Update pages/Introduction.py

pages/Introduction.py

@@ -70,21 +70,21 @@ st.write('**TF - IDF Score:** \n - Combines TF and IDF to calculate the importan
 st.write("Examples:")
 
 st.write("""
-### Example
+**Example**
 **Consider these two documents:**
 
 - "I love NLP"
 - "NLP is amazing"
 
-#### Step 1: Calculate TF
+**Step 1: Calculate TF**
 - "NLP" appears once in each document, so its TF is **1/3** in both.
 - Words like "love" and "amazing" also have a TF of **1/3**.
 
-#### Step 2: Calculate IDF
+**Step 2: Calculate IDF**
 - "NLP" appears in both documents, so its IDF is **log(2/2) = 0**.
 - "love" and "amazing" appear in only one document each, so their IDF is **log(2/1) = 0.69**.
 
-#### Step 3: Compute TF-IDF
+**Step 3: Compute TF-IDF**
 - "NLP" gets a TF-IDF score of **1/3 × 0 = 0** (not unique).
 - "love" and "amazing" get scores of **1/3 × 0.69 = 0.23** (more unique).
 """)