feat(altair): implement scatter-regression-lowess (#2870)

## Implementation: `scatter-regression-lowess` - altair

Implements the **altair** version of `scatter-regression-lowess`.

**File:** `plots/scatter-regression-lowess/implementations/altair.py`

**Parent Issue:** #2855

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:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20608462838)*

---------

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

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plots/scatter-regression-lowess/implementations/altair.py

@@ -0,0 +1,49 @@
+""" pyplots.ai
+scatter-regression-lowess: Scatter Plot with LOWESS Regression
+Library: altair 6.0.0 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+from statsmodels.nonparametric.smoothers_lowess import lowess
+
+
+# Data - complex non-linear relationship
+np.random.seed(42)
+n = 200
+x = np.linspace(0, 10, n)
+# Non-linear pattern: sine wave with trend and noise
+y = 2 * np.sin(x * 0.8) + 0.5 * x + np.random.normal(0, 0.8, n)
+
+# Create DataFrame for scatter points
+df = pd.DataFrame({"x": x, "y": y})
+
+# Compute LOWESS smoothed values
+lowess_result = lowess(y, x, frac=0.3, return_sorted=True)
+df_lowess = pd.DataFrame({"x": lowess_result[:, 0], "y_lowess": lowess_result[:, 1]})
+
+# Scatter points layer
+scatter = (
+    alt.Chart(df)
+    .mark_point(size=100, opacity=0.6, color="#306998")
+    .encode(x=alt.X("x:Q", title="Independent Variable (x)"), y=alt.Y("y:Q", title="Dependent Variable (y)"))
+)
+
+# LOWESS curve layer
+lowess_line = (
+    alt.Chart(df_lowess).mark_line(strokeWidth=4, color="#FFD43B").encode(x=alt.X("x:Q"), y=alt.Y("y_lowess:Q"))
+)
+
+# Combine layers
+chart = (
+    (scatter + lowess_line)
+    .properties(width=1600, height=900, title=alt.Title("scatter-regression-lowess · altair · pyplots.ai", fontSize=28))
+    .configure_axis(labelFontSize=18, titleFontSize=22, gridOpacity=0.3)
+    .configure_view(strokeWidth=0)
+)
+
+# Save as PNG and HTML
+chart.save("plot.png", scale_factor=3.0)
+chart.save("plot.html")

plots/scatter-regression-lowess/metadata/altair.yaml

@@ -0,0 +1,27 @@
+library: altair
+specification_id: scatter-regression-lowess
+created: '2025-12-30T23:52:29Z'
+updated: '2025-12-30T23:55:55Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608462838
+issue: 2855
+python_version: 3.13.11
+library_version: 6.0.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-regression-lowess/altair/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-regression-lowess/altair/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-regression-lowess/altair/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent visual clarity with properly sized text elements and good color contrast
+  - LOWESS curve is visually distinct and captures the complex non-linear pattern
+    smoothly
+  - Clean, idiomatic Altair code using declarative layer composition
+  - Proper use of frac=0.3 for balanced smoothness as specified
+  - Good marker sizing and transparency for the data density (200 points)
+  - Canvas utilization and layout are well balanced
+  weaknesses:
+  - Axis labels are generic rather than having real-world context with units
+  - No interactive features like tooltips or zoom added despite Altair strength in
+    interactivity
+  - Markers use outline style which can be slightly less visible than filled markers