diff --git a/day5/requirements.txt b/day5/requirements.txt
index d37edb49b..b3c12277c 100644
--- a/day5/requirements.txt
+++ b/day5/requirements.txt
@@ -4,4 +4,5 @@ mlflow
 pandas
 pytest
 great_expectations
-black
\ No newline at end of file
+black
+psutil
\ No newline at end of file
diff --git "a/day5/\346\274\224\347\277\2223/tests/test_model.py" "b/day5/\346\274\224\347\277\2223/tests/test_model.py"
index e11a19a5c..48f4ea819 100644
--- "a/day5/\346\274\224\347\277\2223/tests/test_model.py"
+++ "b/day5/\346\274\224\347\277\2223/tests/test_model.py"
@@ -11,6 +11,7 @@
 from sklearn.preprocessing import OneHotEncoder, StandardScaler
 from sklearn.compose import ColumnTransformer
 from sklearn.pipeline import Pipeline
+import psutil
 
 # テスト用データとモデルパスを定義
 DATA_PATH = os.path.join(os.path.dirname(__file__), "../data/Titanic.csv")
@@ -157,7 +158,7 @@ def test_model_reproducibility(sample_data, preprocessor):
         steps=[
             ("preprocessor", preprocessor),
             ("classifier", RandomForestClassifier(n_estimators=100, random_state=42)),
-        ]
+        ] 
     )
 
     # 学習
@@ -171,3 +172,19 @@ def test_model_reproducibility(sample_data, preprocessor):
     assert np.array_equal(
         predictions1, predictions2
     ), "モデルの予測結果に再現性がありません"
+    
+def memory_usage_amount(train_model):
+    model, X_test, y_test = train_model
+    # プロセス取得
+    process = psutil.Process(os.getpid())
+
+    # predict前のメモリ
+    mem_before = process.memory_info().rss / 1024 ** 2  # MB単位
+
+    # 実行
+    model.predict(X_test)
+
+    # predict後のメモリ
+    mem_after = process.memory_info().rss / 1024 ** 2
+    
+    assert mem_after - mem_before < 10, "推論にメモリを使いすぎです"