Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation which converts a set of correlated variables to a set of uncorrelated variables. PCA is a 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:
# Reduced to 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:
This article is attributed to GeeksforGeeks.org
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