Update app.py

app.py

@@ -0,0 +1,111 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.datasets import load_breast_cancer
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score, classification_report
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Streamlit Page Config
+st.set_page_config(page_title="SVM Classifier", layout="wide")
+st.title("🔬 SVM Classifier on Breast Cancer Dataset")
+
+# Intro Section
+st.markdown("""
+## 🤖 What is a Support Vector Machine (SVM)?
+Support Vector Machine is a powerful classification algorithm that works by finding the optimal decision boundary (hyperplane) that best separates different classes.
+
+### Key Features:
+- Maximizes the margin between classes
+- Uses support vectors — data points closest to the margin
+- Can handle linear and non-linear data using **kernels**
+
+---
+
+## 📊 Dataset: Breast Cancer Diagnosis
+We’ll classify tumors as **Malignant (1)** or **Benign (0)** based on features from cell nuclei in digitized images.
+""")
+
+# Load Dataset
+@st.cache_data
+def load_data():
+    data = load_breast_cancer()
+    df = pd.DataFrame(data.data, columns=data.feature_names)
+    df["target"] = data.target
+    return df, data
+
+df, data_info = load_data()
+
+# Show Data
+st.subheader("🔍 Data Preview")
+st.dataframe(df.head(), use_container_width=True)
+
+# Sidebar Settings
+st.sidebar.header("⚙️ SVM Settings")
+kernel = st.sidebar.selectbox("Kernel Type", ["linear", "rbf", "poly"])
+C = st.sidebar.slider("Regularization (C)", min_value=0.01, max_value=10.0, value=1.0)
+
+# Preprocess
+X = df.drop("target", axis=1)
+y = df["target"]
+
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
+
+# Model Training
+model = SVC(kernel=kernel, C=C, probability=True, random_state=42)
+model.fit(X_train, y_train)
+y_pred = model.predict(X_test)
+
+# Results
+acc = accuracy_score(y_test, y_pred)
+st.success(f"✅ Accuracy: {acc * 100:.2f}%")
+
+st.markdown("### 📋 Classification Report")
+st.text(classification_report(y_test, y_pred, target_names=data_info.target_names))
+
+# Feature Visualization
+st.subheader("📈 Visualizing with 2 Features")
+feature_x = st.selectbox("X-axis Feature", df.columns[:-1], index=0)
+feature_y = st.selectbox("Y-axis Feature", df.columns[:-1], index=1)
+
+X_vis = df[[feature_x, feature_y]]
+X_vis_scaled = scaler.fit_transform(X_vis)
+X_train_vis, X_test_vis, y_train_vis, y_test_vis = train_test_split(X_vis_scaled, y, test_size=0.2, random_state=42)
+
+model_vis = SVC(kernel=kernel, C=C)
+model_vis.fit(X_train_vis, y_train_vis)
+
+# Decision Boundary
+h = 0.02
+x_min, x_max = X_vis_scaled[:, 0].min() - 1, X_vis_scaled[:, 0].max() + 1
+y_min, y_max = X_vis_scaled[:, 1].min() - 1, X_vis_scaled[:, 1].max() + 1
+xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
+Z = model_vis.predict(np.c_[xx.ravel(), yy.ravel()])
+Z = Z.reshape(xx.shape)
+
+fig, ax = plt.subplots(figsize=(8, 6))
+plt.contourf(xx, yy, Z, alpha=0.3, cmap="coolwarm")
+sns.scatterplot(x=X_vis_scaled[:, 0], y=X_vis_scaled[:, 1], hue=df["target"], palette="coolwarm", ax=ax)
+plt.xlabel(feature_x)
+plt.ylabel(feature_y)
+plt.title("SVM Decision Boundary")
+st.pyplot(fig)
+
+# Summary
+st.markdown("""
+---
+## 💡 Summary
+- SVM creates a hyperplane that separates classes.
+- Works well for small and high-dimensional datasets.
+- The `C` parameter controls the trade-off between margin and misclassification.
+
+### Tips:
+- Use **RBF kernel** for non-linear data.
+- Try adjusting C to see how the margin changes.
+""")