feat(altair): implement elbow-curve (#2338)

## Implementation: `elbow-curve` - altair

Implements the **altair** version of `elbow-curve`.

**File:** `plots/elbow-curve/implementations/altair.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20528203451)*

---------

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/elbow-curve/implementations/altair.py

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+""" pyplots.ai
+elbow-curve: Elbow Curve for K-Means Clustering
+Library: altair 6.0.0 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-26
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+
+# Data - Simulate K-means inertia values with realistic decay
+np.random.seed(42)
+k_values = list(range(1, 12))
+
+# Realistic inertia: sharp drop initially, then diminishing returns
+# Using exponential decay with noise
+base_inertia = 5000
+inertia = []
+for k in k_values:
+    decay = base_inertia * np.exp(-0.35 * (k - 1)) + 200
+    noise = np.random.uniform(-50, 50)
+    inertia.append(max(decay + noise, 150))
+
+# Mark optimal k (elbow point at k=4)
+optimal_k = 4
+
+df = pd.DataFrame({"Number of Clusters (k)": k_values, "Inertia (Within-Cluster Sum of Squares)": inertia})
+
+# Create base line chart
+line = (
+    alt.Chart(df)
+    .mark_line(color="#306998", strokeWidth=4)
+    .encode(
+        x=alt.X(
+            "Number of Clusters (k):Q",
+            scale=alt.Scale(domain=[0.5, 11.5]),
+            axis=alt.Axis(tickCount=11, values=k_values),
+        ),
+        y=alt.Y("Inertia (Within-Cluster Sum of Squares):Q", scale=alt.Scale(domain=[0, max(inertia) * 1.1])),
+    )
+)
+
+# Add points at each k value
+points = (
+    alt.Chart(df)
+    .mark_point(size=300, color="#306998", filled=True)
+    .encode(
+        x="Number of Clusters (k):Q",
+        y="Inertia (Within-Cluster Sum of Squares):Q",
+        tooltip=["Number of Clusters (k)", "Inertia (Within-Cluster Sum of Squares)"],
+    )
+)
+
+# Highlight the elbow point (optimal k)
+elbow_df = df[df["Number of Clusters (k)"] == optimal_k]
+elbow_point = (
+    alt.Chart(elbow_df)
+    .mark_point(size=600, color="#FFD43B", filled=True, stroke="#306998", strokeWidth=3)
+    .encode(x="Number of Clusters (k):Q", y="Inertia (Within-Cluster Sum of Squares):Q")
+)
+
+# Add annotation for elbow point
+elbow_text = (
+    alt.Chart(elbow_df)
+    .mark_text(align="left", baseline="bottom", dx=15, dy=-15, fontSize=20, fontWeight="bold", color="#306998")
+    .encode(
+        x="Number of Clusters (k):Q",
+        y="Inertia (Within-Cluster Sum of Squares):Q",
+        text=alt.value(f"Optimal k = {optimal_k}"),
+    )
+)
+
+# Combine layers
+chart = (
+    (line + points + elbow_point + elbow_text)
+    .properties(
+        width=1600, height=900, title=alt.Title("elbow-curve · altair · pyplots.ai", fontSize=28, anchor="middle")
+    )
+    .configure_axis(labelFontSize=18, titleFontSize=22, gridColor="#CCCCCC", gridOpacity=0.3)
+    .configure_view(strokeWidth=0)
+)
+
+# Save outputs
+chart.save("plot.png", scale_factor=3.0)
+chart.save("plot.html")

plots/elbow-curve/metadata/altair.yaml

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+library: altair
+specification_id: elbow-curve
+created: '2025-12-26T19:35:15Z'
+updated: '2025-12-26T19:37:48Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20528203451
+issue: 0
+python_version: 3.13.11
+library_version: 6.0.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/elbow-curve/altair/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/elbow-curve/altair/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/elbow-curve/altair/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent elbow visualization with clear annotation of optimal k=4
+  - Well-designed layered chart combining line, points, and highlighted elbow marker
+  - Good use of Altair declarative grammar with separate chart layers
+  - Appropriate color scheme with Python-themed blue and contrasting yellow highlight
+  - Interactive tooltips provide additional context on hover
+  - Both PNG and HTML outputs generated for static and interactive viewing
+  weaknesses:
+  - No legend present to explain what the line/points represent
+  - Y-axis label could note that inertia is unitless or define the metric more explicitly