1 -K-means Algorithm

from sklearn import cluster
import pandas as pd
import numpy as np

dataset = pd.DataFrame({
    'x': [11, 11, 20, 12, 16, 33, 24, 14, 45, 52, 51, 52, 55, 53, 55, 61, 62, 70, 72, 10],
    'y': [39, 36, 30, 52, 53, 46, 55, 59, 12, 15, 16, 18, 11, 23, 14, 8, 18, 7, 24, 70]
})

myKmeans = cluster.KMeans(n_clusters=2)
myKmeans.fit(dataset)
labels = myKmeans.labels_
centers = myKmeans.cluster_centers_

print(labels)
print(centers)
import matplotlib.pyplot as plt
plt.scatter(dataset['x'],dataset['y'], s=10)
plt.scatter(centers[0],centers[1],s=100)
plt.show()

2 Hierarchical Algorithm

from sklearn.cluster import AgglomerativeClustering
import pandas as pd
import numpy as np

dataset = pd.DataFrame({
    'x': [11, 11, 20, 12, 16, 33, 24, 14, 45, 52, 51, 52, 55, 53, 55, 61, 62, 70, 72, 10],
    'y': [39, 36, 30, 52, 53, 46, 55, 59, 12, 15, 16, 18, 11, 23, 14, 8, 18, 7, 24, 70]
})

cluster = AgglomerativeClustering(n_clusters=2, affinity='euclidean', linkage='ward')  
cluster.fit_predict(dataset)
print(cluster.labels_)  

3 PCA

from sklearn.decomposition import PCA
iris = pd.read_csv('iris.csv')
iris
X = iris.drop('Species', axis=1)
pca = PCA(n_components=4)
pca.fit(X)
pca_df=(pd.DataFrame(pca.components_,columns=X.columns))
pca_df
print(pca.explained_variance_ratio_)
X['PC1'] = X['Sepal.Length']* pca_df['Sepal.Length'][0] + X['Sepal.Width']* pca_df['Sepal.Width'][0]+ X['Petal.Length']* pca_df['Petal.Length'][0]+X['Petal.Width']* pca_df['Petal.Width'][0]
X['PC2'] = X['Sepal.Length']* pca_df['Sepal.Length'][1] + X['Sepal.Width']* pca_df['Sepal.Width'][1]+ X['Petal.Length']* pca_df['Petal.Length'][1]+X['Petal.Width']* pca_df['Petal.Width'][1]
X['PC3'] = X['Sepal.Length']* pca_df['Sepal.Length'][2] + X['Sepal.Width']* pca_df['Sepal.Width'][2]+ X['Petal.Length']* pca_df['Petal.Length'][2]+X['Petal.Width']* pca_df['Petal.Width'][2]
X['PC4'] = X['Sepal.Length']* pca_df['Sepal.Length'][3] + X['Sepal.Width']* pca_df['Sepal.Width'][3]+ X['Petal.Length']* pca_df['Petal.Length'][3]+X['Petal.Width']* pca_df['Petal.Width'][3]
X