Update pages/Logistic Regression.py

pages/Logistic Regression.py

@@ -1,113 +1,107 @@
 import streamlit as st
 
-st.set_page_config(page_title="Logistic Regression Explained", page_icon="🔍", layout="wide")
+st.set_page_config(page_title="Logistic Regression", page_icon="🔐", layout="wide")
 
-# Header
-st.title("🔍 Logistic Regression (Classification)")
+# Title
+st.markdown("<h1 style='text-align: center;'>🔐 Logistic Regression - Made Simple</h1>", unsafe_allow_html=True)
 
+# What is Logistic Regression?
+st.header("📚 What is Logistic Regression?")
 st.markdown("""
-Logistic Regression is a **classification algorithm** used to predict the probability of a binary outcome.  
-Despite its name, it’s used for **classification**, not regression!
+Logistic Regression is a **classification algorithm** used to predict **discrete outcomes** (like Yes/No, 0/1, Spam/Not Spam).
+
+Think of it like:
+> “Based on your features (like age, income, health), should you get insurance? Yes or No?”
+
+### Key Facts:
+- It's a **supervised learning** algorithm.
+- Despite the name, it’s used for **classification**, not regression.
+- It outputs a **probability** between 0 and 1 using the **sigmoid function**.
 """)
 
-# SECTION 1 — Concept
-with st.container():
-    st.header("🧠 Core Idea")
-    st.markdown("""
-    Logistic Regression predicts the **probability** of an event happening.  
-    It applies the **Sigmoid (Logistic)** function to a linear equation.
-    
-    **General Formula:**
-    """)
-    st.latex(r"\hat{y} = \sigma(z) = \frac{1}{1 + e^{-z}}")
-    st.latex(r"z = w_0 + w_1x_1 + w_2x_2 + ... + w_nx_n")
+# Use Cases
+st.header("🎯 Real-World Use Cases")
+st.markdown("""
+- 📧 **Spam Detection**: Is this email spam or not?
+- 🏥 **Medical Diagnosis**: Does a patient have diabetes or not?
+- 👥 **Customer Churn**: Will a customer leave the company?
+- 💳 **Fraud Detection**: Is this transaction fraudulent?
+""")
+
+# How it works
+st.header("⚙️ How Does Logistic Regression Work?")
+st.markdown("Let's break it down step-by-step:")
+
+with st.expander("🔢 Step 1: Make a Linear Equation (just like Linear Regression)"):
+    st.latex(r"z = w_0 + w_1x_1 + w_2x_2 + \ldots + w_nx_n")
     st.markdown("""
-    - \\( \hat{y} \\): predicted probability  
-    - \\( z \\): linear combination of inputs  
-    - \\( \sigma(z) \\): Sigmoid function that squashes output between 0 and 1
+    - We calculate a **weighted sum** of the input features.
+    - It's just a straight line equation where weights decide how important each feature is.
     """)
 
-# SECTION 2 — How it Works
-with st.container():
-    st.header("⚙️ How Logistic Regression Works")
-    col1, col2 = st.columns(2)
-
-    with col1:
-        st.markdown("### 🧮 Step-by-step:")
-        st.markdown("""
-        1. Compute linear equation \\( z \\)  
-        2. Apply sigmoid: \\( \hat{y} = \frac{1}{1 + e^{-z}} \\)  
-        3. Threshold output:
-            - \\( \hat{y} > 0.5 \\) → Class 1  
-            - \\( \hat{y} ≤ 0.5 \\) → Class 0
-        """)
-    with col2:
-        st.image("https://upload.wikimedia.org/wikipedia/commons/8/88/Logistic-curve.svg",
-                 caption="Sigmoid Curve — S-Shaped", use_column_width=True)
-
-# SECTION 3 — Use Cases
-with st.container():
-    st.header("🚀 Real-World Use Cases")
+with st.expander("🧪 Step 2: Pass it into a Sigmoid Function"):
+    st.latex(r"\sigma(z) = \frac{1}{1 + e^{-z}}")
     st.markdown("""
-    - 🔐 Spam Detection  
-    - 🧬 Disease Diagnosis  
-    - 💳 Credit Card Fraud  
-    - 📉 Churn Prediction  
-    - 🔍 Search Ranking Classification  
+    - The sigmoid turns the output into a **probability** between 0 and 1.
+    - If the output is close to 1 → Class 1  
+      If close to 0 → Class 0
     """)
 
