About Me

Just another stupid person trying to change the world!
About Me
Photo by Erik van Dijk / Unsplash

I am a graduate student in the Institute for Computational and Data Sciences (ICDS) at the State University of New York (SUNY) at Buffalo currently pursuing MS in Engineering Sciences (Data Science). Apart from that I am a full stack developer proficient in both front-end and server-side languages with knowledge of NodeJS with Express, MySQL, TypeScript, Angular and more.

Why Computer Science?

Computers had caught hold of me since my teenage days, and I developed a passion for them and went on to study computer science and software development. I try to build products through projects which might influence the human life in an efficient manner. To look at my projects till now, go through my GitHub Profile. I do not always create perfect or useful things and few projects are just for fun, I am trying to do better! Well, there is certainly yet more to learn, yet more problems to solve and yet more to build in the not too distant future!

Although I am a person related with computer science, I equally enjoy concepts of physics, and if I didn’t have gone for this field, I might have ended being a physicist. Good thing, I didn't!


  1. A Comprehensive Approach to Analysis and Detection of Emerging Threats due to Network Intrusion – This project aims to build a predictive model to fit the data and evaluate performance of the network intrusion detection mechanisms based on the ML methods. This also aims to provide the new researchers with the updated knowledge, recent trends, and progress of the field – which is very imperative in this era.
  2. Predicting Mortality Rate based on the Comprehensive Features of ICU Patients – Performed an exploratory data analysis (EDA) using R while utilizing a SQL based dataset to obtain the aggregated features and pre-process data. Built Random Forest and Naïve Bayes Machine Learning models on the extracted data. Research Paper submitted to IEEE MediComp 2022.
  3. Analyzing Earth Surface Temperature using Previous Climate Change Patterns – Built a time series machine learning model to predict the 7-day temperature forecast for any provided city. The dataset used is the Berkeley Earth Surface Temperature Study dataset. Temperatures from 1950 to 2015 have been considered. Project available on GitHub.
  4. Sentiment Analysis of Topic-Wise Tweets using Kafka, Tweepy and PySpark – Sentiment analysis of tweets for a particular targeted topic. The project using Tweepy Streaming, Kafka Producer and Consumer to read relevant tweets. It produces a live matplotlib bar chart with “Positive”, “Negative” and “Neutral” variables. The sentiment of each tweet is also stored in a Cassandra database. Project available on GitHub.