diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 8cb341a..706bdf8 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -9,14 +9,14 @@ on:
     paths:
       - "powershap/**"
       - "tests/**"
-      - "poetry.lock"
+      - "uv.lock"
       - ".github/workflows/test.yml"
   pull_request:
     branches: [ main ]
     paths:
       - "powershap/**"
       - "tests/**"
-      - "poetry.lock"
+      - "uv.lock"
       - ".github/workflows/test.yml"
 
 jobs:
@@ -26,54 +26,48 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: ['ubuntu-latest']  # TODO - do we add other OSes?
-        python-version: ["3.9", "3.10", "3.11", "3.12"]
+        os: ['ubuntu-latest', 'windows-latest']  # TODO - do we add other OSes?
+        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
     defaults:
       run:
         shell: bash
 
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v5
+
+    - name: Install uv
+      uses: astral-sh/setup-uv@v6
+      with:
+        enable-cache: true
+
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
+        
+    - name: Install llvm on Ubuntu
+      if: runner.os == 'Linux'
+      run: sudo apt-get install llvm # Because https://github.com/slundberg/shap/issues/1854
+
+    - name: Install MSVC build tools on Windows
+      if: runner.os == 'Windows'
+      uses: microsoft/setup-msbuild@v1
 
-    - name: Install Poetry
-      uses: snok/install-poetry@v1
-      with:
-        version: 1.5.1
-    - name: Cache poetry
-      id: cached-poetry-dependencies
-      uses: actions/cache@v3
-      with:
-        path: ~/.cache/pypoetry/virtualenvs
-        key: ${{ runner.os }}-poetry-${{ hashFiles('poetry.lock') }}-python-${{ matrix.python-version }}
-    - run: poetry --version
 
-    - run: sudo apt-get install llvm  # Because https://github.com/slundberg/shap/issues/1854
     - name: Install dependencies
-      run: poetry install --all-extras
-      # Do not use caching (anymore)
-#      if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
+      run: uv sync --locked --all-extras --dev
 
-#     - name: Lint with flake8
-#       run: |
-#         # stop the build if there are Python syntax errors or undefined names
-#         flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-#         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-#         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with pytest
-
       run: |
-        poetry run pytest --cov=powershap --junitxml=junit/test-results-${{ matrix.python-version }}.xml --cov-report=xml tests
+        uv run pytest --cov=powershap --junitxml=junit/test-results-${{ matrix.python-version }}.xml --cov-report=xml tests
+
     - name: Upload pytest test results
-      uses: actions/upload-artifact@v3
+      uses: actions/upload-artifact@v4
       with:
-        name: pytest-results-${{ matrix.python-version }}
-        path: junit/test-results-${{ matrix.python-version }}.xml
+        name: pytest-results-${{ matrix.python-version }}-${{matrix.os}}
+        path: junit/test-results-${{ matrix.python-version }}-${{matrix.os}}.xml
         # Use always() to always run this step to publish test results when there are test failures
       if: ${{ always() }}
 
