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Hi there πŸ‘‹

I am Vinay [36], a data science practitioner living in Nijmegen, Netherlands. Originally from India, I have moved around a lot in my growing years and spent the last decade in Singapore. I have worked across multiple domains and picked up various skills that can't be grouped under a single role like a data scientist or data engineer or ML engineer. So I think of myself as the data guy who can work across various stages of AI applications, from research to deployment. Trying to streamline myself a bit more now to pursue projects where my skillset (NLP/ML/DL) or passion (football analytics) find utility. Welcome to my github profile!

πŸ”­ I’m currently working as a ML Developer in the domain of SEO, for similar.ai
πŸ‘― I’m also interested in collaborating on βš½πŸ“ˆfootball analytics related projects. Check my latest package out for plotting tracking/event data - pitchly! Downloads Downloads
πŸ˜„ Pronouns: He/Him
⚑ Fun fact: Love puns (and word games and crosswords). o.pun.soars is a pun on open source (in case you were wondering)

I have used these to varying capacities

My Skills

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Vinay Warrier's Projects

acl-rd-tec-2.0 icon acl-rd-tec-2.0

The ACL RD-TEC 2.0: A corpus of annotated terms in context from domain of computational linguistics

awoiaf icon awoiaf

Extracts data from A wiki of Ice and Fire

clean-data-coursera icon clean-data-coursera

This repository contains the scripts and related files for the project module of the Coursera course "Getting and Cleaning Data" from John Hopkins University

exploratory-data icon exploratory-data

All files pertaining to the projects in Coursera's Exploratory Data Analysis course

football-transfers-network-analysis icon football-transfers-network-analysis

ANALYSIS OF FOOTBALL TRANSFERS IN EUROPE USING NETWORK SCIENCE Jan 2016 – Apr 2016 Project description Football is arguably the most popular game worldwide, and from a network perspective it provides myriad of data to analyse. Association Football in Europe is the hub for all the businesses related to the game as well as dream destinations of many players across the globe. The movement of a player from one club to another is termed a football transfer and this event has a fee involved between the dealing clubs, most of the time. Since such transfers make or break a club’s performance for a season or more, they are a subject of immense interest in sports analytics and football betting. Using network science techniques, we analyze the European transfer market from data for 6 seasons spanning the top 5 countries playing football. We learn how this directed network of multiple attributes and dimensions can unravel and confirm some club performances based on network characteristics like betweenness centrality, reciprocity, and transitivity. The data is collected using Python and all social network analysis is performed using R.

kloppy icon kloppy

kloppy: standardizing soccer tracking- and event data

lastrow_to_fot icon lastrow_to_fot

Functions to convert from LastRow tracking format to FoT friendly format

machine_learning icon machine_learning

Python coded examples and documentation of machine learning algorithms.

ml-from-scratch icon ml-from-scratch

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

pitchly icon pitchly

A python package that is a wrapper for Plotly to generate football tracking and event data plots

pymdb icon pymdb

Python module to scrape data from IMDb Top 250 and parse xml of movie data from OMDb API (www.omdbapi.com)

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