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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
""") |