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AYUSH OTURKAR photo

ayush1695 Goto Github PK

followers: 6.0 following: 5.0 repos: 19.0 gists: 0.0

Name: AYUSH OTURKAR

Type: User

Company: AB-InBev

Bio: Data Scientist with extensive experience working across diverse industries where I help generate business value by crunching data using AI/ML.

Twitter: oturkar_ayush

Location: New York, United States

You can Reach me out at 👉   ayushoturkarnitb    oturkar_ayush  ayush1695#3473  Buy me a Coffee 👈 You can buy me a Coffee at  

Brief Profile Summary:

👉 Graduate student with close to 5 years of prior Data Science experience in delivering data-driven solutions using cutting-edge AI/ML to healthcare & CPG behemoths.

👉 Worked on real world problem statements majoring recommendation system, payment leakage detection, spend optimization, driver analysis and sales forecasting, image classification, fraud detection, Named Entity Recognition and many more.

👉 Helped organizations generate/save more than $10 M in value realization to date. Skilled in solving complex problems using right mix of technical & business skills.

👉 Data Science Fanatic 🤟

👉 Kaggle Master

My Day-to-Day Work Glimpse:

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Kaggle Standings

Ayush Oturkar's Summary Card

What I hope to achieve:

---Complete ML Life Cycle Architect--

GitHub Statistics

Gif Credit: Google Cloud

AYUSH OTURKAR's Projects

fraud-detection-model icon fraud-detection-model

Developed a robust credit card Fraud detection model on highly imbalance credit card transaction data.

game_analytics_eg icon game_analytics_eg

Did some Advanced Analytics on gamers data to help coaches optimize the competition pool and source right talent leveraging insights from historic data.

lightgbm-with-focal-loss icon lightgbm-with-focal-loss

An implementation of the focal loss to be used with LightGBM for binary and multi-class classification problems

mlops-iris icon mlops-iris

Create a fully automated, end-to-end IRIS Training and Deployment using Azure MLOps

polars icon polars

Fast multi-threaded, hybrid-out-of-core DataFrame library in Rust | Python | Node.js

predict_future_sales_spring-capstone icon predict_future_sales_spring-capstone

Aim of this project is to develop a robust predictive model for accurately forecasting product sales across multiple shops, even under conditions of varying shop and product availability. This project will utilize a real-world dataset containing daily historical sales data, spanning January 2013 to October 2015, along with other supplemental info.

shap icon shap

Implementation of SHAPly values for Black box model interpretation on Diabetes multi-class classification Dataset

song-popularity-prediction icon song-popularity-prediction

Statistical & Predictive Analysis of Song Polularity. Involves Hypothesis testing & detailed exploration of levers affecting a song popolarity.

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