Gaussian Mixture Models Implementation

building_kmeans.py

#training k-means model
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=4)
kmeans.fit(data)

#predictions from kmeans
pred = kmeans.predict(data)
frame = pd.DataFrame(data)
frame['cluster'] = pred
frame.columns = ['Weight', 'Height', 'cluster']

#plotting results
color=['blue','green','cyan', 'black']
for k in range(0,4):
    data = frame[frame["cluster"]==k]
    plt.scatter(data["Weight"],data["Height"],c=color[k])
plt.show()

data_distribution.py

import pandas as pd
data = pd.read_csv('Clustering_gmm.csv')

plt.figure(figsize=(7,7))
plt.scatter(data["Weight"],data["Height"])
plt.xlabel('Weight')
plt.ylabel('Height')
plt.title('Data Distribution')
plt.show()

gaussian_mixture_model.py

import pandas as pd
data = pd.read_csv('Clustering_gmm.csv')

# training gaussian mixture model 
from sklearn.mixture import GaussianMixture
gmm = GaussianMixture(n_components=4)
gmm.fit(data)

#predictions from gmm
labels = gmm.predict(data)
frame = pd.DataFrame(data)
frame['cluster'] = labels
frame.columns = ['Weight', 'Height', 'cluster']

color=['blue','green','cyan', 'black']
for k in range(0,4):
    data = frame[frame["cluster"]==k]
    plt.scatter(data["Weight"],data["Height"],c=color[k])
plt.show()