L'analyse des composantes principales essaie de trouver les axes principaux qui sont des variables décorrélées qui décrivent au mieux nos données.

Applies Principal Component Analysis (PCA) to dimensionality reduction using Python, SQL, and GBQ.

Principle Component Analysis

Machine Learning- Unsupervised Learning(PCA)

Used Principal Component Analysis on Iris Dataset and reduced it from 4-features to 3-features and captured 93% of variance

Data prepration and preprocessing for predictive modeling with SAS and Python

MITx - MicroMasters Program on Statistics and Data Science - Machine Learning with Python - Second Project

Predictive Model for BRENT price movements

Use unsupervised machine learning techniques to analyze cryptocurrency data

In this project, we use differents methods to transform our dataset (usually dimension modification) before making prediction thanks to machine learning and regressions.

Implimenting PCA using numpy and comparing the results

In this project, I will be implementing Principal Component Analysis (PCA) from scratch on an ecological footprint consummation database for countries and a three-dimensional scale using a movie database. The goal of this project is to gain a deeper understanding of PCA and to demonstrate its capabilities in exploring complex datasets.

Adult Census Income

NUS Pattern Recognition module graded assignments

Cluster population demographics to find a companies target customer base

The database was created with records of absenteeism at work from July 2007 to July 2010 at a courier company in Brazil. The objective here is to predict for each new individual, whether he is going to be absent for more than 3 hours or no (3 hours is the median for the absenteeism hours).

Principal Component Analysis (PCA) is a powerful dimensionality reduction technique used in data analysis and machine learning. 🌟 It transforms a dataset into a set of linearly uncorrelated variables called principal components, which capture the most variance in the data. 📉

PCA in c