     - name: Upload coverage to Codecov
-      uses: codecov/codecov-action@v3
\ No newline at end of file
+      uses: codecov/codecov-action@v5
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
deleted file mode 100644
index 432e987..0000000
--- a/poetry.lock
+++ /dev/null
@@ -1,2438 +0,0 @@
-# This file is automatically @generated by Poetry 1.7.0 and should not be changed by hand.
-
-[[package]]
-name = "absl-py"
-version = "2.1.0"
-description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
-optional = false
-python-versions = ">=3.7"
-files = [
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-    {file = "absl_py-2.1.0-py3-none-any.whl", hash = "sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308"},
-]
-
-[[package]]
-name = "astunparse"
-version = "1.6.3"
-description = "An AST unparser for Python"
-optional = false
-python-versions = "*"
-files = [
-    {file = "astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8"},
-    {file = "astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872"},
-]
-
-[package.dependencies]
-six = ">=1.6.1,<2.0"
-wheel = ">=0.23.0,<1.0"
-
-[[package]]
-name = "black"
-version = "22.12.0"
-description = "The uncompromising code formatter."
-optional = false
-python-versions = ">=3.7"
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-]
-
-[package.dependencies]
-click = ">=8.0.0"
-mypy-extensions = ">=0.4.3"
-pathspec = ">=0.9.0"
-platformdirs = ">=2"
-tomli = {version = ">=1.1.0", markers = "python_full_version < \"3.11.0a7\""}
-typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
-
-[package.extras]
-colorama = ["colorama (>=0.4.3)"]
-d = ["aiohttp (>=3.7.4)"]
-jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
-uvloop = ["uvloop (>=0.15.2)"]
-
-[[package]]
-name = "cachetools"
-version = "5.3.3"
-description = "Extensible memoizing collections and decorators"
-optional = false
-python-versions = ">=3.7"
-files = [
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-]
-
-[[package]]
-name = "catboost"
-version = "1.2.5"
-description = "CatBoost Python Package"
-optional = false
-python-versions = "*"
-files = [
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-
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-    {file = "wrapt-1.14.1.tar.gz", hash = "sha256:380a85cf89e0e69b7cfbe2ea9f765f004ff419f34194018a6827ac0e3edfed4d"},
-]
-
-[[package]]
-name = "xgboost"
-version = "1.7.6"
-description = "XGBoost Python Package"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "xgboost-1.7.6-py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.macosx_12_0_x86_64.whl", hash = "sha256:4c34675b4d2678c624ddde5d45361e7e16046923e362e4e609b88353e6b87124"},
-    {file = "xgboost-1.7.6-py3-none-macosx_12_0_arm64.whl", hash = "sha256:59b4b366d2cafc7f645e87d897983a5b59be02876194b1d213bd8d8b811d8ce8"},
-    {file = "xgboost-1.7.6-py3-none-manylinux2014_aarch64.whl", hash = "sha256:281c3c6f4fbed2d36bf95cd02a641afa95e72e9abde70064056da5e76233e8df"},
-    {file = "xgboost-1.7.6-py3-none-manylinux2014_x86_64.whl", hash = "sha256:b1d5db49b199152d62bd9217c98760207d3de86d2b9d243260c573ffe638f80a"},
-    {file = "xgboost-1.7.6-py3-none-win_amd64.whl", hash = "sha256:127cf1f5e2ec25cd41429394c6719b87af1456ce583e89f0bffd35d02ad18bcb"},
-    {file = "xgboost-1.7.6.tar.gz", hash = "sha256:1c527554a400445e0c38186039ba1a00425dcdb4e40b37eed0e74cb39a159c47"},
-]
-
-[package.dependencies]
-numpy = "*"
-scipy = "*"
-
-[package.extras]
-dask = ["dask", "distributed", "pandas"]
-datatable = ["datatable"]
-pandas = ["pandas"]
-plotting = ["graphviz", "matplotlib"]
-pyspark = ["cloudpickle", "pyspark", "scikit-learn"]
-scikit-learn = ["scikit-learn"]
-
-[[package]]
-name = "zipp"
-version = "3.18.1"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
-    {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
-]
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
-
-[metadata]
-lock-version = "2.0"
-python-versions = ">=3.9,<=3.12"
-content-hash = "bce14ad75b844a584b53bcd02675d17d1e0cdc1c36a4ee5d573d45a71fd2522b"
diff --git a/powershap/powershap.py b/powershap/powershap.py
index 625abfe..2d1b65f 100644
--- a/powershap/powershap.py
+++ b/powershap/powershap.py
@@ -8,7 +8,9 @@
 from sklearn.base import BaseEstimator
 from sklearn.feature_selection import SelectorMixin
 from sklearn.model_selection import BaseCrossValidator
-from sklearn.utils.validation import check_is_fitted
+from sklearn.utils.validation import check_is_fitted,validate_data
+
+
 
