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Rajat Shrivastav photo

rajatalpha Goto Github PK

followers: 0.0 following: 1.0 repos: 37.0 gists: 0.0

Name: Rajat Shrivastav

Type: User

Bio: Enthusiast Engineering graduate willing to contribute being one of a kind, seeking to use strong statistical knowledge, analytic ability and communication skill

Location: Mumbai,Maharashtra,India

Rajat Shrivastav's Projects

atcc_app icon atcc_app

This is the full repo of the Automatic Traffic Controller PyQt5 App

cats-dogs_heroku icon cats-dogs_heroku

This is the github folder for the end to end deployment of a basic Dogs and Cats Classifier. This was the very first deployment version. More customization will be appended.

datasets icon datasets

This contains all the datasets required for Assignment Solving......

deansdilemma icon deansdilemma

This repository contains the code for Dean's Dilemma's data , we can work more on this Dataset

easyocr_practise icon easyocr_practise

This repo contains all the python wrappers for OCR, one such wrapper is Easy OCR. Please find the Linksof Github work attached.

hummingbird-demo icon hummingbird-demo

A small demo which shows how Microsoft's Hummingbird can scale ML Model Inferences using GPU's

kpmg_virtual_internship icon kpmg_virtual_internship

This Repo consists of all the codes and docs files necessary for completing all the tasks in hand for the Virtual Internship

langchain_projects icon langchain_projects

This is the Repo where I have hosted 4 projects based on open Source Hugging Face models with a streamlit UI. You can find individual requirements file for each of these projects.

machinehack_energyopprediction icon machinehack_energyopprediction

This repo gives the complete code solution on the Machine Hack Energy output predictions. I have got an RMSE of 3.03 on the final model while the Winner of this hackathon got only 3.22, so good work on this.

machinehack_forestcovertypeprediction icon machinehack_forestcovertypeprediction

This repo gives the full approach and code for Machinehack Forest Cover type Classification. Here LGBM model gave a final log loss score of 0.33, Which I would like to improve to around 0.1. I also added weights to the predictions and 5 fold cross validation. I used Bayesian Optimization to get optimized parameters.

madewithml icon madewithml

Learn how to responsibly deliver value with ML.

mlassignments icon mlassignments

This Repository contains all the assignments of Andrew Ng course completed in MATLAB.

myfirstrprogram icon myfirstrprogram

This is the code cotaining all the r codes inside the books workly perfectly.

opencv icon opencv

Open Source Computer Vision Library

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