ML | Principal Component Analysis(PCA)

Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation that converts a set of correlated variables to a set of uncorrelated variables. PCA is the most widely used tool in exploratory data analysis and in machine learning for predictive models. Moreover, PCA is an unsupervised statistical technique used to examine the interrelations among a set of variables. It is also known as a general factor analysis where regression determines a line of best fit.

Module Needed:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Code #1:

# Here we are using inbuilt dataset of scikit learn
from sklearn.datasets import load_breast_cancer

# instantiating
cancer = load_breast_cancer()

# creating dataframe
df = pd.DataFrame(cancer['data'], columns = cancer['feature_names'])

# checking head of dataframe
df.head()

Output:

Code #2:

# Importing standardscalar module
from sklearn.preprocessing import StandardScaler

scalar = StandardScaler()

# fitting
scalar.fit(df)
scaled_data = scalar.transform(df)

# Importing PCA
from sklearn.decomposition import PCA

# Let's say, components = 2
pca = PCA(n_components = 2)
pca.fit(scaled_data)
x_pca = pca.transform(scaled_data)

x_pca.shape

Output:

569, 2

# giving a larger plot
plt.figure(figsize =(8, 6))

plt.scatter(x_pca[:, 0], x_pca[:, 1], c = cancer['target'], cmap ='plasma')

# labeling x and y axes
plt.xlabel('First Principal Component')
plt.ylabel('Second Principal Component')

Output:

# components
pca.components_

Output:

df_comp = pd.DataFrame(pca.components_, columns = cancer['feature_names'])

plt.figure(figsize =(14, 6))

# plotting heatmap
sns.heatmap(df_comp)

Output: