My version of topic modelling using Latent Dirichlet Allocation (LDA) which finds the best number of topics for a set of documents using ldatuning package which comes with different metrics
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Updated
Nov 15, 2018 - R
My version of topic modelling using Latent Dirichlet Allocation (LDA) which finds the best number of topics for a set of documents using ldatuning package which comes with different metrics
A comparison of centroid-based, density-based and hierarchical clustering algorithms
Unsupervised learning project on atmospheric gas data using PCA and K-Means clustering
The multi-sample Gaussian mixture model (MSGMM) is a clustering model adapted to fitting multiple samples simultaneously using the EM algorithm.
analysing patterns in a leaked dataset of passwords with unsupervised market basket-type analysis
Supervised and Unsupervised statistical learning techniques applied on Pima Indians Diabetes Dataset
Kmeans Clustering
Four practical applications: 50% of the overall grade, all weigh equal (12.50% out of 100%). The mark in each has to be greater or equal to 4 (out of 10) to pass this compulsory part.
Individual Academic project for DSA1101: Introduction to Data Science
Implemented a new feature selection algorithm for clustering as part of my master's research.
Recommendation Engine using R
Collection of machine learning algoritm written in R. covering various supervised and unsupervised machine learning algorithm. These codes are made as supplement of academic module in our data mining and knowledge management course.
Fun fact: It is more valuable to hold on to an existing customer than to acquire new ones. Source: Trust me
Public teaching resource on unsupervised learning
Work done for University of Pittsburgh course "Principles of Data Science" (STAT 1261) with Dr. Junshu Bao in Fall semester of 2018.
This project aims to analyze the heart failure dataset to build a classifier that identifies the most important factors and allows predicting death from heart failure.
Using Apache Spark for marketing analytics
🎓 An unsupervised learning project analyzing PISA 2018 data for Finland. Uses PCA and K-Means in R to identify and profile distinct student clusters. Developed for the ULM course at ISCTE.
Partitioning Clustering Analysis of Vehicles Data
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