MI Research Cluster Analysis.py

import pandas as pd
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
from itertools import combinations
from sklearn.metrics import silhouette_score

# Load survey data into a DataFrame (replace 'IceCool_MI.csv' with your dataset)
survey_data = pd.read_csv('IceCool_MI.csv')

# Select relevant features for clustering
selected_features = survey_data[['brand_switching', 'price_sense', 'brand_aware', 'packaging', 'taste']]

# Standardize the data (scaling to mean=0 and variance=1)
scaler = StandardScaler()
scaled_data = scaler.fit_transform(selected_features)

# Determine the number of clusters (you can use various methods like the Elbow method)
num_clusters = 3  # Adjust as needed

# Create combinations of variables for scatter plots
variables = selected_features.columns
combinations = list(combinations(variables, 2))

# Initialize a list to store silhouette scores
silhouette_scores = []

# Loop through combinations of variables
for var1, var2 in combinations:
    # Perform K-Means clustering
    kmeans = KMeans(n_clusters=num_clusters, random_state=42)
    cluster_labels = kmeans.fit_predict(scaled_data)

    # Calculate silhouette score
    silhouette = silhouette_score(scaled_data, cluster_labels)
    silhouette_scores.append(silhouette)

    # Create a scatter plot
    plt.figure(figsize=(10, 8))
    cluster_colors = sns.color_palette("hsv", n_colors=num_clusters)
    scatter = sns.scatterplot(data=survey_data, x=var1, y=var2, hue=cluster_labels, palette=cluster_colors, s=100, alpha=0.7)

    # Set plot labels and title
    plt.xlabel(var1)
    plt.ylabel(var2)
    plt.title(f'Survey Data Clusters: {var1} vs. {var2}\nSilhouette Score: {silhouette:.2f}')

    # Customize the legend
    legend = plt.gca().get_legend()
    legend.set_title('Cluster')
    legend.get_title().set_fontsize('12')

    # Display the plot
    plt.show()

# Print silhouette scores
for i, (var1, var2) in enumerate(combinations):
    print(f'Silhouette Score for {var1} vs. {var2}: {silhouette_scores[i]:.2f}')