ritabratamaiti

AI_in_Finance

This is the code for "AI in Finance" By Siraj Raval on Youtube

ASD-ML-API

This project has 3 goals: To find out the best machine learning pipeline for predicting ASD cases using genetic algorithms, via the TPOT library. (Classification Problem) Compare the accuracy of the accuracy of the determined pipeline, with a standard Naive-Bayes classifier. Saving the classifier as an external file, and use this file in a Flask API to make predictions in the cloud.

Blooddonorprediction

Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use them for predicting the values of a certain field, given that we have information regarding the other fields. Most specifically, in this study, we look at the Electronic Health Records (EHRs) that are compiled by hospitals. These EHRs are convenient means of accessing data of individual patients, but there processing as a whole still remains a task. However, EHRs that are composed of coherent, well-tabulated structures lend themselves quite well to the application to machine language, via the usage of classifiers. In this study, we look at a Blood Transfusion Service Center Data Set (Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan). We used scikit-learn machine learning in python. From Support Vector Machines(SVM), we use Support Vector Classification(SVC), from the linear model we import Perceptron. We also used the K.neighborsclassifier and the decision tree classifiers. We segmented the database into the 2 parts. Using the first, we trained the classifiers and the next part was used to verify if the classifier prediction matched that of the actual values.

Chem-Faiss

This projects utilises vector similarity search from Faiss, in conjunction with chemical fingerprinting to build a scalabale similarity search architecture for compounds/molecules.

chucknorrisjokes

Chuck Norris Jokes Generator (JS API Project)

deep_medicine

Landing Page for Medical Professionals interested in improving their medical analysis using Deep learning tools

DL4GP

DL4GP(Deep Learning for Genomics and Proteonomics) is a project which leverages Deep Learning to learn patterns from genetic and protein sequence. We propose neural network architectures which can perform variety of tasks using NLP like techniques such as identifying sequences and drug discovery.

generated-drones

Drone designs generated with genetic algorithms

pyRunBrowser

Code Editor + Browser Runtime for Python

RapidML

RapidML is a smart Python framework for rapidly prototyping Machine Learning APIs for the Web!

ritabratamaiti/RapidML

RapidML is a smart Python framework for rapidly prototyping Machine Learning APIs for the Web!

ritabratamaiti/Chem-Faiss

This projects utilises vector similarity search from Faiss, in conjunction with chemical fingerprinting to build a scalabale similarity search architecture for compounds/molecules.

ritabratamaiti/ASD-ML-API

This project has 3 goals: To find out the best machine learning pipeline for predicting ASD cases using genetic algorithms, via the TPOT library. (Classification Problem) Compare the accuracy of the accuracy of the determined pipeline, with a standard Naive-Bayes classifier. Saving the classifier as an external file, and use this file in a Flask API to make predictions in the cloud.

ritabratamaiti/pyRunBrowser

Code Editor + Browser Runtime for Python

ritabratamaiti/Blooddonorprediction

Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use them for predicting the values of a certain field, given that we have information regarding the other fields. Most specifically, in this study, we look at the Electronic Health Records (EHRs) that are compiled by hospitals. These EHRs are convenient means of accessing data of individual patients, but there processing as a whole still remains a task. However, EHRs that are composed of coherent, well-tabulated structures lend themselves quite well to the application to machine language, via the usage of classifiers. In this study, we look at a Blood Transfusion Service Center Data Set (Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan). We used scikit-learn machine learning in python. From Support Vector Machines(SVM), we use Support Vector Classification(SVC), from the linear model we import Perceptron. We also used the K.neighborsclassifier and the decision tree classifiers. We segmented the database into the 2 parts. Using the first, we trained the classifiers and the next part was used to verify if the classifier prediction matched that of the actual values.

ritabratamaiti/DL4GP

DL4GP(Deep Learning for Genomics and Proteonomics) is a project which leverages Deep Learning to learn patterns from genetic and protein sequence. We propose neural network architectures which can perform variety of tasks using NLP like techniques such as identifying sequences and drug discovery.

ritabratamaiti/chucknorrisjokes

Chuck Norris Jokes Generator (JS API Project)

ritabratamaiti/covid19-dashboard

🦠 Django + Plotly Coronavirus dashboard. Powerful data driven Python web-app, with an awesome UI. Contributions welcomed! Found on 🕶Awesome-list

ritabratamaiti/Daily-Journal

JSON files mostly

ritabratamaiti/DeepBindRG

ritabratamaiti/docs

Source code for the Streamlit Python library documentation

ritabratamaiti/faiss

A library for efficient similarity search and clustering of dense vectors.

ritabratamaiti/Federated_learning_setup

ritabratamaiti/flask-hello-world

Flask Hello World Example for Render

ritabratamaiti/img2img-turbo

One-step image-to-image with Stable Diffusion turbo: sketch2image, day2night, and more

ritabratamaiti/llm-examples

Streamlit LLM app examples for getting started

ritabratamaiti/NeuralTuringMachine

Tensorflow implementation of a Neural Turing Machine

ritabratamaiti/NewAPI

ritabratamaiti/public_assets

ritabratamaiti/publications

My Publications

ritabratamaiti/quart-schema

Quart-Schema is a Quart extension that provides schema validation and auto-generated API documentation.

ritabratamaiti/ritabratamaiti

ritabratamaiti/scikit-learn

scikit-learn: machine learning in Python

ritabratamaiti/spree

Spree is an open source E-commerce platform for Rails 6 with a modern UX, optional PWA frontend, REST API, GraphQL, several official extensions and 3rd party integrations. Over 1 million downloads and counting! Check it out:

ritabratamaiti/startbootstrap-new-age

A web app landing page theme created by Start Bootstrap

ritabratamaiti/Stock-Analysis

ritabratamaiti/stockraptor

Stock Raptor combines an Artificial Intelligence powered dashboard with a stock ticker. Stock Raptor is currently in an open beta with free access!

ritabratamaiti/tailwind-landing-page

ritabratamaiti/test_v1

ritabratamaiti/transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.