change

app.py

@@ -96,24 +96,21 @@ with tab2:
     
     # Visualizations for Item Purchased distribution
     fig, ax = plt.subplots(figsize=(10, 5))
-    sns.countplot(y=df['Item Purchased'], order=df['Item Purchased'].value_counts().index, palette='viridis', ax=ax)
+    sns.countplot(y=df['Item Purchased'], order=df['Item Purchased'].value_counts().index, hue=df['Item Purchased'], palette='viridis', ax=ax)
     ax.set_title("Overall Item Purchase Distribution")
     ax.set_xlabel("Count")
     ax.set_ylabel("Item Purchased")
     st.pyplot(fig)
     
-    # Heatmap for Matrix Visualization
-    st.subheader("Feature Correlation Matrix")
-    label_encoders = {}
-    for col in categorical_features:
-        le = LabelEncoder()
-        df[col + '_Numeric'] = le.fit_transform(df[col])
-        label_encoders[col] = le
-    
-    correlation_matrix = df[['Gender_Numeric', 'Purchase Amount (USD)', 'Previous Purchases', 'Frequency of Purchases_Numeric']].corr()
-    fig, ax = plt.subplots(figsize=(10, 6))
-    sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', ax=ax)
-    st.pyplot(fig)
+    # Separate Correlation Matrices for Each Label
+    labels = ['Gender', 'Frequency of Purchases', 'Item Purchased']
+    for label in labels:
+        df[f'{label}_Numeric'] = LabelEncoder().fit_transform(df[label])
+        correlation_matrix = df[[f'{label}_Numeric', 'Purchase Amount (USD)', 'Previous Purchases']].corr()
+        fig, ax = plt.subplots(figsize=(10, 6))
+        sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', ax=ax)
+        st.subheader(f"Feature Correlation Matrix for {label}")
+        st.pyplot(fig)
 
 with tab3:
     st.subheader("Enter Customer Details")
@@ -149,4 +146,4 @@ with tab3:
         # Display average purchase amount trend
         avg_purchase_amount = cluster_data.groupby('Item Purchased')['Purchase Amount (USD)'].mean()
         st.write("### Average Purchase Amount for Items in This Cluster:")
-        st.write(avg_purchase_amount)
+        st.write(avg_purchase_amount)