 from .shap_wrappers import ShapExplainerFactory
 from .utils import powerSHAP_statistical_analysis
@@ -365,11 +367,11 @@ def fit(self, X, y, stratify=None, groups=None, **kwargs):
         # Perform the necessary sklearn checks -> X and y are both ndarray.
         # Logs the feature names as well (in self.feature_names_in_ in sklearn 1.x).
         #
-        # These two operations (_validate_data and pd.DataFrame) will also copy
+        # These two operations (validate_data and pd.DataFrame) will also copy
         # the data into a new place in memory, avoiding data mutation. How this
         # happens may not be obvious at first glance:
         #
-        # 1. _validate_data ensures that the data is a numpy array, copying it
+        # 1. validate_data ensures that the data is a numpy array, copying it
         # upon conversion if necessary.
         #
         # 2. pd.DataFrame then copies X, which is now an numpy array, into a
@@ -377,7 +379,9 @@ def fit(self, X, y, stratify=None, groups=None, **kwargs):
         #
         # If this is changed in some way which would allow explain() to mutate
         # the original data, it should cause the data mutation tests to fail.
-        X, y = self._explainer._validate_data(self._validate_data, X, y, multi_output=True)
+
+        # X, y = validate_data(self, X, y, multi_output=True)
+        X, y = self._explainer.validate_data(self, X, y, multi_output=True)
         X = pd.DataFrame(data=X, columns=list(range(X.shape[1])))
 
         self._print("Starting powershap")
@@ -530,5 +534,8 @@ def transform(self, X):
             )
         return super().transform(X)
 
-    def _more_tags(self):
+    # Since sklearn 1.6, the tag system changed so this function is necessary to make it compatible
+    # https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_6_0.html#improvements-to-the-developer-api-for-third-party-libraries
+    def __sklearn_tags__(self):
         return self._explainer._get_more_tags()
+
diff --git a/powershap/shap_wrappers/shap_explainer.py b/powershap/shap_wrappers/shap_explainer.py
index 1d4b8ac..8e852f2 100644
--- a/powershap/shap_wrappers/shap_explainer.py
+++ b/powershap/shap_wrappers/shap_explainer.py
@@ -11,6 +11,18 @@
 import shap
 from numpy.random import RandomState
 from sklearn.model_selection import train_test_split
+from sklearn.utils.validation import validate_data
+
+from sklearn.utils._tags import (
+    ClassifierTags,
+    RegressorTags,
+    Tags,
+    TargetTags,
+    InputTags,
+    TransformerTags,
+    get_tags,
+)
+
 from tqdm.auto import tqdm
 
 
@@ -33,9 +45,10 @@ def __init__(self, model: Any):
     # Should be implemented by subclass
     def _fit_get_shap(self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs) -> np.array:
         raise NotImplementedError
-
-    def _validate_data(self, validate_data: Callable, X, y, **kwargs):
-        return validate_data(X, y, **kwargs)
+    
+    # If the explainer supports nan values, infinite values, or others, the explainer must override this function
+    def validate_data(self, _estimator, X, y, **kwargs):
+        return validate_data(_estimator, X, y, **kwargs)
 
     # Should be implemented by subclass
     @staticmethod
@@ -194,7 +207,10 @@ def explain(
         return pd.DataFrame(data=shaps, columns=X.columns.values)
 
     def _get_more_tags(self):
-        return {}
+        return Tags(
+            estimator_type=None,
+            target_tags=TargetTags(required=False),
+        )
 
 
 ### CATBOOST
@@ -208,22 +224,27 @@ def supports_model(model) -> bool:
         supported_models = [CatBoostRegressor, CatBoostClassifier]
         return isinstance(model, tuple(supported_models))
 
-    def _validate_data(self, validate_data: Callable, X, y, **kwargs):
-        kwargs["force_all_finite"] = False  # catboost allows NaNs and infs in X
+    def validate_data(self, _estimator, X, y, **kwargs):
+        kwargs["ensure_all_finite"] = False  # catboost allows NaNs and infs in X
         kwargs["dtype"] = None  # allow non-numeric data
-        return super()._validate_data(validate_data, X, y, **kwargs)
+        return validate_data(_estimator, X, y, **kwargs)
+
 
     def _fit_get_shap(self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs) -> np.array:
         # Fit the model
         PowerShap_model = self.model.copy().set_params(random_seed=random_seed)
-        PowerShap_model.fit(X_train, Y_train, eval_set=(X_val, Y_val))
+        PowerShap_model.fit(X_train, Y_train, eval_set=(X_val, Y_val), **kwargs)
         # Calculate the shap values
         C_explainer = shap.TreeExplainer(PowerShap_model)
         return C_explainer.shap_values(X_val)
 