-# SECTION 4 — Loss Function
-with st.container():
-    st.header("❌ Loss Function: Binary Cross Entropy")
-    st.markdown("Logistic regression minimizes this loss function:")
-    st.latex(r"Loss = - \left[ y \cdot \log(\hat{y}) + (1 - y) \cdot \log(1 - \hat{y}) \right]")
+    st.image("https://upload.wikimedia.org/wikipedia/commons/8/88/Logistic-curve.svg",
+             caption="S-Shaped Sigmoid Curve", use_column_width=True)
+
+with st.expander("🧠 Step 3: Make the Final Prediction"):
     st.markdown("""
-    - Encourages the model to assign high probabilities to the correct class.
-    - Used to update weights via **gradient descent**.
+    - If \\( \sigma(z) > 0.5 \\), we predict **Class 1**  
+    - Else, we predict **Class 0**
     """)
 
-# SECTION 5 — Evaluation Metrics
-st.header("📏 Evaluation Metrics")
+# Visualize the Flow
+st.header("🔄 Visualization of the Process")
+st.markdown("Feature inputs → Weighted sum → Sigmoid function → Probability → Class label")
 
-col1, col2, col3 = st.columns(3)
+# Loss Function
+st.header("❌ Loss Function - Binary Cross Entropy")
+st.markdown("To improve the model, we need to know **how wrong it is**. That’s where the loss function comes in.")
+st.latex(r"Loss = - [y \cdot \log(\hat{y}) + (1 - y) \cdot \log(1 - \hat{y})]")
+st.markdown("""
+- If predicted probability (\\( \hat{y} \\)) is far from actual label (\\( y \\)), the loss is high.
+- This helps adjust weights to improve predictions.
+""")
 
-with col1:
-    st.subheader("✔️ Accuracy")
+# Evaluation Metrics
+st.header("📏 Evaluation Metrics Explained Simply")
+
+with st.expander("1️⃣ Accuracy"):
     st.latex(r"Accuracy = \frac{TP + TN}{TP + TN + FP + FN}")
-    st.caption("Correct predictions over all predictions")
+    st.markdown("**How often** did we get it right?")
 
-with col2:
-    st.subheader("🎯 Precision")
+with st.expander("2️⃣ Precision (When you care about False Positives)"):
     st.latex(r"Precision = \frac{TP}{TP + FP}")
-    st.caption("How many predicted positives were actually positive?")
+    st.markdown("Out of all predicted **Positives**, how many were **correct**?")
 
-with col3:
-    st.subheader("🔍 Recall")
+with st.expander("3️⃣ Recall (When you care about False Negatives)"):
     st.latex(r"Recall = \frac{TP}{TP + FN}")
-    st.caption("How many actual positives did we catch?")
-
-col4, col5 = st.columns(2)
+    st.markdown("Out of all actual **Positives**, how many did we catch?")
 
-with col4:
-    st.subheader("📊 F1-Score")
+with st.expander("4️⃣ F1 Score (Balance between Precision and Recall)"):
     st.latex(r"F1 = 2 \cdot \frac{Precision \cdot Recall}{Precision + Recall}")
-    st.caption("Balances precision and recall")
+    st.markdown("It balances both **precision** and **recall**.")
 
-with col5:
-    st.subheader("🧠 ROC-AUC")
+with st.expander("5️⃣ ROC-AUC"):
     st.markdown("""
-    - Measures performance across all thresholds  
-    - Plots TPR vs FPR  
-    - AUC = area under ROC curve
+    - 📈 ROC: Curve that plots **True Positive Rate** vs **False Positive Rate**  
+    - 🧠 AUC: Area Under Curve — closer to 1 = better performance
     """)
 
-# SECTION 6 — Key Takeaways
-st.header("📌 Summary & Tips")
+# Summary
+st.header("✅ Quick Summary")
 st.markdown("""
-- Logistic Regression is simple, interpretable, and works well for **linearly separable data**  
-- Assumes no multicollinearity, independent features  
-- Works best when:
-    - You need quick, interpretable results  
-    - The relationship between features and class is linear in log-odds  
-- Use regularization (`L1`, `L2`) to avoid overfitting  
+- Logistic Regression is great for **binary and multi-class classification**
+- Uses **sigmoid function** to map outputs to probability
+- Optimizes using **log loss**
+- Simple, fast, and interpretable model — best for **linearly separable** data
+- Evaluate using metrics like Accuracy, Precision, Recall, F1, and ROC-AUC
 """)
+
+st.success("🎉 Great job! You've just mastered the basics of Logistic Regression.")