Update app.py

app.py

@@ -4,12 +4,10 @@ import numpy as np
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
 from sklearn.svm import SVC
-from sklearn.linear_model import LogisticRegression
 from sklearn.preprocessing import StandardScaler
 from sklearn.metrics import classification_report, accuracy_score
 import matplotlib.pyplot as plt
 import seaborn as sns
-from io import StringIO
 
 # Page config
 st.set_page_config(page_title="Explore SVM Algorithm", layout="wide")
@@ -20,7 +18,6 @@ st.title("🔍 Support Vector Machine (SVM)")
 # -----------------------------------
 st.markdown("""
 ## 🤖 What is a Support Vector Machine (SVM)?
-
 SVM is a powerful supervised learning algorithm used for classification and regression.
 It works by finding a hyperplane that best separates the classes in the feature space.
 
@@ -30,8 +27,8 @@ It works by finding a hyperplane that best separates the classes in the feature
 - Can use **kernel tricks** to handle non-linear classification
 
 ---
-## ⚙️ How SVM Works
 
+## ⚙️ How SVM Works
 1. Find the optimal hyperplane that separates classes.
 2. Use **support vectors** — data points closest to the hyperplane.
 3. Maximize the margin between these support vectors.
@@ -41,6 +38,7 @@ It works by finding a hyperplane that best separates the classes in the feature
 - *Linear*: Straight line separation  
 - *RBF (Gaussian)*: Circular, good for complex boundaries  
 - *Polynomial*: Curved boundaries  
+
 ---
 """)
 
@@ -84,23 +82,6 @@ svm_report = classification_report(y_test, svm_pred, target_names=iris.target_na
 st.markdown("### 📊 SVM Classification Report")
 st.text(svm_report)
 
-# -----------------------------------
-# Compare with Logistic Regression
-# -----------------------------------
-st.markdown("### 🔁 Compare with Logistic Regression")
-
-log_model = LogisticRegression(max_iter=200)
-log_model.fit(X_train, y_train)
-log_pred = log_model.predict(X_test)
-log_acc = accuracy_score(y_test, log_pred)
-
-st.info(f"📈 Logistic Regression Accuracy: {log_acc*100:.2f}%")
-
-if log_acc > svm_acc:
-    st.warning("🤔 Logistic Regression outperformed SVM! Try tuning SVM parameters or switching kernels.")
-else:
-    st.success("✅ SVM performed better than Logistic Regression on this dataset.")
-
 # -----------------------------------
 # Visualize Decision Boundaries
 # -----------------------------------
@@ -154,14 +135,13 @@ Use them when:
 - You want flexibility via kernels.
 
 ---
-### 🧠 Did You Know?
 
+### 🧠 Did You Know?
 - SVMs are **robust to overfitting**, especially in high-dimensional space.
 - The **'C' parameter** controls the trade-off between training error and margin size.
 - The **kernel trick** allows SVMs to operate in infinite-dimensional space.
 
 ### 📌 Pros & Cons
-
 | Pros                            | Cons                                |
 |---------------------------------|-------------------------------------|
 | Works well on complex boundaries| Slower on large datasets            |
@@ -169,8 +149,8 @@ Use them when:
 | Can handle non-linear data      | Less interpretable than simpler models |
 
 ---
-### 🌀 Kernel Choice Summary
 
+### 🌀 Kernel Choice Summary
 | Kernel      | Use Case                        |
 |-------------|---------------------------------|
 | Linear      | Simple, linearly separable data |
@@ -181,3 +161,4 @@ Use them when:
 """)
 
 
+