## What is it? How does it work?

Principal Component Analysis (PCA) is a statistical method for reducing the dimension of a dataset.

It is a popular method for analysing a large dataset, increasing the interpretability of the data without losing much information.

It does that by maximizing the percentage of total variance explained with new uncorrelated variables, the principal components, which are linear combinations of the initial variables.

They are the eigenvectors of the covariance matrix with the highest eigenvalues.

## A first simple illustrative example

Let’s start with a first simple example.

We consider a dataset composed of 18 elements, circles with close size, and different colors.

If we remove the circles from the box and we reorder them a bit, we identify some common common characteristics.

- First of all, they are all circles, with quite similar sizes.
- 10 out of 18 are dark, the 8 others are light.
- 10 out of 18 contains some red, while 8 contains some blue.
- 7 out of 18 contains some yellow.

Circles have different sizes, but it is a second order characteristic compared to the one listed above.

So with the four following principal components, we are likely to explain most of the information in our dataset:

- Average sized circle
- Light vs Dark
- Red vs Blue
- Red & Plus vs Yellow

With a dimension of 4 instead of 18, we were able to explain most of the variance in our universe.

## Computing the principal components

The principal components are new, uncorrelated variables, linear combinaisons of the initial variables, which maximize the total variance explained.

It can be shown that the first principal axes are the eigenvectors of the covariance matrix with the highest eigenvalues.

## 2D dataset

Let’s consider a 2 dimensional dataset. The scatter plot below shows the relationship between the two variables.

The first principal component is the direction of greatest variation.

It is formed by minimizing the mean-squared distance between all variables and their projections.

The *mean squared* distance tells you how much variance the first principal component does not explain, it is explained by the second one, in the orthogonal direction.

## Principal component analysis vs linear regression

The ordinary least squares regression line is formed by minimizing the mean-squared error in the y-direction.

It is in general different from the first principal component which is formed by minimizing the mean-squared distance between all variables and their projections. It it the (orthogonal) total least square line.

## Bivariate normal distribution

We consider two correlated gaussian variables X1 and X2, with zero mean, 0.2 and 0.1 standard déviations respectively, and a correlation of 0.8.

We simulate below 1000 data points and plot below the scatterplot with the 1, 2, 3 standard deviations. The 3 standard deviations ellipse encloses 98.9% of the data points.

The first eigenvector, with the highest eigenvalue points in the direction of the first axis of the ellipses with the longest axis while the second eigenvector points in the direction of the sortes axis.

In this example, 94% of the variance is explained by the first principal component.

Below is the Python code used to for the simulations:

```
#import libraries:
from matplotlib.patches import Ellipse
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
from sklearn.decomposition import PCA
#simulation bivariate normal variables
sd1 = 0.2
sd2 = 0.1
corr = 0.8
correl = np.array([[1.0,corr],[corr,1.0]])
diag_sd = np.array([[sd1,0.0],[0.0,sd2]])
covar = diag_sd.dot(correl.dot(diag_sd))
X = np.random.multivariate_normal([0,0],covar,1000)
#Principal Component Analysis
#fit
pca = PCA(n_components = 2)
pca.fit(X)
#Explained variance (eigenvalues)
var_explained = pca.explained_variance_
print("Explained Variance:")
print(var_explained)
#PCA components (eigenvectors)
print("PCA Components:")
pca_components = pca.components_
print(pca_components)
#Plot
ax = plt.subplot(111, aspect = 'equal')
x = X[:, 0]
y = X[:, 1]
#Ellipse
for j in range(1, 4):
ellipse = Ellipse(xy=(np.mean(x), np.mean(y)),
width = np.sqrt(var_explained[0]) * j * 2, height = np.sqrt(var_explained[1]) * j * 2,
angle = np.rad2deg(np.arccos(abs(pca_components[0, 0]))),color = 'lightcoral')
ellipse.set_facecolor('none')
ax.add_artist(ellipse)
plt.rcParams["figure.figsize"] = (10,5)
#Scatter plot
plt.scatter(x, y, alpha = 0.7, color = 'dodgerblue')
plt.xlabel('X1')
plt.ylabel('X2')
#arrows for principal components
for i in range(0, 2):
var_explained = pca.explained_variance_[i]
pca_components = pca.components_[i]
pca_components_len = pca_components * 3 * np.sqrt(var_explained)
arrowprops = dict(arrowstyle = '->', linewidth = 4, color = 'limegreen')
ax.annotate('', pca.mean_ + pca_components_len , pca.mean_ - pca_components_len, arrowprops = arrowprops)
plt.axis('equal');
plt.show()
```

## To Go Further

Principal Component Analysis in Finance

**Save 10%** on All **Quant Next Courses** with the Coupon Code: **QuantNextBlog10**

**For students and graduates: We offer a 50% discount on all courses, please contact us if you are interested: contact@quant-next.com**