+    # Function to define the tags which will be used in sklearn pipelines
     def _get_more_tags(self):
-        return {"allow_nan": True}
-
+        return Tags(
+            estimator_type=None,
+            target_tags=TargetTags(required=False),
+            input_tags=InputTags(allow_nan=True)
+        )
 
 ### LGBM
 
@@ -236,22 +257,27 @@ def supports_model(model) -> bool:
         supported_models = [LGBMClassifier, LGBMRegressor]
         return isinstance(model, tuple(supported_models))
 
-    def _validate_data(self, validate_data: Callable, X, y, **kwargs):
-        kwargs["force_all_finite"] = False  # lgbm allows NaNs and infs in X
-        return super()._validate_data(validate_data, X, y, **kwargs)
+    def validate_data(self, _estimator, X, y, **kwargs):
+        kwargs["ensure_all_finite"] = False  # lgbm allows NaNs and infs in X
+        return validate_data(_estimator, X, y, **kwargs)
 
     def _fit_get_shap(self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs) -> np.array:
         # Fit the model
         # Why we need to use deepcopy and delete LGBM __deepcopy__
         # https://github.com/microsoft/LightGBM/issues/4085
         PowerShap_model = copy(self.model).set_params(random_seed=random_seed)
-        PowerShap_model.fit(X_train, Y_train, eval_set=(X_val, Y_val))
+        PowerShap_model.fit(X_train, Y_train, eval_set=(X_val, Y_val), **kwargs)
         # Calculate the shap values
         C_explainer = shap.TreeExplainer(PowerShap_model)
         return C_explainer.shap_values(X_val)
 
+    # Function to define the tags which will be used in sklearn pipelines
     def _get_more_tags(self):
-        return {"allow_nan": True}
+        return Tags(
+            estimator_type=None,
+            target_tags=TargetTags(required=False),
+            input_tags=InputTags(allow_nan=True)
+        )
 
 
 ### XGBOOST
@@ -264,22 +290,27 @@ def supports_model(model) -> bool:
 
         supported_models = [XGBClassifier, XGBRegressor]
         return isinstance(model, tuple(supported_models))
-
-    def _validate_data(self, validate_data: Callable, X, y, **kwargs):
-        kwargs["force_all_finite"] = False  # xgboost allows NaNs and infs in X
+    
+    def validate_data(self, _estimator, X, y, **kwargs):
+        kwargs["ensure_all_finite"] = False  # xgboost allows NaNs and infs in X
         kwargs["dtype"] = None  # allow non-numeric data
-        return super()._validate_data(validate_data, X, y, **kwargs)
+        return validate_data(_estimator, X, y, **kwargs)
 
     def _fit_get_shap(self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs) -> np.array:
         # Fit the model
         PowerShap_model = copy(self.model).set_params(random_state=random_seed)
-        PowerShap_model.fit(X_train, Y_train, eval_set=[(X_val, Y_val)])
+        PowerShap_model.fit(X_train, Y_train, eval_set=[(X_val, Y_val)], **kwargs)
         # Calculate the shap values
         C_explainer = shap.TreeExplainer(PowerShap_model)
         return C_explainer.shap_values(X_val)
 
+    # Function to define the tags which will be used in sklearn pipelines
     def _get_more_tags(self):
-        return {"allow_nan": True}
+        return Tags(
+            estimator_type=None,
+            target_tags=TargetTags(required=False),
+            input_tags=InputTags(allow_nan=True)
+        )
 
