Initial Commit

pages/📈 Analytics Dashboard.py

@@ -48,6 +48,7 @@ def analytics_dashboard_page():
         }).round(3)
         hourly_stats.columns = ['Total_Transactions', 'Fraud_Count', 'Fraud_Rate']
         hourly_stats = hourly_stats.reset_index()
+        st.write("hourly_stats", hourly_stats)  # Debug output
         fig = px.line(
             hourly_stats, 
             x='Hour', 
@@ -71,6 +72,7 @@ def analytics_dashboard_page():
     st.markdown("### 💵 Transaction Amount Analysis")
     col1, col2 = st.columns(2)
     with col1:
+        st.write("transactions", transactions)  # Debug output
         fig = px.histogram(
             transactions, 
             x='Amount', 
@@ -80,7 +82,6 @@ def analytics_dashboard_page():
             labels={'Is_Fraud': 'Fraud Status'},
             marginal="box"
         )
-        fig.update_layout(xaxis_range=[0, 2000])
         st.plotly_chart(fig, use_container_width=True)
     with col2:
         fig = px.box(
@@ -90,13 +91,13 @@ def analytics_dashboard_page():
             title="Amount Distribution: Normal vs Fraud",
             labels={'Is_Fraud': 'Fraud Status', 'Amount': 'Transaction Amount (₹)'}
         )
-        fig.update_layout(yaxis_range=[0, 1000])
         st.plotly_chart(fig, use_container_width=True)
     st.markdown("### 👥 Customer Demographics & Fraud Risk")
     age_bins = pd.cut(transactions['Customer_Age'], bins=6, precision=0)
     age_stats = transactions.groupby(age_bins)['Is_Fraud'].agg(['count', 'sum', 'mean']).reset_index()
     age_stats.columns = ['Age_Group', 'Total', 'Fraud_Count', 'Fraud_Rate']
-    age_stats['Age_Group'] = age_stats['Age_Group'].astype(str)
+    age_stats['Age_Group'] = age_stats['Age_Group'].astype(str)  # Fix Interval serialization
+    st.write("age_stats", age_stats)  # Debug output
     fig = px.bar(
         age_stats, 
         x='Age_Group',