K means clustering for election campaigns

k-means-3-var-3-cluster.py

# Performing K-Means to find 3 clusters using the three variables
kmeans = KMeans(n_clusters=3, random_state=0).fit(df[["Age", "Income", "Debt"]])

k-means-elbow.py

errors = [] # Create an empty list to collect inertias

# Loop through some desirable number of clusters
# The KMeans algorithm's fit method returns a property called inertia_ that has the information we need
for k in range(2, 10):
    errors.append(
        {
            "inertia": KMeans(n_clusters=k, random_state=0)
            .fit(df[["Age", "Income", "Debt"]])
            .inertia_,
            "num_clusters": k,
        }
    )

# for convenience convert the list to a pandas dataframe
df_inertia = pd.DataFrame(errors)

# Create a line plot of inertia against number of clusters
sns.lineplot(
    x=df_inertia.num_clusters,
    y=df_inertia.inertia,
)

k-means-election-campaign.py

# Imports
import pandas as pd # Pandas for reading data
from sklearn.cluster import KMeans # KMeans Clustering itself

# Read dataset from file using pandas
df = pd.read_csv("./voters_demo_sample.csv")

# perform K-Means clustering to find 2 clusters considering only age and income of voters
kmeans = KMeans(n_clusters=2, random_state=0).fit(df[["Age", "Income"]])

# See the cluster label for each data point / Group label of each voter
kmeans.labels_

# Identifying the (final) cluster centroids
kmeans.cluster_centers_

#Output
# array([[56.05275779, 39.92805755],
#        [30.55364807, 22.2360515 ]])

k-means-visualization-3-clusters.py

# Performing K-Means to find 3 clusters using the same two variables
kmeans = KMeans(n_clusters=3, random_state=0).fit(df[["Age", "Income"]])

ax = sns.scatterplot(
    x=df.Age,
    y=df.Income,
    hue=kmeans.labels_,
    palette=sns.color_palette("colorblind", n_colors=3),
    legend=None,
)

k-means-visualization.py

# Imports for visualiation
import seaborn as sns

# Create scatterplot
ax = sns.scatterplot(
    x=df.Age,
    y=df.Income,
    hue=kmeans.labels_,
    palette=sns.color_palette("colorblind", n_colors=2),
    legend=None,
)