Ashwin Agrawal

ABOUT ME

Developer at R Organisation-Google Summer of code and Quantitative Research Consultant at World Quant LLC (algo trading)

R Developer- Google Summer of Code in R organisation for statistical computing r- Research Consultant at World Quant- Algo trading-Developed several sophisticated trading signals for trading in US, European and Asian markets in python and expression.
Data structures and Algorithms in advanced C++ and C, Advanced Machine learning in R.
Always ready to help and contribute:)

Hindi, English

Chennai (+05:30)

Joined July 2017

EXPERTISE

1 year experience

Selected for second time consecutive for google summer of code 2018 under R project for statistical computing

Shiny R

Data Science

2 years experience

SOCIAL PRESENCE

GitHub

Technical-Indicators-In-C-

C++ codes of different technical indicators in financial markets

C++

BiodiversitydatacleaningGSoC17

Biodiversitydatacleaning

HTML

Stack Overflow

569 Reputation

EMPLOYMENTS

Data Science Software Developer

Myntra

2018-06-01-Present

Develop and build Machine Learning/Deep learning solutions

Python

Pandas

Python

Pandas

Apache Spark

Deep Learning

Theano

Data science in python

PROJECTS

Classification model for bank telemarketing (Data mining, Feature Selection)View Project

2016

1) The research is based on data related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns wer...

1) The research is based on data related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. 2) The goal is to classify whether a client will subscribe a term deposit (yes/no).

Machine learning

Big Data

Machine learning

Big Data

Neural Networks

Random forest

Deep Learning

Feature selection

Predicting air pollution levels (PM10) in a designated city using Machine Learning algorithms.

2017

1) The project was based on building a robust predictive model using different machine learning algorithms and time series analysis for f...

1) The project was based on building a robust predictive model using different machine learning algorithms and time series analysis for future prediction of air pollution levels. 2) Different machine learning algorithms were applied (Random Forest, SVM, Neural Network, Deep Neural Networks).

Neural Networks

Random forest

Neural Networks

Random forest

Deep Learning