Update pages/EDA.py

pages/EDA.py

@@ -57,3 +57,27 @@ for col in num_cols:
 # Display Outlier Counts
 st.write("Number of Outliers Detected:")
 st.write(outlier_counts)
+
+# Title with color
+st.markdown("<h2 style='text-align: center; color: #2E86C1;'>Why Use the IQR Method?</h2>", unsafe_allow_html=True)
+
+# Explanation with bullet points
+st.markdown("""
+<style>
+    .why-text {
+        font-size: 18px;
+        color: #333;
+        background-color: #f9f9f9;
+        padding: 10px;
+        border-radius: 10px;
+    }
+</style>
+<div class='why-text'>
+    1.  **Other methods like mean and standard deviation can be heavily influenced by extreme values.**<br><br>
+    2. **IQR focuses only on the middle 50% of data (between Q1 and Q3), making it less affected by extreme values.**<br><br>
+    3.**Other methods may remove outliers entirely, leading to data loss.**<br><br>
+    4. **Instead of dropping rows, the IQR method replaces outliers with the mean of the column, keeping the dataset size the same.**<br><br>
+    5.  **This is useful when we don’t want to lose important information but still need to control extreme values.**
+</div>
+""", unsafe_allow_html=True)
+