Update pages/3_EDA_and_Feature_Engineering.py

pages/3_EDA_and_Feature_Engineering.py

@@ -30,7 +30,7 @@ if 'df' in st.session_state:
     ax.set_xlabel("Product Category")
     ax.set_ylabel("Count")
     ax.tick_params(axis='x', rotation=45))
-    st.pyplot(fig)
+    st.plt(fig)
     st.markdown("""
     **Insights:**
     - 5 product categories observed.
@@ -45,7 +45,7 @@ if 'df' in st.session_state:
     ax.set_xlabel("Product Brand")
     ax.set_ylabel("Count")
     ax.tick_params(axis='x', rotation=45)
-    st.pyplot(fig)
+    st.plt(fig)
     st.markdown("""
     **Insights:**
     - Samsung and HP have the highest frequencies.
@@ -58,7 +58,7 @@ if 'df' in st.session_state:
     ax.set_title("Product Price Distribution")
     ax.set_xlabel("Product Price")
     ax.set_ylabel("Count")
-    st.pyplot(fig)
+    st.plt(fig)
     st.markdown("""
     **Insights:**
     - Products span a wide price range, from near 0 to 3000.
@@ -73,7 +73,7 @@ if 'df' in st.session_state:
     ax.set_xlabel("Product Price Bucket")
     ax.set_ylabel("Count")
     ax.tick_params(axis='x', rotation=45)
-    st.pyplot(fig)
+    st.plt(fig)
     st.markdown("""
     **Insights:**
     - "Very High" price bucket has the highest concentration.
@@ -94,7 +94,7 @@ if 'df' in st.session_state:
     axs[1].set_xlabel("Customer Age")
 
     plt.tight_layout()
-    st.pyplot(fig)
+    st.plt(fig)
     st.markdown("""
     **Insights:**
     - Age groups are relatively evenly distributed.
@@ -110,7 +110,7 @@ if 'df' in st.session_state:
                                              wedgeprops={'edgecolor': 'black'},
                                              ax=ax)
     ax.set_title("Customer Gender Distribution")
-    st.pyplot(fig)
+    st.plt(fig)
     st.markdown("""
     **Insights:**
     - Gender distribution is almost equal.
@@ -123,7 +123,7 @@ if 'df' in st.session_state:
     ax.set_title("Purchase Frequency Distribution")
     ax.set_xlabel("Purchase Frequency")
     ax.set_ylabel("Count")
-    st.pyplot(fig)
+    st.plt(fig)
     st.markdown("""
     **Insights:**
     - Purchase frequencies range from 1 to 19.
@@ -135,7 +135,7 @@ if 'df' in st.session_state:
     ax.set_title("Customer Satisfaction Distribution")
     ax.set_xlabel("Customer Satisfaction")
     ax.set_ylabel("Count")
-    st.pyplot(fig)
+    st.plt(fig)
     st.markdown("""
     **Insights:**
     - Distinct peaks around integer values (1-5).
@@ -152,7 +152,7 @@ if 'df' in st.session_state:
            startangle=90,
            wedgeprops={'edgecolor': 'black'})
     ax.set_title("Purchase Intent Distribution")
-    st.pyplot(fig)
+    st.plt(fig)
     st.markdown("""
     **Insights:**
     - Binary classification problem (0: Not Purchase, 1: Purchase).
@@ -169,12 +169,9 @@ if 'df' in st.session_state:
     fig, ax = plt.subplots(figsize=(12, 8))
     sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5, ax=ax)
     ax.set_title("Correlation Heatmap")
-    st.pyplot(fig)
+    st.plt(fig)
     st.markdown("""
     **Insights:**
     - Strong correlations can be observed between certain variables.
     - Customer Satisfaction and Purchase Intent might have meaningful relationships.
     """)
-
-else:
-    st.write("Please upload your data.")