Clustering Challenge¶
Clustering is an unsupervised machine learning technique in which you train a model to group similar entities into clusters based on their features.
In this exercise, you must separate a dataset consisting of three numeric features (A, B, and C) into clusters. Run the cell below to load the data.
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import pandas as pd
data = pd.read_csv('data/clusters.csv')
data.head()
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Your challenge is to identify the number of discrete clusters present in the data, and create a clustering model that separates the data into that number of clusters. You should also visualize the clusters to evaluate the level of separation achieved by your model.
Add markdown and code cells as required to create your solution.
Reducing the Dimension of the Data with PCA¶
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from sklearn.preprocessing import MinMaxScaler
from sklearn.decomposition import PCA
# Normalize the numeric features so they're on the same scale
scaled_features = MinMaxScaler().fit_transform(data[data.columns[0:2]])
# Get two principal components
pca = PCA(n_components=2).fit(scaled_features)
features_2d = pca.transform(scaled_features)
features_2d[0:10]
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Plot the Data in a 2D View¶
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import matplotlib.pyplot as plt
%matplotlib inline
plt.scatter(features_2d[:,0],features_2d[:,1])
plt.xlabel('Dimension 1')
plt.ylabel('Dimension 2')
plt.title('Data')
plt.show()
Clustering with K-means - Choosing the Number of Clusters Based on WCSS¶
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#importing the libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
%matplotlib inline
# Create 10 models with 1 to 10 clusters
wcss = []
for i in range(1, 11):
kmeans = KMeans(n_clusters = i)
# Fit the data points
kmeans.fit(data.values)
# Get the WCSS (inertia) value
wcss.append(kmeans.inertia_)
#Plot the WCSS values onto a line graph
plt.plot(range(1, 11), wcss)
plt.title('WCSS by Clusters')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.show()
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from sklearn.cluster import KMeans
# Create a model based on 4 centroids
model = KMeans(n_clusters=4, init='k-means++', n_init=100, max_iter=1000)
# Fit to the data and predict the cluster assignments for each data point
km_clusters = model.fit_predict(data.values)
# View the cluster assignments
km_clusters
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Clustering Result with K-means¶
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def plot_clusters(samples, clusters):
col_dic = {0:'blue',1:'green',2:'orange', 3: 'purple'}
mrk_dic = {0:'*',1:'x',2:'+', 3: '.'}
colors = [col_dic[x] for x in clusters]
markers = [mrk_dic[x] for x in clusters]
for sample in range(len(clusters)):
plt.scatter(samples[sample][0], samples[sample][1], color = colors[sample], marker=markers[sample], s=100)
plt.xlabel('Dimension 1')
plt.ylabel('Dimension 2')
plt.title('Assignments')
plt.show()
plot_clusters(features_2d, km_clusters)
Agglomerative Clustering¶
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from sklearn.cluster import AgglomerativeClustering
agg_model = AgglomerativeClustering(n_clusters=4)
agg_clusters = agg_model.fit_predict(data.values)
agg_clusters
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Visualizing it¶
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import matplotlib.pyplot as plt
%matplotlib inline
def plot_clusters(samples, clusters):
col_dic = {0:'blue',1:'green',2:'orange', 3:'purple'}
mrk_dic = {0:'*',1:'x',2:'+', 3: '.'}
colors = [col_dic[x] for x in clusters]
markers = [mrk_dic[x] for x in clusters]
for sample in range(len(clusters)):
plt.scatter(samples[sample][0], samples[sample][1], color = colors[sample], marker=markers[sample], s=100)
plt.xlabel('Dimension 1')
plt.ylabel('Dimension 2')
plt.title('Assignments')
plt.show()
plot_clusters(features_2d, agg_clusters)
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