diff --git a/python-client/giskard/core/errors.py b/python-client/giskard/core/errors.py
index 44c526f1c4..8782c4a7d2 100644
--- a/python-client/giskard/core/errors.py
+++ b/python-client/giskard/core/errors.py
@@ -19,3 +19,8 @@ def __init__(self, flavor: str = None, functionality: str = None, msg: str = Non
                 f"with {self.functionality or self.flavor} support "
                 f"with `pip install giskard[{self.flavor}]`."
             )
+
+
+class GiskardImportError(ImportError):
+    def __init__(self, missing_package: str) -> None:
+        self.msg = f"The '{missing_package}' Python package is not installed; please execute 'pip install {missing_package}' to obtain it."
diff --git a/python-client/giskard/integrations/wandb/wandb_utils.py b/python-client/giskard/integrations/wandb/wandb_utils.py
index 8e1fe773db..c72f381833 100644
--- a/python-client/giskard/integrations/wandb/wandb_utils.py
+++ b/python-client/giskard/integrations/wandb/wandb_utils.py
@@ -5,11 +5,13 @@
 import pandas as pd
 from shap import Explanation
 
+from giskard.core.errors import GiskardImportError
+
 try:
     import wandb  # noqa
     from wandb.wandb_run import Run
 except ImportError as e:
-    raise ImportError("The 'wandb' python package is not installed. To get it, run 'pip install wandb'.") from e
+    raise GiskardImportError("wandb") from e
 
 
 @contextlib.contextmanager
diff --git a/python-client/giskard/models/model_explanation.py b/python-client/giskard/models/model_explanation.py
index 59232c67f3..427836fb93 100644
--- a/python-client/giskard/models/model_explanation.py
+++ b/python-client/giskard/models/model_explanation.py
@@ -4,13 +4,11 @@
 
 import numpy as np
 import pandas as pd
-from shap.maskers import Text
-from shap import KernelExplainer, Explanation, Explainer
 
 from giskard.datasets.base import Dataset
 from giskard.models.base import BaseModel
-from giskard.models.shap_result import ShapResult
 from giskard.ml_worker.utils.logging import timer
+from giskard.core.errors import GiskardImportError
 
 warnings.filterwarnings("ignore", message=".*The 'nopython' keyword.*")
 logger = logging.getLogger(__name__)
@@ -123,6 +121,11 @@ def _calculate_dataset_shap_values(model: BaseModel, dataset: Dataset) -> np.nda
     shap_values : np.ndarray
         The model's SHAP values as a numpy array
     """
+    try:
+        from shap import KernelExplainer
+    except ImportError as e:
+        raise GiskardImportError("shap") from e
+
     # Prepare background sample to be used in the KernelSHAP.
     background_df = model.prepare_dataframe(dataset.df, dataset.column_dtypes, dataset.target)
     background_sample = _get_background_example(background_df, dataset.column_types)
@@ -163,7 +166,7 @@ def _get_highest_proba_shap(shap_values: np.ndarray, model: BaseModel, dataset:
     return [shap_values[predicted_class][sample_idx] for sample_idx, predicted_class in enumerate(predictions)]
 
 
-def explain_with_shap(model: BaseModel, dataset: Dataset, only_highest_proba: bool = True) -> ShapResult:
+def explain_with_shap(model: BaseModel, dataset: Dataset, only_highest_proba: bool = True):
     """Explain the model with SHAP and return the results as a `ShapResult` object.
 
     Parameters
@@ -183,6 +186,12 @@ def explain_with_shap(model: BaseModel, dataset: Dataset, only_highest_proba: bo
     ShapResult
         The model's SHAP values.
     """
+
+    try:
+        from shap import Explanation
+        from giskard.models.shap_result import ShapResult
+    except ImportError as e:
+        raise GiskardImportError("shap") from e
     shap_values = _calculate_dataset_shap_values(model, dataset)
     if only_highest_proba and model.is_classification:
         shap_values = _get_highest_proba_shap(shap_values, model, dataset)
@@ -196,6 +205,11 @@ def explain_with_shap(model: BaseModel, dataset: Dataset, only_highest_proba: bo
 
 
 def _calculate_sample_shap_values(model: BaseModel, dataset: Dataset, input_data: Dict) -> np.ndarray:
+    try:
+        from shap import KernelExplainer
+    except ImportError as e:
+        raise GiskardImportError("shap") from e
+
     df = model.prepare_dataframe(dataset.df, column_dtypes=dataset.column_dtypes, target=dataset.target)
     data_to_explain = _prepare_for_explanation(pd.DataFrame([input_data]), model=model, dataset=dataset)
 
@@ -229,6 +243,11 @@ def explain(model: BaseModel, dataset: Dataset, input_data: Dict):
 
 @timer()
 def explain_text(model: BaseModel, input_df: pd.DataFrame, text_column: str, text_document: str):
+    try:
+        from shap.maskers import Text
+        from shap import Explainer
+    except ImportError as e:
+        raise GiskardImportError("shap") from e
     try:
         masker = Text(tokenizer=r"\W+")
         text_explainer = Explainer(text_explanation_prediction_wrapper(model.predict_df, input_df, text_column), masker)
diff --git a/python-client/giskard/models/shap_result.py b/python-client/giskard/models/shap_result.py
index 3737915116..60b502ee49 100644
--- a/python-client/giskard/models/shap_result.py
+++ b/python-client/giskard/models/shap_result.py
@@ -2,11 +2,10 @@
 from dataclasses import dataclass
 from typing import Optional
 
-from shap import Explanation
-
 from giskard.core.core import ModelType, SupportedModelTypes
 from ..utils.analytics_collector import analytics
 from giskard.client.python_utils import warning
+from giskard.core.errors import GiskardImportError
 
 
 class PanelNames(str, Enum):
@@ -39,6 +38,11 @@ class ShapResult:
         A flag indicating whether to provide SHAP explanations only for the predictions with the highest probability or not.
     """
 
+    try:
+        from shap import Explanation
+    except ImportError as e:
+        raise GiskardImportError("shap") from e
+
     explanations: Explanation
     feature_types: Optional[dict] = None
     model_type: Optional[ModelType] = None