pages/11_Ensembling_Techniques.py

import streamlit as st
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from sklearn.ensemble import VotingClassifier, BaggingClassifier, RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import numpy as np

st.set_page_config(page_title="Ensemble Methods", page_icon="🤖", layout="wide")

st.markdown("<h1 style='text-align: center;'>🤖 Ensemble Learning Visualized</h1>", unsafe_allow_html=True)
st.markdown("### Select an Ensemble Method from the options below:")

st.sidebar.title("🤖 Choose an Ensemble Technique")
model_choice = st.sidebar.radio("Select Ensemble Method:", ["Voting", "Bagging", "Random Forest"])

X, y = make_moons(n_samples=300, noise=0.25, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

def plot_decision_boundary(model, X, y, title):
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 200),
                         np.linspace(y_min, y_max, 200))
    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)

    fig, ax = plt.subplots()
    ax.contourf(xx, yy, Z, alpha=0.3, cmap='RdYlBu')
    ax.scatter(X[:, 0], X[:, 1], c=y, cmap='RdYlBu', edgecolor='k')
    ax.set_title(title)
    st.pyplot(fig)

# Model Training and Plotting
if model_choice == "Voting":
    st.subheader("🗳️ Voting Classifier")
    st.write("""
    Combines multiple classifiers (Logistic Regression, SVM, and Decision Tree) to vote on predictions.  
    You can choose between **Hard Voting** (majority class) and **Soft Voting** (average probabilities).
    """)
    clf1 = LogisticRegression()
    clf2 = SVC(probability=True)
    clf3 = DecisionTreeClassifier(max_depth=5)

    voting_clf = VotingClassifier(estimators=[
        ('lr', clf1), ('svc', clf2), ('dt', clf3)],
        voting='soft')

    voting_clf.fit(X_train, y_train)
    y_pred = voting_clf.predict(X_test)
    acc = accuracy_score(y_test, y_pred)
    st.write(f"🎯 **Accuracy:** {acc:.2f}")
    plot_decision_boundary(voting_clf, X, y, "Voting Classifier Decision Region")
    st.markdown("👉 [Open Voting Ensemble Notebook](https://colab.research.google.com/drive/1LPZR9RnvEXP8mzOLOBfSVVyHHZ7GFns4?usp=sharing)", unsafe_allow_html=True)

elif model_choice == "Bagging":
    st.subheader("🧺 Bagging Classifier")
    st.write("""
    Trains multiple Decision Trees on random subsets (with replacement) of data and averages their predictions.  
    Helps reduce variance and overfitting.
    """)
    bag_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=100, random_state=42)
    bag_clf.fit(X_train, y_train)
    y_pred = bag_clf.predict(X_test)
    acc = accuracy_score(y_test, y_pred)
    st.write(f"🎯 **Accuracy:** {acc:.2f}")
    plot_decision_boundary(bag_clf, X, y, "Bagging Classifier Decision Region")
    st.markdown("👉 [Open Bagging Ensemble Notebook](https://colab.research.google.com/drive/1cumZl7H9fqyORfaw236WWxQViJxvSKHV?usp=sharing)", unsafe_allow_html=True)

elif model_choice == "Random Forest":
    st.subheader("🌲 Random Forest")
    st.write("""
    A forest of randomized decision trees.  
    Each tree sees a bootstrapped sample and a random subset of features at every split.
    """)
    rf_clf = RandomForestClassifier(n_estimators=100, random_state=42)
    rf_clf.fit(X_train, y_train)
    y_pred = rf_clf.predict(X_test)
    acc = accuracy_score(y_test, y_pred)
    st.write(f"🎯 **Accuracy:** {acc:.2f}")
    plot_decision_boundary(rf_clf, X, y, "Random Forest Decision Region")
    st.markdown("👉 [Open Random Forest Notebook](https://colab.research.google.com/drive/1S6YyfTx9N35E5fpPF0z6ZDm85BSp1deT?usp=sharing)", unsafe_allow_html=True)

st.markdown("---")
st.success("✅ Ensemble techniques improve model stability, reduce overfitting, and deliver better results. Try them on your data!")