DataScience-Statistics,Machine Learning,AppliedAI

Machine Learning

Application of Malay Word2Vec and its visualization in 2D and 3D space.

A multi-class classification problem where the task is to classify a file to one of 9 types of Malware usually found in a Windows system, using information from the raw data and metadata of the file.

Automatic classification of consumer goods from text and images

Used several clustering algorithms to explore whether the patients can be placed into distinct groups so that myopia, or nearsightedness can be predicted.

t-distributed stochastic neighborhood embedding (t-SNE) is a unsupervised non-linear dimensionality reduction and data visualization technique. The math behind t-SNE is quite complex but the idea is simple. It embeds the points from a higher dimension to a lower dimension trying to preserve the neighborhood of that point. I compared PCA and t-SN…

Deep Learning vs Tranditional ML methods for TB Drug Resistance prediction from Genomic data

Single Cell Expression Atlas t-SNE plot

T-SNE based Image sorting

cluster Ted corpus; visualize in word-cloud; reduce by t-sne

Doc2Vec and Annotated Lyrics: Are they "Genius"? (DSI Capstone II Project)

Classify malware into families based on file content and characteristics

Visualization of various image data using Principal Component Analysis and t-SNE

This project is to build a model that predicts the human activities such as Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing or Laying using readings from the sensors on a smartphone carried by the user.

This project explores the use of glove embedding to improve twitter sentiment classification performance - AI534 class

Analysis of the phylogeny and domains of OLIG2, as well as its expression in cerebral cells using t-SNE. And a chip-seq analysis to further investigate OLIG2 function.

Welcome to "Lab I - Dimensionality Reduction"! This repository contains practical exercises and code examples to help you master the fundamental concept of dimensionality reduction in data analysis and machine learning. Explore various techniques, including PCA, t-SNE, and more, through hands-on coding exercises and insightful explanations

Supermarket customers clustering using k-means, DBScan, hierarchical clustering and RFM analysis