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💫 About Me:

Hi, I am Somdyuti, a Software Engineer proficient in Full Stack Development and Machine Learning.

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💻 Tech Stack:

CSS3 Java HTML5 JavaScript Python Azure AWS Angular Angular.js Bootstrap NodeJS React React Native Spring NPM Apache Maven Jenkins MySQL MongoDB Keras NumPy Pandas PyTorch scikit-learn SciPy TensorFlow Jira Raspberry Pi

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Somdyuti Das Adhikary's Projects

chat-app icon chat-app

This is a chat app created using web sockets from socket.io

companies-to-apply icon companies-to-apply

This repository contains opportunities for you to apply to more than 150 product base companies(NOT JUST FAANG ) & good start-ups.

computer-science icon computer-science

:mortar_board: Path to a free self-taught education in Computer Science!

covid-19_analysis_and_prediction icon covid-19_analysis_and_prediction

COVID-19 Visualization and Prediction using different ML algorithms. A comparative study of the accuracy of different ML algorithms is done.

covid-19_x-ray_detection icon covid-19_x-ray_detection

In this project, we will analyze the chest X-Rays of people, in order to differentiate between infected and non-infected patients.

developer-roadmap icon developer-roadmap

Interactive roadmaps, guides and other educational content to help developers grow in their careers.

fullstack-course4 icon fullstack-course4

Example code for HTML, CSS, and Javascript for Web Developers Coursera Course

sentinel-a-smart-fire-fighting-helmet icon sentinel-a-smart-fire-fighting-helmet

Fire-fighting has been a problem since the medieval ages. Even with the advent of modern technology , we haven ’t been able to reduce fire accidents significantly . Our proposed project aims to use all relevant data, to remedy any fire accident. The project uses a variety of sensors and the collected data is processed and utilized to alert authorities and display meaningful results . All our sensors are integrated with one central Micro -controller . The Micro-controller is responsible for relaying the data to a shared database , from where the processed data is retrieved. Location data will be broadcast from the server to all clients in the nearby area.

the_spark_foundation icon the_spark_foundation

The following tasks are part of the internship program at GRIP, The Sparks Foundation on Data Science and Business Analytics domain.

vehicle-classification-with-engine-sound-using-neural-network icon vehicle-classification-with-engine-sound-using-neural-network

In the research reported in this paper, we studied the efficacy of the LVQ algorithm in classifying engine noises from household cars. We note that the accuracy rate of LVQ is only satisfactory and there is still a considerable amount of tweaking that the model requires before it is implementable for any of its use cases (toll plazas, car parkings, etc). One prominent restriction is collecting high quality audio samples of engine noise from regular street cars. To automate it is difficult and to do so manually is time intensive. This research suggests that while the LVQ algorithm has a lot of potential for classifying time based signals there is a long way to go before State of the Art (SotA) automobile engine noise classification is achieved using this technique.

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