Hello, I'm Aanisha Bhattacharyya

A Computer Science and Engineering student, with a love towards Coding.

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About Me

I am Aanisha Bhattacharyya,student of BTech,Computer Science and Engineering,in Institute of Engineering and Management,Kolkata. I do web-development, and also enjoy exploring Deep Learning projects, that can make a social impact.I am a dedicated learner,and love to learn new skills.

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My Skills

FrontEnd Web Development

90%

Backend Web Development

80%

Python

90%

Deep Learning

65%

C++

80%

My Resume

Education

August 2018 - June 2022

B Tech in Computer Science and Engineering

First Year YGPA: 9.00

Institute of Engineering and Management,Kolkata
March 2016 - May 2018.

ISC-Class 12

Scored 95% in ISC boards,affliated to CISCE,New Delhi.Had PCM with Computer Science.

Methodist School,Dankuni
April 2004 - May 2016

ICSE-Class 10

Scored 94.6% in ICSE boards,affiliated to CISCE,New Delhi.Had Science with PCMB and Computer Science.

Methodist School,Dankuni

Experience

September 2021 - Present

ML Intern - Julia

I work on the surrogate modelling team

Julia Computing
May 2021 - Aug 2021

Research Intern

I worked on a project involving multimodal document generation.

Adobe Research
October 2020 - Apr 2021

Junior ML Engineer

I worked on the Child Growth Monitor Project, to detect malnutrition in children and on Envisionit project, to facilitate detection of breast cancer from scan images.

Omdena

My Projects

Dog App Classifier

This model can classify between dog breeds and also, for a human, matches it with a breed, its likely to resemble(as a fun addition). The model is made using transfer learning on ResNet50 Model.

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Love Song Lyrics Generator

This is a LSTM model made in PyTorch, which can generate new love song lyrics, from the dataset of songs.

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Emotion Detection in Realtime

This is a Flask App, which can detect and classify emotions, like happiness, sadness, etc in realtime, supports more than 1 person at a time. The model is a CNN, built using Keras and Tensorflow backend, with an accuracy of about 70%.

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Style Transfer

Implemented Style Transfer, as a part of a competition in the Udacity Challenge Phase of the Deep Learning Nanodegree. Used VGG19 model, for implementing the Style Transfer.

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Mask detector

To detect, whether a person is wearing a mask or not, trained on images from a Kaggle dataset.(Still working on this project)

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Fake News Detector

Made the model with Sklearn, using TfidfVectorizer for feature extraction and PassiveAggressiveClassifier as Classifier. Model Accuracy: 94.32%.

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CovidAPI

This is an API to give information about the Covid19 cases around the world.And also country-wise.Its built in Flask[Python].Hosted on Azure.

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AirPurity

An action on Google Assistant, to display information about the aqi[Air Quality Index] of a city, in real-time.Made using a custom webhook using NodeJS and Air Visual API.

ChemistryAPI

This is an API to give information about elements in the periodic table.Its hosted on Heroku.

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LearnEnglish

LearnEnglish is an API integrated responsive website, to tell the meaning of words in English and Spanish, as well as the correct pronunciation of the word.

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Real-time-image-classifier

This is a ml5.js based realtime image classifier that classifies images taken from webcam, using mobilenet model.

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Additional Achievement

Education

Bertelsmann AI Scholar

Awarded fully sponsored DeepLearning Nanodegree, among 1500 students, based on my performance on the challenge phase.

Completed Google FooBar

Got the oppurtunity, to participate in FooBar, in May 2020.

GeekForGeeks

Rank 8 in my college, I practice DSA and coding challenges.

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Technical Script Writer 2020

Ranked 47, in the Technical Script Writer Competition by geeksforgeeks for my blog: "Creating a JSON based API using NodeJS".

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GCP-Machine Learning Track

For successful completion of 5 tracks, under Machine Learning, in Qwiklabs.Was awarded swags, from Google & DSC.

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Experience

Published Research Paper

My research paper-A REVIEW ON HUMAN ACTION RECOGNITION IN SURVILLANCE VIDEOS-was done in a group of four and was published in a recognised Journal.

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