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harsh9t

Discovered public repositories for harsh9t in the GitHub catalog.

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harsh9t/simple-spreadsheet

Simple spreadsheet is a Ruby Gem reader for common spreadsheet formats: Excel (.xls, .xlsx), Open-office (.ods) and CSV (standard, excel, tab separated)

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harsh9t/rdflib

RDFLib is a Python library for working with RDF, a simple yet powerful language for representing information.

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harsh9t/rdf2html

This tool generates a bootstrap webpage from an semantic (RDF) ontology

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harsh9t/Estadistica

Scripts que automatizan el proceso de calculo estadísticos. Útil para estudiantes de Ingeniería de Sistemas en la Santiago Mariño.

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harsh9t/Terminal

Make a website like a Unix Terminal. Incorporate your own commands.

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harsh9t/anyToJSON

Fetches CSV, JSON data from REST APIs, Flat Files etc and converts them to JSON. Extendible to support additional data formats

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harsh9t/ListRank-MF

A Java implementation of ListRank-MF as mentioned in List-wise Learning to Rank with Matrix Factorization for Collaborative Filtering

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harsh9t/kaggle-yelp-business-rating-prediction

Code for Kaggle Contest "RecSys2013: Yelp Business Rating Prediction" -- Predicting Business Ratings on Yelp.com Using Machine Learning. My final submission scored an RMSE of 1.2307 which earned a rank of #7 out of ~250 teams. This a collection of code that is meant to be run from python console, it is not a stand-alone program

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harsh9t/kaggle-seeclickfix-ensemble

Prize winning solution to the SeeClickFix contest hosted on Kaggle, developed by teammates Bryan Gregory and Miroslaw Horbal. The purpose of the contest was to train a model (as scored by RMSLE) using supervised learning that will accurately predict the views, votes, and comments that an issue posted to the www.seeclickfix.com website will receive. My teammate and I used this ensemble code base to combine our top ranked individual models to create a prize-winning solution, defeating >500 other teams and winning a prize of $1,000.

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harsh9t/Hits_Letor

Academic Project - Implementation of Hubs and Authorities & Learning to Rank with Microsoft Research Data using SVM

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harsh9t/LETOR-1

Search Parameter Tuning on Microsoft Learning to Rank 4.0 dataset using Linear Regression

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harsh9t/xapian

Mirror of the Xapian repository. You're welcome to open pull requests on github (they'll just get merged indirectly).

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harsh9t/SnippetDetector

This project aims to build a system which given a biomedical question and a set of relevant documents will extract all the relevant snippets from these documents.

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