Update pages/12_Logistic_Regression.py

pages/12_Logistic_Regression.py

@@ -30,7 +30,25 @@ st.sidebar.markdown("---")
 
 st.markdown("<h1 style='text-align: center;'>📈 Logistic Regression (Theory)</h1>", unsafe_allow_html=True)
 
-with st.expander("📘 What is Logistic Regression?"):
+section = st.radio(
+    "📚 Choose a Topic",
+    [
+        "Introduction",
+        "Step vs Sigmoid",
+        "Loss Function",
+        "Gradient Descent",
+        "Multiclass Logistic Regression",
+        "Regularization",
+        "Multicollinearity",
+        "Hyperparameters",
+        "Colab Notebook"
+    ],
+    horizontal=True
+)
+
+# Section Content
+if section == "Introduction":
+    st.header("📘 What is Logistic Regression?")
     st.write("""
     Logistic Regression is a **supervised learning algorithm** used for **classification tasks**.
     - Mostly used for **binary classification**
@@ -40,7 +58,8 @@ with st.expander("📘 What is Logistic Regression?"):
     ⚠️ Assumption: The data should be **linearly separable** or nearly so.
     """)
 
-with st.expander("⚙️ Step vs Sigmoid Function"):
+elif section == "Step vs Sigmoid":
+    st.header("⚙️ Step vs Sigmoid Function")
     st.write("""
     Initially, people used **step function** for classification:
 
@@ -61,7 +80,8 @@ with st.expander("⚙️ Step vs Sigmoid Function"):
     - \\( z = 0 \\) → output = 0.5
     """)
 
-with st.expander("🧮 Loss Function in Logistic Regression"):
+elif section == "Loss Function":
+    st.header("🧮 Loss Function in Logistic Regression")
     st.write("""
     Logistic Regression uses **Log Loss** (or Binary Cross Entropy) for optimization:
     $$
@@ -72,7 +92,8 @@ with st.expander("🧮 Loss Function in Logistic Regression"):
     - Minimize this loss using **Gradient Descent**
     """)
 
-with st.expander("🔁 Gradient Descent & Learning Rate"):
+elif section == "Gradient Descent":
+    st.header("🔁 Gradient Descent & Learning Rate")
     st.write("""
     Gradient Descent updates weights to minimize loss:
     $$
@@ -90,8 +111,10 @@ with st.expander("🔁 Gradient Descent & Learning Rate"):
     - **Stochastic (SGD)**: Updates per data point  
     - **Mini-Batch**: Updates per small batch → widely used
     """)
-    
-with st.expander("🔢 Multiclass Logistic Regression"):
+
+elif section == "Multiclass Logistic Regression":
+    st.header("🔢 Multiclass Logistic Regression")
+
     st.subheader("1️⃣ Softmax Regression")
     st.write("""
     Generalizes binary logistic regression to multi-class using **Softmax Function**:
@@ -118,7 +141,8 @@ with st.expander("🔢 Multiclass Logistic Regression"):
     Then pick the class with the highest confidence score.
     """)
 
-with st.expander("🧲 Regularization in Logistic Regression"):
+elif section == "Regularization":
+    st.header("🧲 Regularization in Logistic Regression")
     st.write("""
     Regularization helps reduce **overfitting** by penalizing large weights.
     - **L1 (Lasso)**: \\( \\lambda \\sum |w| \\) → Feature selection  
@@ -128,7 +152,8 @@ with st.expander("🧲 Regularization in Logistic Regression"):
     ⚖️ Helps balance **bias vs variance**
     """)
 
-with st.expander("🔍 Detecting Multicollinearity"):
+elif section == "Multicollinearity":
+    st.header("🔍 Detecting Multicollinearity")
     st.write("""
     Multicollinearity = when predictors are highly correlated.  
     🎯 Check using **VIF (Variance Inflation Factor)**:
@@ -139,7 +164,8 @@ with st.expander("🔍 Detecting Multicollinearity"):
     - Applying regularization
     """)
 
-with st.expander("⚙️ Hyperparameters in Logistic Regression"):
+elif section == "Hyperparameters":
+    st.header("⚙️ Hyperparameters in Logistic Regression")
     st.table([
         ["penalty", "Regularization type ('l1', 'l2', 'elasticnet', None)"],
         ["dual", "Use dual formulation (for liblinear only)"],
@@ -154,12 +180,13 @@ with st.expander("⚙️ Hyperparameters in Logistic Regression"):
         ["l1_ratio", "Used with ElasticNet"],
     ])
 
-st.markdown("---")
-st.markdown("### 📓 Want to see it in action? Open the Colab notebook below:")
-st.markdown("""
-<a href='https://colab.research.google.com/drive/1IiZmfkCcMltcE5-r5PbQqRpj_maR_M-f?usp=sharing' target='_blank'>
-🔗 Open Logistic Regression Notebook
-</a>
-""", unsafe_allow_html=True)
+elif section == "Colab Notebook":
+    st.header("📓 Google Colab Notebook")
+    st.markdown("""
+    <a href='https://colab.research.google.com/drive/1IiZmfkCcMltcE5-r5PbQqRpj_maR_M-f?usp=sharing' target='_blank'>
+    🔗 Open Logistic Regression Notebook
+    </a>
+    """, unsafe_allow_html=True)
 
+st.markdown("---")
 st.success("Logistic Regression is simple yet powerful. Master it before diving into complex models!")