diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2258e26..a415057 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -11,6 +11,8 @@ Format: [Keep a Changelog](https://keepachangelog.com/en/1.1.0/)
   CONTRIBUTORS.md. Implemented as `scripts/check_credit.sh`, wired into
   `ci.yml` on `pull_request` events only; failures surface as inline
   `::error::` annotations on the Files tab. Closes #113.
+- Regression coverage ensuring `MovesManagementClassifier.fit` preserves
+  DataFrame column names in `feature_names_in_`. Closes #54.
 ### Fixed
 - `CRMCleaner.transform` no longer silently corrupts complex amounts into
   wrong finite floats: cells holding actual `complex` values are masked to
diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md
index e6d4555..a3e9df3 100644
--- a/CONTRIBUTORS.md
+++ b/CONTRIBUTORS.md
@@ -38,6 +38,9 @@ contribution. Code, docs, tests, and review all count.
   regression test pinning the `donor_id` error that `RFMTransformer.fit` raises
   for non-DataFrame input
   ([#139](https://github.com/PhilanthroPy-Project/PhilanthroPy/pull/139)).
+- [@stoppo22](https://github.com/stoppo22): added DataFrame feature-name
+  coverage for `MovesManagementClassifier.fit`
+  ([#146](https://github.com/PhilanthroPy-Project/PhilanthroPy/pull/146)).
 
 ## Getting listed
 
diff --git a/tests/test_moves.py b/tests/test_moves.py
index 6868500..4cbe566 100644
--- a/tests/test_moves.py
+++ b/tests/test_moves.py
@@ -5,6 +5,7 @@
 """
 
 import numpy as np
+import pandas as pd
 import pytest
 
 from philanthropy.models import MovesManagementClassifier
@@ -55,3 +56,21 @@ def test_action_priority_summary_counts_every_donor(stage_Xy):
     summary = clf.action_priority(X)["portfolio_summary"]
     assert sum(summary.values()) == 30
     assert set(summary) <= set(_STAGES)
+
+
+def test_fit_with_dataframe_sets_feature_names_in():
+    X = pd.DataFrame(
+        np.random.default_rng(0).random((30, 5)),
+        columns=["age", "income", "donations", "engagement", "years_active"],
+    )
+    y = np.asarray(_STAGES * 10)
+
+    clf = MovesManagementClassifier(max_iter=10, random_state=0).fit(X, y)
+
+    np.testing.assert_array_equal(
+        clf.feature_names_in_,
+        np.array(
+            ["age", "income", "donations", "engagement", "years_active"],
+            dtype=object,
+        ),
+    )
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