Update app.py

app.py

@@ -92,19 +92,20 @@ with tab2:
 
     st.subheader("📄 View Dataset Preview")
     if st.button("🔍 Show Dataset Head"):
-        st.dataframe(df.head())
+        st.dataframe(df.head())  # Displays the first few rows to understand the data format and structure
 
     st.subheader("⚠️ Fault Distribution")
     fault_counts = df['Fault'].value_counts()
-    st.bar_chart(fault_counts)
-    st.write(df['Fault'].value_counts(normalize=True) * 100)
+    st.bar_chart(fault_counts)  # Insight: Helps us understand class imbalance. If faults are rare, classification might need balancing.
+    st.write(df['Fault'].value_counts(normalize=True) * 100)  # Insight: Shows percentage distribution of Fault vs Normal.
 
     st.subheader("📊 Correlation Heatmap")
     corr = df.corr()
     fig, ax = plt.subplots(figsize=(10, 8))
     sns.heatmap(corr, annot=True, fmt=".2f", cmap="coolwarm", ax=ax)
     st.pyplot(fig)
-    
+    # Insight: Shows correlation between features. Helps identify multicollinearity (e.g., front and rear wheel speeds may be strongly correlated).
+
     st.markdown("### 📉 Feature Distributions by Fault")
     features = ['Brake_Pressure', 'Pad_Wear_Level', 'Wheel_Speed_FL', 'Wheel_Speed_FR',
                 'Wheel_Speed_RL', 'Wheel_Speed_RR', 'Fluid_Temperature', 'Pedal_Position']
@@ -114,6 +115,8 @@ with tab2:
         fig, ax = plt.subplots()
         sns.kdeplot(data=df, x=feature, hue="Fault", fill=True, common_norm=False, alpha=0.4, ax=ax)
         st.pyplot(fig)
+        # Insight: Helps compare how each feature behaves under Fault vs Normal. 
+        # For example, if Faults have higher brake pressure, this plot will reveal it through overlapping or shifting distributions.
 
     st.markdown("### 📦 Boxplots to Compare Fault vs Normal")
     for feature in features:
@@ -121,6 +124,8 @@ with tab2:
         fig, ax = plt.subplots()
         sns.boxplot(data=df, x='Fault', y=feature, palette="Set2", ax=ax)
         st.pyplot(fig)
+        # Insight: Boxplots show outliers, spread, and median differences between Fault and No Fault.
+        # Useful to detect features with significant variance or skew in faulty cases.
 
     st.markdown("### 📍 Scatterplots: Detect Patterns or Anomalies")
     st.markdown("These help you check combinations of features with color-coded fault info.")
@@ -129,11 +134,16 @@ with tab2:
     sns.scatterplot(data=df, x="Brake_Pressure", y="Pad_Wear_Level", hue="Fault", palette="Set1", ax=ax)
     ax.set_title("Brake Pressure vs Pad Wear Level")
     st.pyplot(fig)
+    # Insight: Shows relationship between brake pressure and pad wear. 
+    # You may find that high pressure and high wear are often associated with faults.
 
     fig, ax = plt.subplots()
     sns.scatterplot(data=df, x="Pedal_Position", y="Fluid_Temperature", hue="Fault", palette="Set2", ax=ax)
     ax.set_title("Pedal Position vs Fluid Temperature")
     st.pyplot(fig)
+    # Insight: Helps detect abnormal combinations (e.g., high pedal position with low fluid temperature) that could indicate a fault.
+
+     
 
 # ----------------------------- TAB 3 ---------------------------------
 with tab3: