ANKIT CHAHAR

Dedicated And Hard Working Computer Science Engineering Undergraduate student at R.V. College Of Engineering, Bangalore, India. Interested in Machine Learning, Deep Learning, Research, finance and innovating to solve real world problems. Aiming to leverage proven critical thinking, teamwork and multitasking skills to successfully fill the software engineer role.

• Core-Team Member and Senior Associate at E-Cell, RVCE
• Coordinator at Rotaract Club, RVCE:2019-2022
• Member, MUN MK Convent:2016-2018

• Ankit Chahar, Sai Rohith T., Ninad Patil, Rajat Shah, “An Indispensable Contemplation on Natural Language Processing Using Ensemble Techniques for Text classification” In 8th International Conference on Advanced Computing and Communication Systems (ICACCS 2022),
  Published for inclusion into IEEE Xplore.
• Ankit Chahar, Anish R., Anson A, Abhishek S., Chandan V., “Stroke Prediction System Using Machine Learning Algorithm” in the International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) 2021, Published as Springer Lecture Notes.
• Swapnil Bhalerao, Ankit Chahar, Amal Mathew, Eshita Sohani, “ Relative analysis of classification techniques for stroke prediction system” In International Conference on Computational Intelligence & Engineering System Design (CIESD-2021), Published in AIP Journal as Conference Proceedings.

• Classifying Genes for Lung Cancer using Genome Sequencing Data

Dataset Annotation ,Dataset Preprocessing, Undersampling using Near Miss. Four types of Classification models were implemented and tested, Logistic Regression, KNN Classifier, Support Vector Machine Classifier and Random Forest Classifier, All the machine learning algorithms were implemented with Python using the scikit-learn library (26) and the preprocessing of the data using the imbalance library(27).

• Instant Application for Samsung Store

Dynamic Delivery And instant feature for Applications, thus reducing the overall size of the apps. Deployed it for the first time in the Samsung App Store. Utilized Android studio, Java, Git, Github.

• Ankit Chahar, Sai Rohith T., Ninad Patil, Rajat Shah, “An Indispensable Contemplation on Natural Language Processing Using Ensemble Techniques for Text classification” In 8th International Conference on Advanced Computing and Communication Systems (ICACCS 2022),
  Published for inclusion into IEEE Xplore.
• Ankit Chahar, Anish R., Anson A, Abhishek S., Chandan V., “Stroke Prediction System Using Machine Learning Algorithm” in the International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) 2021, Published as Springer Lecture Notes.
• Swapnil Bhalerao, Ankit Chahar, Amal Mathew, Eshita Sohani, “ Relative analysis of classification techniques for stroke prediction system” In International Conference on Computational Intelligence & Engineering System Design (CIESD-2021), Published in AIP Journal as Conference Proceedings.
• Ankit Chahar, V Manikanta S., Ankit Basrur , Sameer. A., Arohan Ajit , Ninad Patil “Mental Health At Work Prediction Using Neural Networks” in 8th International Conference on Advanced Computing and Communication Systems (ICACCS 2022),Published for inclusion into IEEE Xplore.
• Ankit Chahar, Y. Angeline Christobel ,Ritika, Prashant K. Adakane ,Deepa Sonal,Vikas Tripathi,” The Implementation of Big Data With Cloud and Edge Computing in Enhancing the Smart Grid Information Processes Through Sem Model” Published for inclusion in IEEE Scopus Indexed.
• Ayanesh Chowdhury, Ankit Chahar, Bharath Kumar Thulasidoss, Maria Clarissa Fionalita, Shanmuga Vel, “Student performance Prediction using Ensemble Technique” in 8th International Conference on Advanced Computing and Communication Systems (ICACCS 2022), Published for inclusion into IEEE Xplore.
• Ankit Chahar, Ayanesh Chowdhury ,Bharath Kumar Thulasidoss ,Putta Vishal Reddy, Harshal Patel, Ninad ,“Water Quality Analysis Using Deep Learning ” Published for inclusion in IEEE.
• Shreeanant Bharadwaj , Swapnil Bhalerao, Abhishek.Suryawanshi, Eshita Sohani, Ankit Chahar, Anish Reddy Banda, "Breast Cancer Prediction using Neural Networks "In International Conference on Computational Intelligence & Engineering System Design (CIESD-2021), Published in AIP Journal as Conference Proceedings.
• Ankit Chahar,Ayanesh Chowdhury,Bhogadi Sreeja,Kota Nitish,B Nitesh Vamshi,V Manikanta Sanjay " Lethargy Detection during work using Keras Deep learning " Published for inclusion into IEEE Xplore .
• Ayanesh Chowdhury,Ankit Chahar,Rohan Eswara,Dr.Mohammed Abdul Raheem,Shaik Ehetesha,Bharath Kumar Thulasidoss" Fetal Health Prediction using neural networks " in 8th International Conference on Advanced Computing and Communication Systems (ICACCS 2022),Published for inclusion into IEEE Xplore.

• Published for inclusion into IEEE Xplor

• Classifying Genes for Lung Cancer using Genome Sequencing Data Dataset Annotation ,Dataset Preprocessing, Undersampling using Near Miss. Four types of Classification models were implemented and tested, Logistic Regression, KNN Classifier, Support Vector Machine Classifier and Random Forest Classifier, All the machine learning algorithms were implemented with Python using the scikit-learn library (26) and the preprocessing of the data using the imbalance library(27).
• Instant Application for Samsung Store Dynamic Delivery And instant feature for Applications, thus reducing the overall size of the apps. Deployed it for the first time in the Samsung App Store. Utilized Android studio, Java, Git, Github.
• Sports Event Management System for Sports Department of RVCE The application uses SQLite database and the JavaScript library, React for frontend development. Services and tools like Github, Git, Postman, Python Anywhere. There are two interfaces between the user and the software, a web portal for the administrator and a mobile application.
• Stock Management system for a Exchange Completely integrated application with Real time data feed for both the investor and the company of invested stocks also provided with public information about competitors. Used MySQL, Redis, React, Next.js, Express, NodeJS, TensorFlow.js.