# importing required libraries

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

from sklearn.decomposition import PCA

from sklearn.preprocessing import StandardScaler

#import the breast _cancer dataset

from sklearn.datasets import load_breast_cancer

data=load_breast_cancer()

data.keys()

 

# Check the output classes

print(data['target_names'])

 

# Check the input attributes

print(data['feature_names'])

# construct a dataframe using pandas

df1=pd.DataFrame(data['data'],columns=data['feature_names'])

 

# Scale data before applying PCA

scaling=StandardScaler()

 

# Use fit and transform method

scaling.fit(df1)

Scaled_data=scaling.transform(df1)

 

# Set the n_components=3

principal=PCA(n_components=3)

principal.fit(Scaled_data)

x=principal.transform(Scaled_data)

 

# Check the dimensions of data after PCA

print(x.shape)

# Check the values of eigen vectors

# prodeced by principal components

principal.components_

plt.figure(figsize=(10,10))

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

plt.xlabel('pc1')

plt.ylabel('pc2')

# import relevant libraries for 3d graph

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure(figsize=(10,10))

 

# choose projection 3d for creating a 3d graph

axis = fig.add_subplot(111, projection='3d')

 

# x[:,0]is pc1,x[:,1] is pc2 while x[:,2] is pc3

axis.scatter(x[:,0],x[:,1],x[:,2], c=data['target'],cmap='plasma')

axis.set_xlabel("PC1", fontsize=10)

axis.set_ylabel("PC2", fontsize=10)

axis.set_zlabel("PC3", fontsize=10)

# check how much variance is explained by each principal component

print(principal.explained_variance_ratio_)