Experiment 7(1)

from sklearn.datasets import load_breast_cancer
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
data = load_breast_cancer()
df = pd.DataFrame(data = data.data, columns = data.feature_names)
print(df.head())

#NEXT CELL
standardized = StandardScaler()
standardized.fit(df)
StandardScaler(copy = True, with_mean = True, with_std = True)
scaled_data = standardized.transform(df)
print(scaled_data)

#NEXT CELL
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import seaborn as sns
pca = PCA(n_components = 2)
pca.fit(scaled_data)
x_pca = pca.transform(scaled_data)
%matplotlib inline
fig = plt.figure(figsize = (8,8))
ax = fig.add_subplot(1,1,1)
ax.set_xlabel('Principal Component 1', fontsize = 15)
ax.set_ylabel('Principal Component 2', fontsize = 15)
ax.set_title("2 Component PCA", fontsize = 15)
ax.scatter(x_pca[:,0], x_pca[:,1])