I have been into the field of Machine Learning and data Analysis for about 2 years now, beginning to dive into Deep Learning. I have a profound knowledge on various Machine Learning algorithms like Linear regression, Logistic Regression, Support Vector Machines, Naive Bayes, Decision Trees, etc. I have worked on multiple project works like Diabetes Prediction, Fake News prediction as well as Emotion Prediction Models. I have even worked in the field of IoT and Front end Development as a part of my hobby to learn more about different tech. Moreover, I recently got into Digital Signal Processing and it totally got into me on how to combine ML and DSP together. Since then, I have been trying to process things using ML.
Programming Languages: Python, C, C++, Java, Solidity, JavaScript, HTML(Markup Language), CSS(Style Sheets), R
Machine Learning Tools: Numpy, Pandas, Matplotlib, Seaborn, Tensorflow, Pytorch, etc.
Electronics and Design Tools: Matlab, GNU Octave, Flexran
Database: MySQL, MongoDB
Cyber Security Tools: Metasploit, Webhttrack, Burp Suite, Netcraft, Whois, Nmap, Maltego, RedHawks, OSNIT
Front end: HTML, CSS
IDEs: Jupyter, Google Colab, IBM Watson Studio, Spyder, VSCode, Intellij, and obviously the Konsole
Other: Linux -> Arch and Debian(Kali Linux), Smart Contracts, Blockchain, Metasploit
Pulled out a dataset from kaggle.com and used it to detect the legibility of the news. The model I used was Logistic Regression, got accuracy about 91%.
Used a Kaggle Dataset to predict whether a patient has diabetes or not, achieving about 76.6% accuracy
Using classical algorithms, specifically Efficient Frontier Method to find out an overall optimized portfolio.
A static but fully responsive music web app made entirely with pure HTML, CSS, and JavaScript. The web app is made entirely with pure logic and no frameworks used.
Control the LED of your NodeMCU ESP8266 using an app made by you using Flutter
Built a Temperature and Humidity Monitoring System using NodeMCU. Wrote a Conference paper on IEEE where a model was built form the same and used specifically for tea estates and other plantations in the hill stations for accurate monitoring of essential parameters.
1. A. Sanyal, P. Chowdhury, C. Ganguly, "IoT- based Wireless Real-time Temperature and Humidity Surveillance System for Hill Stations": Accepted at IEEE International Conference on Recent Trends in Computer Science and Technology (ICRTCST-2021)
Domains: Machine Learning, Software Development, Linux Administration, Python Development, IoT, and anything(Fast Learner)