Update clustering.py

clustering.py

@@ -1,43 +1,47 @@
-from sklearn.cluster import KMeans
-from sklearn.metrics import silhouette_score
-from sklearn.preprocessing import StandardScaler
-import streamlit as st
-import matplotlib.pyplot as plt
-import seaborn as sns
-import pandas as pd
-from sklearn.decomposition import PCA
-
-
-def perform_clustering(df, n_clusters):
-    df = df.dropna()
-
-    scaler = StandardScaler()
-    df_value_scaled = scaler.fit_transform(df)
-
-    # Apply KMeans with the selected number of clusters
-    model = KMeans(n_clusters=n_clusters, random_state=42)
-    model.fit(df_value_scaled)
-    labels = model.predict(df_value_scaled)
-    score = silhouette_score(df_value_scaled, labels)
-
-    df['Cluster'] = labels
-    return df, score, df_value_scaled, labels, model
-
-def plot_clusters(df_value_scaled, labels, new_data_point=None):
-    pca = PCA(n_components=2)
-    components = pca.fit_transform(df_value_scaled)
-    df_components = pd.DataFrame(data=components, columns=['PC1', 'PC2'])
-    df_components['Cluster'] = labels
-
-    plt.figure(figsize=(10, 6))
-    sns.scatterplot(x='PC1', y='PC2', hue='Cluster', data=df_components, palette='viridis', s=100, alpha=0.7)
-
-    # Plot new data point if provided
-    if new_data_point is not None:
-        plt.scatter(new_data_point[:, 0], new_data_point[:, 1], color='red', marker='o', s=100, label='New Data Point')
-
-    plt.title('Cluster Visualization')
-    plt.xlabel('Principal Component 1')
-    plt.ylabel('Principal Component 2')
-    plt.legend(title='Cluster')
-    st.pyplot(plt)
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_score
+from sklearn.preprocessing import StandardScaler
+import streamlit as st
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+from sklearn.decomposition import PCA
+
+def summarize_cluster_characteristics(clustered_data, labels, cluster_number):
+    cluster_data = clustered_data[labels == cluster_number]
+    summary = cluster_data.mean().to_dict()
+    return summary
+
+def perform_clustering(df, n_clusters):
+    df = df.dropna()
+
+    scaler = StandardScaler()
+    df_value_scaled = scaler.fit_transform(df)
+
+    # Apply KMeans with the selected number of clusters
+    model = KMeans(n_clusters=n_clusters, random_state=42)
+    model.fit(df_value_scaled)
+    labels = model.predict(df_value_scaled)
+    score = silhouette_score(df_value_scaled, labels)
+
+    df['Cluster'] = labels
+    return df, score, df_value_scaled, labels, model
+
+def plot_clusters(df_value_scaled, labels, new_data_point=None):
+    pca = PCA(n_components=2)
+    components = pca.fit_transform(df_value_scaled)
+    df_components = pd.DataFrame(data=components, columns=['PC1', 'PC2'])
+    df_components['Cluster'] = labels
+
+    plt.figure(figsize=(10, 6))
+    sns.scatterplot(x='PC1', y='PC2', hue='Cluster', data=df_components, palette='viridis', s=100, alpha=0.7)
+
+    # Plot new data point if provided
+    if new_data_point is not None:
+        plt.scatter(new_data_point[:, 0], new_data_point[:, 1], color='red', marker='o', s=100, label='New Data Point')
+
+    plt.title('Cluster Visualization')
+    plt.xlabel('Principal Component 1')
+    plt.ylabel('Principal Component 2')
+    plt.legend(title='Cluster')
+    st.pyplot(plt)