#pca-analysis

Seeing Beyond 3D: How t-SNE Turns Complex Data into Strategic Business Insight

In today’s data-intensive economy, organisations routinely manage high-dimensional data. Datasets are characterized by hundreds or even…

Normalizing Data and Princible Component Analysis

Mastering Applied Data Science | Day 5

Machine Learning With Manya: The Case of the Squished Play-Doh Puppies

How Manya and Pallabi used a magic trick called t-SNE to flatten a 3D toy world without crushing close friends.

Feature Extraction in Machine Learning

What is Feature Extraction?

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is one of the most popular unsupervised machine learning techniques for dimensionality reduction, data…

Mastering Principal Component Analysis (PCA): A Comprehensive Guide with Detailed Math and Code

PCA is arguably the most elegant algorithm in machine learning — a mathematical scalpel that strips away noise and redundancy to reveal the…

A Practical Guide to PCA: How to Shrink Your Data Without Losing What Matters

“The art of being wise is the art of knowing what to overlook.” — William James

Behind the speeches: A topic modeling analysis of colombian presidential candidates (2026)

A Machine Learning approach to compare 5 government plans — without picking sides. Author: Eduardo Toledo . Github repo

Wokeness detection

INTRODUCTION

🌐 Principal Component Analysis (PCA): Simplifying High-Dimensional Data

Introduction

Introduction

Indian equity returns(Nifty 50) are not 50 independent things moving randomly. They move together in patterns, the whole market goes up…

Extracting Mortality Trends Using PCA and ARIMA: An Actuarial Case Study of Bangladesh

The full data pipeline, models, and visualizations are available in the GitHub repository below.

The Day I Realized Most Data Is Useless (And How PCA Fixed It)

It Started With 100 Features… and Zero Clarity

Unsupervised Learning & Dimensionality Reduction

As part of my #GDGOCAttock-AIML-Fellowship1, Week 7 was all about Unsupervised Learning — learning how to let the data speak for itself…

PCA: Proving the Magic with Pure Linear Algebra

We’ve all seen the advice. If you ask, “How do I become a great AI researcher?” the answer is always: “Master the foundations — Linear…

The Beautiful Math Behind PCA: Part2 : Singular Value Decomposition

A complete walkthrough of SVD from scratch — and how it makes PCA faster, cleaner, and more general.

Tantangan Clustering Data Text dengan metode TF-IDF dan DBSCAN

Saat mencoba melakukan clustering berita online, saya sih cukup yakin TF-IDF dan DBSCAN adalah kombinasi yang masuk akal. Keduanya sering…

Kümeleme ve Boyut indirgeme: Denetimsiz Öğrenmenin Sezgisel Anatomisi

Kümeleme ve Boyut İndirgeme Nedir?

SVD for Image Compression — a hands-on walk-through

Today we leave pure theory and apply linear algebra in code: SVD for image compression.

Decoding Dimensionality Reduction: PCA, UMAP, and t-SNE in Bioinformatics

A Deep Dive into Visualising Complex Biological Data

Week 14: Visualizing Autism Data with PCA and t-SNE

So far, we’ve talked about numbers, correlations, and accuracy scores. But sometimes the most powerful insight comes from seeing the data…

Understanding Dimensionality Reduction and Feature Selection in Machine Learning

In the age of big data and high-throughput analytics, modern machine learning pipelines often deal with datasets containing thousands of…

Data Science in Finance: Dimension Reduction

Tentang Materi

K-Nearest-Rank Scores: A Tool for Local Structure Analysis in Embeddings

When using a low-dimensional projection, we should always be aware that it is merely a projection of a higher and more complex dimensional…

Classifying Fighter Types with K-Means Clustering

In the UFC we often see different types of fighters, where some are mainly strikers, some are mainly grapplers, and some are a hybrid of…

Understanding Dimensionality Reduction

Discover the importance of dimensionality reduction, its techniques, and how to apply them to image datasets while visualizing and…

Dimensionality Reduction Techniques

化學資訊學基礎：簡單介紹什麼是化學空間

化學空間（Chemical Space）是一個假想的”空間”概念，其中放置著所有可能存在的分子，任一特定分子在化學空間中都有一個特定的位置，就像一張巨大的地圖上畫上了所有的城市。

Navigating High-Dimensional Spaces: An In-Depth Exploration of PCA and t-SNE for Dimensionality…

Introduction

Part16: Unsupervised Machine Learning: Introduction to Unsupervised Learning and K Means

Overview of Unsupervised Learning

Distribution adjustment in polygenic scores

We will adjust a polygenic score (PGS) distribution for genetic ancestry, letting us calculated ancestry-normalized polygenic scores for…

The Power of Randomized SVD

Randomized SVD is a method for reducing the computational cost of singular value decomposition.