Update pages/KNN ALGORITHM.py

pages/KNN ALGORITHM.py

@@ -25,26 +25,26 @@ st.markdown("""
 - Use resampling techniques (SMOTE, undersampling, oversampling) to balance.
 """)
 st.markdown("#### How kNN works")
-st.markdown("##### 1️⃣ Choose a K value")
+st.markdown("##### 1️.Choose a K value")
 st.write("Example: **K = 3**")
 
-st.markdown("##### 2️⃣ Take a query point")
+st.markdown("##### 2️.Take a query point")
 st.latex(r"x_q = [x_1, x_2, \ldots]")
 
-st.markdown("##### 3️⃣ Calculate distance to all training points")
+st.markdown("##### 3️.Calculate distance to all training points")
 st.latex(r"d(x_q, x_1) = d_1, \ d(x_q, x_2) = d_2, \ldots, d(x_q, x_n) = d_n")
 st.write("👉 Usually Euclidean distance is used.")
 
-st.markdown("##### 4️⃣ Sort distances in ascending order")
+st.markdown("##### 4️.Sort distances in ascending order")
 st.write("Example: **d6, d7, d8, ...**")
 
-st.markdown("##### 5️⃣ Pick the K nearest neighbors")
+st.markdown("##### 5️.Pick the K nearest neighbors")
 st.write("If **K = 3**, choose the 3 nearest points → **x6, x7, x8**")
 
-st.markdown("##### 6️⃣ Check their labels (classes)")
+st.markdown("##### 6️.Check their labels (classes)")
 st.write("Example: y6, y7, y8")
 
-st.markdown("##### 7️⃣ Perform Majority Voting 🗳️")
+st.markdown("##### 7️.Perform Majority Voting 🗳️")
 st.write("The most frequent class among K neighbors becomes the predicted label.")
 
 st.success("Example: If 2 are Green 🍏 and 1 is Red 🍎 → Query point is predicted as Green 🍏")