 
 ### RANDOMFOREST
@@ -304,7 +335,7 @@ def _fit_get_shap(self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs) -
 
         # Fit the model
         PowerShap_model = clone(self.model).set_params(random_state=random_seed)
-        PowerShap_model.fit(X_train, Y_train)
+        PowerShap_model.fit(X_train, Y_train, **kwargs)
         # Calculate the shap values
         C_explainer = shap.TreeExplainer(PowerShap_model)
         return C_explainer.shap_values(X_val)
@@ -330,47 +361,108 @@ def _fit_get_shap(self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs) -
             PowerShap_model = clone(self.model).set_params(random_state=random_seed)
         except Exception:
             PowerShap_model = clone(self.model)
-        PowerShap_model.fit(X_train, Y_train)
+        PowerShap_model.fit(X_train, Y_train, **kwargs)
 
         # Calculate the shap values
         C_explainer = shap.explainers.Linear(PowerShap_model, X_train)
         return C_explainer.shap_values(X_val)
 
+# This support an Sklearn Pipeline Explainer, which will be a wrapper around a ShapExplainer
+class PipelineExplainer(ShapExplainer):
 
-### DEEP LEARNING
+    def __init__(self, model: Any):
+        from .shap_explainer_factory import ShapExplainerFactory 
+        """Create a Powershap explainer instance.
 
+        Parameters
+        ----------
+        model: Any
+            The  model from which powershap will use its shap values to perform feature
+            selection.
+
+        """
+        assert self.supports_model(model)
+        self.shap_explainer = ShapExplainerFactory.get_explainer(model=model.steps[-1][1])
+        self.model = model
 
-class DeepLearningExplainer(ShapExplainer):
     @staticmethod
     def supports_model(model) -> bool:
-        import tensorflow as tf  # ; import torch
+        from sklearn.pipeline import Pipeline
 
-        # import torch  ## TODO: do we support pytorch??
-
-        supported_models = [tf.keras.Model]  # , torch.nn.Module]
-        return isinstance(model, tuple(supported_models))
+        return issubclass(type(model), Pipeline)
 
     def _fit_get_shap(self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs) -> np.array:
-        import tensorflow as tf
+        from sklearn.base import clone
+        from sklearn.pipeline import Pipeline
+        
+        # Because the ShapExplainer behavior is different for each model, we extract the model and only keep the preprocessing pipeline
+        powershap_pipeline = clone(Pipeline(self.model.steps[:-1]))
+
+        # 2. Build the parameter dictionary to set the random states to the random seed
+        params_to_set = {}
+        for step_name, step_estimator in powershap_pipeline.steps:
+            if 'random_state' in step_estimator.get_params():
+                # Format: 'step_name__parameter_name'
+                params_to_set[f'{step_name}__random_state'] = random_seed
+
+        # 3. Apply the parameters to the cloned pipeline
+        powershap_pipeline.set_params(**params_to_set)
+                
+        # We fit the pipeline here to be used to transform the data
+        powershap_pipeline.fit(X_train, Y_train, **kwargs)
+
+        # Get the transformed data from all the preceding steps
+        transformed_X_train = powershap_pipeline.transform(X_train)
+        transformed_X_val = powershap_pipeline.transform(X_val)
+
+        return self.shap_explainer._fit_get_shap(transformed_X_train, Y_train, transformed_X_val, Y_val, random_seed, **kwargs)
+
+
+    def validate_data(self, _estimator, X, y, **kwargs):
+        # The assumption here is that the used model is the limiting factor for validation of the data
+        return self.shap_explainer.validate_data(_estimator, X, y, **kwargs)
+    
+    def _get_more_tags(self):
+        return self.shap_explainer._get_more_tags()
 
