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Name: Pek Yun Ning
Type: User
Bio: Data Scientist | with exceptional focus on NLP, Text Analytics, Topic Modelling, Recommendation Systems, Trend Analysis, Pattern Recognition, Novel Algorithms
Location: Singapore
Name: Pek Yun Ning
Type: User
Bio: Data Scientist | with exceptional focus on NLP, Text Analytics, Topic Modelling, Recommendation Systems, Trend Analysis, Pattern Recognition, Novel Algorithms
Location: Singapore
Add symbol, currency, and/or 2 d.p. (to every excel cell)
This workshop demonstrates how to build a Document parser and query engine with Amazon Textract and other services, such as ElasticSearch and DynamoDB.
Autofit excel cell widths (using xlwings). Xlwings is an extremely efficient, state-of-the-art python library to manage, edit, and manipulate excel files & data. Its documentation can be viewed at https://readthedocs.org/projects/xlwings/downloads/pdf/stable.
Bold Excel Cells (using openpyxl)
This is how nouns, verbs, and adjectives can be classified from a bunch of text.
Combine excel sheets from two workbooks (using openpyxl)
Hierarchical unsupervised and semi-supervised topic models for sparse count data with CorEx
Correlation Explanation (Corex) is a topic modelling technique that is great at identifying 'hidden' topics, or low-frequency-worded but representative topics, very well. It was originally created by Greg Ver Steeg.
Removes blank cells in CSV files using Python. In Python list, it is seen as 'nan'.
Anonymising data in meeting notes.
Doc2vec method of topic modelling. It's document-level of Word2vec. Builds on the concept of word vector representations.
Doc2X is a novel topic modelling technique created in June 2019, by yours truly, Pek Yun Ning. It hybridises the older Doc2Vec and Corex topic modelling algorithms to form this all-new algorithm.
TensorFlow documentation
A simple implementation of 'enumeration' in Python. In this case, we number webchats from one whole chunk of text filled with tons of webchat entries.
Excel - find unique values in columns, & difference between two columns
FastText is a topic modelling technique originally created by Facebook AI Research team. Its first stable version release was on December 2018. FastText is now available on Python's gensim and scikit-learn as well.
Data science applications in geographical data. Involves hexbins and projections.
LDA2Char is a novel topic modelling technique created in June 2019, by yours truly, Pek Yun Ning. It hybridises the older LDA and FastText topic modelling algorithms to form this all-new algorithm.
LDA2Word is a novel topic modelling technique created in June 2019, by yours truly, Pek Yun Ning. It hybridises the older LDA and Word2Vec topic modelling algorithms to form this all-new algorithm.
LDA2X is a novel topic modelling technique created in June 2019, by yours truly, Pek Yun Ning. It hybridises the older LDA and Corex topic modelling algorithms to form this all-new algorithm.
LDA2XPand is a novel topic modelling technique created in June 2019, by yours truly, Pek Yun Ning. It hybridises the older LDA, Corex, and Word2Vec topic modelling algorithms to form this all-new algorithm.
Latent Dirichlet Allocation (LDA) is a topic modelling technique that involves a three-layered probabilistic approach, taking into account words at words, documents, and corpus level. It is accompanied by its very own unique and powerful data visualisation tool, LDAvis (as part of this code in its Pythonic version, pyLDAvis), as well.
An application of Logistic Regression and presentation of its accuracy and precision using Confusion Matrix. This was applied to a consumer complaints dataset.
Model Augmentation by adding more sample data points to an existing (small) dataset in a mathematical manner. Carried out using mathematical concepts such as Euclidean Distance and Uniform Criterion (max-of-min concept). This adds exploration factor to the model, reduces prediction error, and improves global accuracy (in optimisation).
In probability theory and information theory, the mutual information of two random variables is a quantity that measures the mutual dependence of the two random variables. This script performs MI over Mutual Information over discrete random variables
Identifies first names, last names, DOBs, and IDs on scanned documents and IDs.
Natural Language Processing and Named Entity Recognition to automatically get specific structured entities from unstructured texts / data input.
Scikit-Learn, NLTK, Spacy, Gensim, Textblob and more
Non-negative Matrix Factorisation (NMF) is a topic modelling technique that uses matrix factorisation concepts to identify topics and top words that describe each topic.
Changing output of AWS Textract, from JSON to Python readable format.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.