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| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| import plotly.express as px | |
| import plotly.graph_objects as go | |
| from sklearn.linear_model import LinearRegression, LogisticRegression | |
| from sklearn.cluster import KMeans | |
| from sklearn.preprocessing import StandardScaler | |
| from sklearn.datasets import make_blobs | |
| def generate_regression_data(n_samples=100, noise=10): | |
| """Generate data for linear regression visualization.""" | |
| np.random.seed(42) | |
| X = np.linspace(0, 10, n_samples) | |
| y = 2 * X + 1 + np.random.normal(0, noise, n_samples) | |
| return X.reshape(-1, 1), y | |
| def generate_classification_data(n_samples=100): | |
| """Generate data for logistic regression visualization.""" | |
| np.random.seed(42) | |
| X = np.random.randn(n_samples, 2) | |
| y = (X[:, 0] + X[:, 1] > 0).astype(int) | |
| return X, y | |
| def generate_clustering_data(n_samples=300): | |
| """Generate data for clustering visualization.""" | |
| np.random.seed(42) | |
| X, _ = make_blobs(n_samples=n_samples, centers=3, cluster_std=1.5) | |
| return X | |
| def show(): | |
| """Display the interactive machine learning visualizations.""" | |
| st.title("Interactive Machine Learning Visualizations") | |
| # Introduction | |
| st.info(""" | |
| This module provides interactive visualizations of three fundamental machine learning concepts: | |
| - 📈 Linear Regression: Predict continuous values | |
| - 🎯 Logistic Regression: Classify binary outcomes | |
| - 🔍 K-Means Clustering: Group similar data points | |
| """) | |
| # Create tabs for different ML concepts | |
| tab1, tab2, tab3 = st.tabs(["📈 Linear Regression", "🎯 Logistic Regression", "🔍 Clustering"]) | |
| with tab1: | |
| st.subheader("Linear Regression") | |
| # Interactive controls | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| n_samples = st.slider("Number of samples", 50, 200, 100) | |
| with col2: | |
| noise = st.slider("Noise level", 1, 20, 10) | |
| # Generate and plot data | |
| X, y = generate_regression_data(n_samples, noise) | |
| # Create scatter plot with dark theme | |
| fig = px.scatter(x=X.flatten(), y=y, | |
| title="Linear Regression Visualization", | |
| labels={'x': 'Feature (X)', 'y': 'Target (y)'}, | |
| template="plotly_dark") | |
| # Add regression line | |
| model = LinearRegression() | |
| model.fit(X, y) | |
| y_pred = model.predict(X) | |
| fig.add_trace(go.Scatter(x=X.flatten(), y=y_pred, | |
| mode='lines', | |
| name='Regression Line', | |
| line=dict(color='red'))) | |
| fig.update_layout( | |
| plot_bgcolor='#1E1E1E', | |
| paper_bgcolor='#1E1E1E', | |
| font=dict(color='white') | |
| ) | |
| st.plotly_chart(fig, use_container_width=True) | |
| # Display model information | |
| st.success(f""" | |
| **Model Information** | |
| - Slope (Coefficient): {model.coef_[0]:.2f} | |
| - Intercept: {model.intercept_:.2f} | |
| - R² Score: {model.score(X, y):.2f} | |
| """) | |
| with tab2: | |
| st.subheader("Logistic Regression") | |
| # Generate data | |
| X, y = generate_classification_data() | |
| # Create scatter plot with dark theme | |
| fig = px.scatter(x=X[:, 0], y=X[:, 1], | |
| color=y.astype(str), | |
| title="Logistic Regression Visualization", | |
| labels={'x': 'Feature 1', 'y': 'Feature 2'}, | |
| template="plotly_dark") | |
| # Add decision boundary | |
| model = LogisticRegression() | |
| model.fit(X, y) | |
| # Create mesh grid for decision boundary | |
| 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.arange(x_min, x_max, 0.1), | |
| np.arange(y_min, y_max, 0.1)) | |
| Z = model.predict(np.c_[xx.ravel(), yy.ravel()]) | |
| Z = Z.reshape(xx.shape) | |
| fig.add_trace(go.Contour(x=xx[0], y=yy[:, 0], z=Z, | |
| showscale=False, | |
| opacity=0.3, | |
| colorscale='RdBu')) | |
| fig.update_layout( | |
| plot_bgcolor='#1E1E1E', | |
| paper_bgcolor='#1E1E1E', | |
| font=dict(color='white') | |
| ) | |
| st.plotly_chart(fig, use_container_width=True) | |
| # Display model information | |
| st.success(f""" | |
| **Model Information** | |
| - Accuracy: {model.score(X, y):.2f} | |
| - Coefficients: [{model.coef_[0][0]:.2f}, {model.coef_[0][1]:.2f}] | |
| - Intercept: {model.intercept_[0]:.2f} | |
| """) | |
| with tab3: | |
| st.subheader("K-Means Clustering") | |
| # Interactive controls | |
| n_clusters = st.slider("Number of clusters", 2, 6, 3) | |
| # Generate data | |
| X = generate_clustering_data() | |
| # Perform clustering | |
| kmeans = KMeans(n_clusters=n_clusters, random_state=42) | |
| clusters = kmeans.fit_predict(X) | |
| # Create scatter plot with dark theme | |
| fig = px.scatter(x=X[:, 0], y=X[:, 1], | |
| color=clusters.astype(str), | |
| title="K-Means Clustering Visualization", | |
| labels={'x': 'Feature 1', 'y': 'Feature 2'}, | |
| template="plotly_dark") | |
| # Add cluster centers | |
| fig.add_trace(go.Scatter(x=kmeans.cluster_centers_[:, 0], | |
| y=kmeans.cluster_centers_[:, 1], | |
| mode='markers', | |
| marker=dict(size=12, symbol='star', color='white'), | |
| name='Cluster Centers')) | |
| fig.update_layout( | |
| plot_bgcolor='#1E1E1E', | |
| paper_bgcolor='#1E1E1E', | |
| font=dict(color='white') | |
| ) | |
| st.plotly_chart(fig, use_container_width=True) | |
| # Display clustering information | |
| st.success(f""" | |
| **Clustering Information** | |
| - Number of Clusters: {n_clusters} | |
| - Inertia (Sum of Squared Distances): {kmeans.inertia_:.2f} | |
| """) | |
| # Footer | |
| st.info(""" | |
| **Key Takeaways** | |
| - Linear Regression: Fits a line to predict continuous values | |
| - Logistic Regression: Creates a decision boundary for classification | |
| - K-Means Clustering: Groups similar data points into clusters | |
| """) |