-        # tf.compat.v1.disable_v2_behavior()  # https://github.com/slundberg/shap/issues/2189
-        # Fit the model
-        PowerShap_model = tf.keras.models.clone_model(self.model)
-        metrics = kwargs.get("nn_metric")
-        PowerShap_model.compile(
-            loss=kwargs["loss"],
-            optimizer=kwargs["optimizer"],
-            metrics=metrics if metrics is None else [metrics],
-            # run_eagerly=True,
-        )
-        _ = PowerShap_model.fit(
-            X_train,
-            Y_train,
-            batch_size=kwargs["batch_size"],
-            epochs=kwargs["epochs"],
-            validation_data=(X_val, Y_val),
-            verbose=False,
-        )
-        # Calculate the shap values
-        C_explainer = shap.DeepExplainer(PowerShap_model, X_train)
-        return C_explainer.shap_values(X_val)
+
+### DEEP LEARNING
+
+# Tensorflow has been phased out and current version does not support deepLearning approach
+# TODO add support for Pytorch instead
+class DeepLearningExplainer(ShapExplainer):
+    @staticmethod
+    def supports_model(model) -> bool:
+        # import tensorflow as tf  # ; import torch
+
+        # import torch  ## TODO: do we support pytorch??
+
+        # supported_models = [tf.keras.Model]  # , torch.nn.Module]
+        # return isinstance(model, tuple(supported_models))
+        return False
+    
+
+    # def _fit_get_shap(self, X_train, Y_train, X_val, Y_val, random_seed, **kwargs) -> np.array:
+    #     # import tensorflow as tf
+
+    #     # tf.compat.v1.disable_v2_behavior()  # https://github.com/slundberg/shap/issues/2189
+    #     # Fit the model
+    #     # PowerShap_model = tf.keras.models.clone_model(self.model)
+    #     # metrics = kwargs.get("nn_metric")
+    #     # PowerShap_model.compile(
+    #     #     loss=kwargs["loss"],
+    #     #     optimizer=kwargs["optimizer"],
+    #     #     metrics=metrics if metrics is None else [metrics],
+    #     #     # run_eagerly=True,
+    #     # )
+    #     # _ = PowerShap_model.fit(
+    #     #     X_train,
+    #     #     Y_train,
+    #     #     batch_size=kwargs["batch_size"],
+    #     #     epochs=kwargs["epochs"],
+    #     #     validation_data=(X_val, Y_val),
+    #     #     verbose=False,
+    #     # )
+    #     # # Calculate the shap values
+    #     # C_explainer = shap.DeepExplainer(PowerShap_model, X_train)
+    #     return C_explainer.shap_values(X_val)
diff --git a/powershap/shap_wrappers/shap_explainer_factory.py b/powershap/shap_wrappers/shap_explainer_factory.py
index c82f01f..ea412da 100644
--- a/powershap/shap_wrappers/shap_explainer_factory.py
+++ b/powershap/shap_wrappers/shap_explainer_factory.py
@@ -10,6 +10,7 @@
     LinearExplainer,
     ShapExplainer,
     XGBoostExplainer,
+    PipelineExplainer
 )
 
 
@@ -23,6 +24,7 @@ class ShapExplainerFactory:
         EnsembleExplainer,
         LinearExplainer,
         DeepLearningExplainer,
+        PipelineExplainer
     ]
 
     @classmethod
diff --git a/powershap/utils.py b/powershap/utils.py
index 68c243e..a7376ae 100644
--- a/powershap/utils.py
+++ b/powershap/utils.py
@@ -61,7 +61,11 @@ def powerSHAP_statistical_analysis(
             required_iterations.append(0)
             effect_size.append(0)
             power_list.append(0)
-
+    
+    # The solve power of statsmodels might not always converge. If that happens it outputs a list of length 1 instead of a float
+    # Numpy typing requires nonambiguous typing so this line is to ensure the cast to np.array later on will not result in a ValueError due to a failed converge
+    required_iterations = [x[0] if not (isinstance(x, float) or isinstance(x, int)) else x for x in required_iterations]
+    
     processed_shaps_df = pd.DataFrame(
         data=np.hstack(
             [
@@ -81,6 +85,7 @@ def powerSHAP_statistical_analysis(
         ],
         index=shaps_df.mean().index,
     )
+
     processed_shaps_df = processed_shaps_df.reindex(
         processed_shaps_df.impact.abs().sort_values(ascending=False).index
     )
diff --git a/pyproject.toml b/pyproject.toml
index 7fc1151..e627166 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,54 +1,54 @@
-[tool.poetry]
+[project]
 name = "powershap"
-version = "0.0.11"
+version = "0.1.0"
 description = "Feature selection using statistical significance of shap values"
-authors = ["Jarne Verhaeghe, Jeroen Van Der Donckt"]
+authors = [{ name = "Jarne Verhaeghe, Jeroen Van Der Donckt" }]
+requires-python = ">=3.9,<=3.13"
 readme = "README.md"
 license = "MIT"
-repository = "https://github.com/predict-idlab/powershap"
-keywords = ["feature selection", "shap", "data-science", "machine learning"]
+keywords = [
+    "feature selection",
+    "shap",
+    "data-science",
+    "machine learning",
+]
+dependencies = [
+    "catboost>=1.0.5",
+    "statsmodels>=0.14.0",
+    "pandas>=1.3",
+    "shap>=0.45.0,<0.46",
+    "scikit-learn>=1.6",
+]
 
