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Name: Khilesh Chauhan

Type: User

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Khilesh Chauhan's Projects

hate_speech_classifier

Project 3 for Metis bootcamp. Objective was NLP analysis of Tweets to classify as "hate speech," "offensive," or "neither." Primary approach was tf-idf vectorization of n-grams of text and part-of-speech up to size 3. Attempted convolutional neural network on tf-idf vectors, without improvement.

haxl

A Haskell library that simplifies access to remote data, such as databases or web-based services.

healthcare-hl7-xml

HealthCare HL7 XML eases the implementation of HL7 v.2.x in XML format using InterSystems technology

hello-aws-data-services

Sample code for AWS data service and ML courses on LinkedIn Learning

hl7

PHP library for Parsing, Generation and Sending HL7 v2 messages

hl7-1

HL7 utilities in Go

hl7-hero-api

:hospital: Support and library resources for HL7 Hero, a mobile app that parses HL7 2.X Schemas.

hl7-javascript-parsing

Making HL7 look pretty and more organized using jQuery and nothing else.

hl7-json-spark

Converts HL7 v2 to JSON and Spark

hl7-object-parser

:wrench: Convert hl7 messages to javascript object.

hl7-parser

HL7 parser - Golang

hl7-to-json

Some code and usage example for processing streaming hl7 data into an equivalent JSON structure.

hl7.net

EdiFabric C# .NET Examples for HL7 2.6

hl72xml

Convert HL7 message to xml format

hl7cda2

An extensible library to manage HL7 CDA2 documents.

hl7js

Javascript lib for parsing and writing HL7 files

hl7lib

Lightweight Java library for HL7 messages

hl7parser

Python based HL7 parser to easily pull information from an HL7 message.

hl7tools-dotnet

HL7Tools for .NET parses HL7V2 pipe-delimited (|) flat files.

hl7v2-fhir-converter

Converts HL7 v2 Messages to FHIR Resources

home-credit-default-risk

2nd Place Solution 💰🥈

home-credit-default-risk-1

Kaggle home credit default risk competition

horovod

Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

hotel-review-sentiment-analysis

Web-based travel scheduling and booking has become one of the major commercial uses.Hotel booking websites use online rating and customer input to support the decision-making process of the client, but reviews provide a better insight into the hotel, but most travelers do not have time or patience to read all reviews. This research analyzes the ratings of hotels and provides information that may miss. The comments and metadata are crawled from the website and grouped according to some specific aspects into pre-defined categories.Here, we try to make efficient reviews sentiment analysis on “booking.com” hotel reviews and apply NLP to pre-processing of data. After that identifying the subjective information in text and classifying each piece of data as positive, negative and neutral response. This pre-processing data convert into vector and apply convolution neural network(CNN) algorithm on vector matrix and outcomes of CNN represent as pie-chart and bar-chart using Django library. This chart are define base on categories like room, food, cleanliness, service, staff, nature view, facilities.

khileshchauhan Goto Github PK

Hi there 👋

Khilesh Chauhan's Projects

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