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app.py

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+import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_classification, make_circles, make_blobs, make_moons
+from sklearn.model_selection import train_test_split, learning_curve
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score, f1_score
+from mlxtend.plotting import plot_decision_regions
+
+# Display image
+st.image("inno.jpg", width=600)
+
+# Streamlit app title
+st.title('Boundary Surfaces Visualization')
+
+# Select dataset
+data = st.sidebar.selectbox('Type of data ', ('Classification', 'Circles', 'Blobs', 'Moons'))
+
+if data == 'Classification':
+    X, y = make_classification(n_samples=100, n_features=2, n_redundant=0, random_state=42)
+elif data == 'Circles':
+    X, y = make_circles(n_samples=100, factor=0.5, noise=0.05)
+elif data == 'Blobs':
+    X, y = make_blobs(n_samples=250, centers=2, n_features=2, cluster_std=1.0, random_state=42)
+elif data == 'Moons':
+    X, y = make_moons(n_samples=250, noise=0.1, random_state=42)
+
+# Split dataset
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+def plot_decision_surface(X, y, model, title):
+    plt.figure(figsize=(6,4))
+    plot_decision_regions(X, y, clf=model, colors="#7f7f7f,#bcbd22,#17becf")
+    plt.title(title)
+    st.pyplot(plt.gcf(), clear_figure=True)
+
+# Select classifier
+classifier_name = st.sidebar.selectbox('Select Classifier', ('KNN', 'Naive Bayes', 'Logistic Regression', 'DecisionTreeClassifier'))
+
+if classifier_name == 'KNN':
+    n_neighbors = st.sidebar.slider('Number of Neighbors (k)', 1, 15, 3)
+    weights = st.sidebar.radio('Weight Function', ('uniform', 'distance'))
+    algorithm = st.sidebar.selectbox('Algorithm', ('auto', 'ball_tree', 'kd_tree', 'brute'))
+    
+    model = KNeighborsClassifier(n_neighbors=n_neighbors, weights=weights, algorithm=algorithm)
+    
+elif classifier_name == 'Naive Bayes':
+    model = GaussianNB()
+    
+elif classifier_name == 'DecisionTreeClassifier':
+    model = DecisionTreeClassifier()
+    
+else:
+    model = LogisticRegression()
+
+# Train model
+model.fit(X_train, y_train)
+
+# Make predictions
+y_pred = model.predict(X_test)
+
+# Compute accuracy & F1-score
+accuracy = accuracy_score(y_test, y_pred)
+f1 = f1_score(y_test, y_pred)
+
+# Display metrics in Streamlit
+st.subheader("Model Performance")
+st.write(f"*Accuracy:* {accuracy:.2f}")
+st.write(f"*F1-score:* {f1:.2f}")
+
+# Plot decision boundary
+plot_decision_surface(X, y, model, f'{classifier_name} Decision Surface')
+
+# Plot Learning Curve
+def plot_learning_curve(model, X, y):
+    train_sizes, train_scores, test_scores = learning_curve(model, X, y, cv=5, scoring='accuracy', train_sizes=np.linspace(0.1, 1.0, 10))
+    
+    train_mean = np.mean(train_scores, axis=1)
+    test_mean = np.mean(test_scores, axis=1)
+    
+    plt.figure(figsize=(6,4))
+    plt.plot(train_sizes, train_mean, 'o-', label="Training Accuracy")
+    plt.plot(train_sizes, test_mean, 'o-', label="Validation Accuracy")
+    
+    plt.xlabel("Training Samples")
+    plt.ylabel("Accuracy")
+    plt.title(f"Learning Curve: {classifier_name}")
+    plt.legend()
+    st.pyplot(plt.gcf(), clear_figure=True)
+
+# Display Learning Curve
+st.subheader("Learning Curve")
+plot_learning_curve(model, X, y)

requirements.txt

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+streamlit
+scikit-learn
+numpy 
+matplotlib
+seaborn
+mlxtend