Python for Data Science

Chapter 4 - Clustering Models

Segment 1 - K-means method

Clustering and Classification Algorithms

K-Means clustering: unsupervised clustering algorithm where you know how many clusters are appropriate

K-Means Use Cases

• Market Price and Cost Modeling
• Insurance Claim Fraud Detection
• Hedge Fund Classification
• Customer Segmentation

K-Means Clustering

Predictions are based on the number of centroids present(K) and nearest mean values, given an Euclidean distance measurement between observations.

When using K-means:

• Scale your variables
• Look at a scatterplot or the data table to estimate the appropriate number of centroids to use for the K parameter value

Setting up for clustering analysis

import numpy as np
import pandas as pd

import matplotlib.pyplot as plt

import sklearn
from sklearn.preprocessing import scale
import sklearn.metrics as sm
from sklearn.metrics import confusion_matrix, classification_report

from sklearn.cluster import KMeans
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets

%matplotlib inline
plt.figure(figsize=(7,4))

<Figure size 504x288 with 0 Axes>




<Figure size 504x288 with 0 Axes>

iris = datasets.load_iris()

X = scale(iris.data)
y = pd.DataFrame(iris.target)
varibale_names = iris.feature_names
X[0:10]

array([[-0.90068117,  1.01900435, -1.34022653, -1.3154443 ],
       [-1.14301691, -0.13197948, -1.34022653, -1.3154443 ],
       [-1.38535265,  0.32841405, -1.39706395, -1.3154443 ],
       [-1.50652052,  0.09821729, -1.2833891 , -1.3154443 ],
       [-1.02184904,  1.24920112, -1.34022653, -1.3154443 ],
       [-0.53717756,  1.93979142, -1.16971425, -1.05217993],
       [-1.50652052,  0.78880759, -1.34022653, -1.18381211],
       [-1.02184904,  0.78880759, -1.2833891 , -1.3154443 ],
       [-1.74885626, -0.36217625, -1.34022653, -1.3154443 ],
       [-1.14301691,  0.09821729, -1.2833891 , -1.44707648]])

Building and running your model

clustering = KMeans(n_clusters=3, random_state=5)

clustering.fit(X)

KMeans(n_clusters=3, random_state=5)

Plotting your model outputs

iris_df = pd.DataFrame(iris.data)
iris_df.columns = ['Sepal_Length','Sepal_Width','Petal_Length','Petal_Width']
y.columns = ['Targets']

color_theme = np.array(['darkgray','lightsalmon','powderblue'])

plt.subplot(1,2,1)

plt.scatter(x=iris_df.Petal_Length, y=iris_df.Petal_Width, c=color_theme[iris.target],s=50)
plt.title('Ground Truth Classfication')

plt.subplot(1,2,2)

plt.scatter(x=iris_df.Petal_Length, y=iris_df.Petal_Width, c=color_theme[clustering.labels_],s=50)
plt.title('K-Means Classfication')

Text(0.5, 1.0, 'K-Means Classfication')

relabel = np.choose(clustering.labels_, [2, 0, 1]).astype(np.int64)

plt.subplot(1,2,1)

plt.scatter(x=iris_df.Petal_Length, y=iris_df.Petal_Width, c=color_theme[iris.target],s=50)
plt.title('Ground Truth Classfication')

plt.subplot(1,2,2)

plt.scatter(x=iris_df.Petal_Length, y=iris_df.Petal_Width, c=color_theme[relabel],s=50)
plt.title('K-Means Classfication')

Text(0.5, 1.0, 'K-Means Classfication')

Evaluate your clustering results

print(classification_report(y, relabel))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        50
           1       0.74      0.78      0.76        50
           2       0.77      0.72      0.74        50

    accuracy                           0.83       150
   macro avg       0.83      0.83      0.83       150
weighted avg       0.83      0.83      0.83       150