Using tf_idf statistics to determine how important a word is to a document in a collection of documents

PROJECTS from Data Science and Analytics, MSc Program 2016-2017 | Hira Fatima

Repositorio com códigos relacionados a pesquisa de TCC sobre desempenho dos algoritmos Naive Bayes, RL e SVM para classificação de revisões.

Code for UCSD CSE 258 Web Mining and Recommender Systems

Walkthrough a toy example of Latent Semantic Analysis

Web app to match resume to job type, using nlp svm classifier model. Data via webscraping. Uploaded resume converted from PDF to text using OCR.

Text classification using Naive Bayes Algorithm¶

Finding optimal clusters for text data using tfids , silhoutte , elbow method , and kmeans

This case study shows how to create a model for text analysis and classification and deploy it as a web service in Azure cloud in order to automatically classify support tickets. This project is a proof of concept made by Microsoft (Commercial Software Engineering team) in collaboration with Endava http://endava.com/en

文本查重小程序

Use of inverted index to find similar documents in a data frame

Tunable full text search engine in JavaScript that: (1) works natively on web apps like Express.js; (2) easy to customize (via BM25) to specific types of documents (e.g. tweets, scientifc journals); (3) is deployable on either the client-side or the server side.

Predict search relevance given a product name and its text attributes

Prediction using KNN and it's hyperparameter tuning.

Python natural language pre-processing scripts

A Term Frequency and inverse distance Frenquency (TF-idF) algorithm in Java language using concurrent techniques

Implemented Machine Learning Models on Amazon Fine Food Reviews Data Set