diff --git a/modules/ml-pipeline/src/pipeline/configs/prediction_analysis.yaml b/modules/ml-pipeline/src/pipeline/configs/prediction_analysis.yaml
new file mode 100644
index 0000000..bed35d9
--- /dev/null
+++ b/modules/ml-pipeline/src/pipeline/configs/prediction_analysis.yaml
@@ -0,0 +1 @@
+dataclient_type: local
diff --git a/modules/ml-pipeline/src/pipeline/prediction_analysis.py b/modules/ml-pipeline/src/pipeline/prediction_analysis.py
index c65684f..7f9f9a1 100644
--- a/modules/ml-pipeline/src/pipeline/prediction_analysis.py
+++ b/modules/ml-pipeline/src/pipeline/prediction_analysis.py
@@ -58,6 +58,13 @@ def prediction_analysis(
 ):
 
     test_df = dataclient.load_data(output_test_filepath)
+    predictions = dataclient.load_data("./data/predictions/predictions.parquet")
+
+    mix_df = test_df.copy()
+    mix_df["predictions"] = predictions
+    mix_df["residual"] = abs(mix_df["predictions"] - mix_df["SAP_ENDING"])
+    mix_df = mix_df.sort_values("residual", ascending=False)
+
     target = "SAP_ENDING"
     test_df_without_target = test_df.drop(columns=[target])
 
@@ -78,12 +85,14 @@ def prediction_analysis(
     ag_wrapper = AutogluonWrapper(
         model.model, feature_names=test_df_without_target.columns
     )
-    explainer = shap.KernelExplainer(ag_wrapper.predict, test_df_without_target)
+    explainer = shap.KernelExplainer(
+        ag_wrapper.predict, test_df_without_target.head(100)
+    )
 
     NSHAP_SAMPLES = 100  # how many samples to use to approximate each Shapely value, larger values will be slower
     N_VAL = 30  # how many datapoints from validation data should we interpret predictions for, larger values will be slower
 
-    ROW_INDEX = 0  # index of an example datapoint
+    ROW_INDEX = 8541  # 23690 #21059  # index of an example datapoint
     single_datapoint = test_df_without_target.iloc[[ROW_INDEX]]
     single_prediction = ag_wrapper.predict(single_datapoint)
 
@@ -93,7 +102,11 @@ def prediction_analysis(
         shap_values_single,
         test_df_without_target.iloc[ROW_INDEX, :],
     )
-    ...
+    shap_single_prediciton_df = pd.DataFrame(
+        shap_values_single, columns=test_df_without_target.columns
+    ).T
+    shap_single_prediciton_df.columns = ["contribution"]
+    shap_single_prediciton_df = shap_single_prediciton_df.sort_values("contribution")
 
 
 if __name__ == "__main__":
diff --git a/modules/ml-pipeline/src/pipeline/requirements/training/requirements-dev.txt b/modules/ml-pipeline/src/pipeline/requirements/training/requirements-dev.txt
index d285422..c54d4c0 100644
--- a/modules/ml-pipeline/src/pipeline/requirements/training/requirements-dev.txt
+++ b/modules/ml-pipeline/src/pipeline/requirements/training/requirements-dev.txt
@@ -3,5 +3,6 @@ boto3==1.28.17
 pandas==1.5.3
 autogluon==0.8.2
 alibi==0.9.4
+shap==0.42.1
 pyarrow==13.0.0
 pre-commit==3.3.3