.

Actuarial Data Analyst – Manager II
• Built predictive time series models in R to predict likelihood of an opportunity to convert from early sales, giving ~89% accuracy. • Optimized dashboards for the segmentation of customers to target marketing strategies thereby increasing sales by $8 million. • Led a team of 5 in computing projections, writing, and presenting technical reports outlining sources of risk preventive strategies.

Nov 2016 - Jan 2018

Reliance Nippon Life Insurance Company Ltd.

Actuarial Data Analytics Consultant
• Devised cluster analysis with K-means segmenting customers, conducted monte-carlo simulations, increasing sales by ~20%. • Automated the procedure of extracting massive amounts of results using VBA thereby speeding up the process by ~70%. • Performed statistical analysis for financial predictions, cost-benefit analyses and risk projections assisting in hedging strategies

Projects

Demonstration of the skills mentioned in GitHub Repositories.

Image Classification
Recognizing facial expressions using Pytorch, Models - ResNet, VGG16

Image Classification
Built an image classifying model with Pytorch, Scikit-Learn, NumPy, SciPy, Pandas, Pickle, MLlib, OpenCV
Open
Manipal Hospital Harvard business review Case study
Determining NPS score in R using supervised methods

Manipal Hospital Harvard business review Case study
Developed supervised machine learning models for NPS score by ggplot2, rpart, randomforest, GBM, ROCR, Caret
Open
Generative Adversarial Networks
Built GANs on the Pokémon data set using Pytorch, Scikit-Learn, NumPy, SciPy, Pandas downloaded from Kaggle

Generative Adversarial Networks
Built GANs on the Pokémon data set using Pytorch, Scikit-Learn, NumPy, SciPy, Pandas downloaded from Kaggle
Open
Movie Recommender System
Built a recommendation model using Alternating Least Squares using Pyspark

Movie Recommender System
Built a recommendation model using Alternating Least Squares using Pyspark
Open
Web Scrapping
Used Selenium, Beautiful Soup to scrape online Dell Query data in Python

Web Scrapping
Used Selenium, Beautiful Soup to scrape online Dell Query data in Python
Open
HackerRank Codes
Neatly coded solutions for Machine Learning and AI in Python

HackerRank Codes
Neatly coded solutions for Machine Learning and AI in Python
Open

Statistics Blogs

She has explained statistics from the basics and currently proceeding towards advanced topics.

Journey towards data science
From Commerce to Actuarial Science to Data Science

A Journey towards Data Science
A glance at my long journey towards where I am.
Open Blog
Chapter 1 Statistics
Learn how to calculate the numerical measures with the help of this blog
Chapter 1 Statistics
Learn how to calculate the numerical measures with the help of this blog
Open Blog
Chapter 2 Data and Graphs
The types of data and the graphical representation of the descriptive statistics, that is, the methodology for describing or summarising a set of data using tables, diagrams
Chapter 2 Data and Graphs
The types of data and the graphical representation of the descriptive statistics, that is, the methodology for describing or summarising a set of data using tables, diagrams.
Open Blog
Chapter 3 Probability
Learn how to calculate basic probability with the help of this blog
Chapter 3 Statistics
Learn how to calculate the basic probability with the help of this blog.
Open Blog
Chapter 4 Random Variable
Learn random variables and their moments.
Chapter 4 Random Variable
Learn random variables and their moments.
Open Blog
Chapter 5 Probability Distribution
Learn probability distributions. These distributions are used in real-life and you are now going to learn what they are, what are their expectations among other things
Chapter 5 Probability Distribution
Learn probability distributions. These distributions are used in real-life and you are now going to learn what they are, what are their expectations among other things
Open Blog