Statistics 

In the last year of my bachelor i choose to purshase a course of exploratory analysis of multivariate data ie multivariate statistics, during this course we focus on the study of Principal Component Analysis (PCA) and Correspondence Factor Analysis (CFA) 

Here are some code and template using PCA 

1. Code1

2. Code2

In the majority of the problems encountered, characteristics or variables X = (X1, . . . , Xp) called explanatory or predictive variables have been observed on a set of n objects, individuals or statistical units.

A first work, often tedious but unavoidable, consists in carrying out a statistical exploration of these data: shape of the distributions, presence of atypical data, correlations and cohesion, possible transformations of the data, multidimensional description, dimension reduction, classification. This is where PCA and other powerful statistical analysis methods are used!

The second part describes the tools of statistical modeling or still of learning usable for the modeling has end of prediction of a target variable Y by the explanatory variables Xj . The enchaınement, possibly iterative, of these steps (exploration then learning) constitutes the foundation of data mining.

Here the course of my professor thanh-mai.pham-ngoc please dont share it!