Ameya Kshirsagar
About Ameya Kshirsagar
Ameya Kshirsagar is a B.Tech in Information Technology graduate from Symbiosis Institute of Technology with gusto to discover value-added aspects of Computer Science and Data Science, competency to take on every learning challenge. His research interest lies in NLP and machine learning applications for interdisciplinary applications ranging from finance, Healthcare, Environment, petroleum, and sports.
Email:ameyark28@gmail.com
Degree:Bachelor of Technology
Phone::D
City:Mumbai
Skills
Certifications
Achievements and Awards
Pro-Tech 2020 National Project Exhibition
Distinction - Science
1st Open State Level Martial Art Championship
Maharashtra State Level Karate Championship
Education
2016-2020
Bachelor of Technology in Information Technology
Symbiosis Institute of Technology, Symbiosis International University
● Member of Google Developer Student Club.
● Feliciated at ProTech 2020.
● Student Mentor.
Experience
May 2020 – Present
Research executive
Pandit Deendayal Energy University - Formerly (PDPU)
Working on Machine Learning, Artificial Intelligence and NLP applications in healthcare, environment, finance, and petroleum industry.
● Implemented machine learning model geothermal micro-seismic event classification and subsurface temperature prediction.
● Performed Exploratory Data Analysis for Higher education trends for Gujarat, India.
● Performed data analysis and prediction of JSC, VOC, FF, and Efficiency (%) for organic photovoltaic cells.
● Implemented machine learning models for efficiently predicting COVID-19 trend patterns.
June 2021 – August 2021
Machine Learning Engineer
Omdena
● Successfully collaborated to build healthcare solutions to predict cardiac arrest due to pulseless electrical activity and asystole employing machine learning for 'Transformative.ai' with AUC = 0.91 and accuracy = 97.05%.
● Lead a group of 11 members to accomplish a sub-task.
●Collaborated on an open-source to implement artificial intelligence techniques and satellite-image analysis to reduce freshwater wastage in Egypt.
June 2021 – August 2021
Data Science & Business Analytics intern
The Sparks Foundation
● Completed tasks with efficiency ranging from peer-reviewing, data wrangling to modeling to learn the pipeline of machine learning projects: data collection, data cleaning, data visualization, modeling, and prediction.
July 2021 – September 2021
Data Science Intern
LetsGrowMore
● Successfully implemented several machine learning and NLP models for applications ranging from stock forecasting, next-word prediction, etc., to contribute to the organization.
January 2021 – August 2021
Delegate
Harvard Project for Asian and International Relations (HPAIR)
● Selected as one of the delegates from 60+countries for the HPAIR conference, which features world-class speakers and guests to foster mentorship, networking, and guidance opportunities for delegates.
January 2019 – July 2019
Data Science apprentice
Persistent Systems
● Improved and automated insurance claiming process with image preprocessing, OCR (PyTesseract), and entity extraction to successfully cover end-toend solutions and aspects of the online claim-reimbursement system with more than 96% accuracy
Stock market prediction of BSE SENSEX using numerical and textual analysis
Implemented five models (Random Forest, Decision Tree, AdaBoost, LightGBM and XGBoost) to compare the capability to forecast the stock market values of SENSEX (S&P BSE SENSEX) with consideration of sentiment analysis. And Random Forest Regressor outperformed other models.
COVID-19 fake/misleading news identification
Implemented NLP for COVID-19 fake news detection. Used tokenization and stopwords for preprocessing, followed by lemmatization for feature engineering followed by scaling with Tf-IDF. Employed several models where logistic regression outperformed other models.
CricFreak
Performed web-scraping, data parsing, pre-processing, and EDA followed by implementing Multi-layer Perceptron Regressor to perform cricket score prediction and angular for the frontend of the web-based application.
US election misinformation identification
Performed NLP misinformation identification for the US election (November 8, 2016). Employed tokenization and stopwords for data cleaning, used word cloud for data visualization followed by feature engineering via lemmatization and scaled with Tf-IDF. Implement four models where logistic regression outperformed SVC, Passive-aggressive classifier and KNneighbours classifier.
Research work/papers
Contact Me
Contact me at
:D
Address
Mumbai
ameyark28@gmail.com