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In this repository, I have developed the entire server-side principal architecture for real-time stock market prediction with Machine Learning. I have used Tensorflow.js for constructing ml model architecture, and Kafka for real-time data streaming and pipelining.

JavaScript 99.96% Shell 0.04%
deep-learning kafka machine-learning mongodb nodejs streaming tensorflow tensorflowjs tensorflowjs-models

real-time-stock-market-prediction's Introduction

Hi there ๐Ÿ‘‹

I am Victor Basu, I'm a passionate Data Scientist and Machine Learning Engineer with a strong background in turning data into actionable insights and building intelligent systems. My journey in the world of data and machine learning began with a curiosity to unravel the hidden patterns in data and use them to make informed decisions. I am a kaggle Notebooks Master, you could follow me on Kaggle at @basu369victor. I have a hand full of experience with the technologies required today at the industry level. Other than Data Science and Machine Learning I do take some interest in web-development stuffs. You could also follow me on LinkedIn at @Victor Basu

My contribution to Keras -

My work on Attention based Protein Structure Prediction

protein Structure Prediction

Highlight

Facial Emotion Recognition

HLD

In this project I have developed an end-to-end pipeline for real-time Facial emotion recognition application through full-stack development. The frontend is developed in react.js and the backend is developed in FastAPI. The emotion prediction model is built with Tensorflow Keras, and for real-time face detection with animation on the frontend, Tensorflow.js have been used.

GitHub project repository - Facial Emotion Recognition

High quality youtube video available at - https://youtu.be/aTe05n6T5Vo

Architecture to host QuickSight Dashboard for HuggingFace model monitoring deployed on SageMaker along with data EDA

architecture

This is a solution that demonstrates how to train and deploy a pre-trained Huggingface model on AWS SageMaker and publish an AWS QuickSight Dashboard that visualizes the model performance over the validation dataset and Exploratory Data Analysis for the pre-processed training dataset. With this as the architecture for the proposing solution, we try to solve the classification of medical transcripts through Machine Learning, which is basically solving a Bio-medical NLP problem. In this solution, we also discuss feature engineering and handling imbalanced datasets through class weights while training by writing a custom Huggingface trainer in PyTorch.

GitHub project repository - Host QuickSight Dashboard for HuggingFace model monitoring deployed on SageMaker along with data EDA Demo video - https://youtu.be/RhTSnn41cnM

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real-time-stock-market-prediction's Issues

How to run?

Can you please make a video tutorial as to how to run this project?

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