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Find and Visualize clusters with K-Means

This template helps you identify and visualize clusters in yourdata. The K Means algorithm aims to assign the data points in your dataset to K distinct clusters. After running the algorithm each observation (data point) will belong to the cluster whose center it is closest to.

# Load packages
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt 
from sklearn.cluster import KMeans
from sklearn.model_selection import train_test_split
sns.set_style('darkgrid')
%config InlineBackend.figure_format = 'retina'
# Upload your data as a csv file and load it as a data frame 
df = pd.read_csv('penguins.csv').dropna()
df.head()
speciesislandbill_length_mmbill_depth_mmflipper_length_mmbody_mass_gsex
0AdelieTorgersen39.118.7181.03750.0Male
1AdelieTorgersen39.517.4186.03800.0Female
2AdelieTorgersen40.318.0195.03250.0Female
4AdelieTorgersen36.719.3193.03450.0Female
5AdelieTorgersen39.320.6190.03650.0Male
# Visualize the correlation your data and identify variables for further analysis
g = sns.PairGrid(df)
g.map(sns.scatterplot);
X = np.array(df.loc[:,['bill_length_mm',                # Choose your variable names
                       'bill_depth_mm']])    \
                        .reshape(-1, 2)

# Determine optimal cluster number with elbow method
wcss = []

for i in range(1, 11):
    model = KMeans(n_clusters = i,     
                    init = 'k-means++',                 # Initialization method for kmeans
                    max_iter = 300,                     # Maximum number of iterations 
                    n_init = 10,                        # Choose how often algorithm will run with different centroid 
                    random_state = 0)                   # Choose random state for reproducibility
    model.fit(X)                              
    wcss.append(model.inertia_)
    
# Show Elbow plot
plt.plot(range(1, 11), wcss)
plt.title('Elbow Method')                               # Set plot title
plt.xlabel('Number of clusters')                        # Set x axis name
plt.ylabel('Within Cluster Sum of Squares (WCSS)')      # Set y axis name
plt.show()
kmeans = KMeans(n_clusters = 3,                 # Set amount of clusters
                init = 'k-means++',             # Initialization method for kmeans
                max_iter = 300,                 # Maximum number of iterations
                n_init = 10,                    # Choose how often algorithm will run with different centroid
                random_state = 0)               # Choose random state for reproducibility

pred_y = kmeans.fit_predict(X)


# Plot the data
plt.scatter(X[:,0], 
            X[:,1])

# Plot the clusters 
plt.scatter(kmeans.cluster_centers_[:, 0], 
            kmeans.cluster_centers_[:, 1], 
            s=200,                             # Set centroid size
            c='red')                           # Set centroid color
plt.show()
Python

Find and Visualize clusters with K-Means

Identify and visualize clusters in your data.

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