-[tool.poetry.dependencies]
-python = ">=3.9,<=3.12"  
-catboost = "^1.0.5"
-statsmodels = "^0.13.2"
-pandas = ">=1.3"
-shap = "^0.45.0"
-scikit-learn = "*"
+[project.urls]
+Repository = "https://github.com/predict-idlab/powershap"
 
-[tool.poetry.dev-dependencies]
-pytest = "^7.1.1"
-pytest-cov = "^3.0.0"
-tensorflow = [
-    { version = "<2.16.0", python = "<3.12"}
-    # support 3.12 when shap support tf 2.16 - https://github.com/shap/shap/issues/3562
+[dependency-groups]
+dev = [
+    "pytest>=7.1.1,<8",
+    "pytest-cov>=3.0.0,<4",
+    "lightgbm>=3.3.2",
+    "xgboost>=1.6.0",
+    "black>=22.12.0,<23",
+    "isort>=5.11.4,<6",
+    "ruff>=0.0.219,<0.0.220",
 ]
-lightgbm = "^3.3.2"
-xgboost = "^1.6.0"
-black = "^22.12.0"
-isort = "^5.11.4"
-ruff = "^0.0.219"
 
 [build-system]
-requires = ["poetry-core>=1.0.0"]
-build-backend = "poetry.core.masonry.api"
-
-## Formatting configuration
+requires = ["hatchling"]
+build-backend = "hatchling.build"
 
-# Linting
 [tool.ruff]
 line-length = 100
 extend-select = ["Q"]
 ignore = ["E402", "F403", "F811", "F401"]
 
-# Formatting
 [tool.black]
 color = true
 line-length = 100
 skip-string-normalization = true
 skip-magic-trailing-comma = true
 
-# Sort imports
 [tool.isort]
 line_length = 100
 known_first_party = ["tsdownsample"]
diff --git a/tests/test_deep_learning_powershap.py b/tests/test_deep_learning_powershap.py
deleted file mode 100644
index 923d7d8..0000000
--- a/tests/test_deep_learning_powershap.py
+++ /dev/null
@@ -1,39 +0,0 @@
-__author__ = "Jeroen Van Der Donckt"
-
-import tensorflow as tf
-
-from powershap import PowerShap
-
-from .conftest import dummy_classification, dummy_regression
-
-# tf.compat.v1.enable_eager_execution()
-
-
-def test_deep_learning_class_powershap(dummy_classification):
-    X, y = dummy_classification
-    n_informative = sum([c.startswith("informative") for c in X.columns])
-    assert n_informative > 0, "No informative columns in the dummy data!"
-
-    model = tf.keras.Sequential()
-    model.add(tf.keras.layers.Dense(5, input_shape=(X.shape[1] + 1,), activation="relu"))
-    model.add(tf.keras.layers.Dense(1, activation="sigmoid"))
-
-    selector = PowerShap(model=model, power_iterations=5, automatic=False)
-
-    selector.fit(X, y, loss="binary_crossentropy", optimizer="adam", batch_size=16, epochs=5)
-    _ = selector.transform(X)
-
-
-def test_deep_learning_regr_powershap(dummy_regression):
-    X, y = dummy_regression
-    n_informative = sum([c.startswith("informative") for c in X.columns])
-    assert n_informative > 0, "No informative columns in the dummy data!"
-
-    model = tf.keras.Sequential()
-    model.add(tf.keras.layers.Dense(5, input_shape=(X.shape[1] + 1,), activation="linear"))
-    model.add(tf.keras.layers.Dense(1, activation="linear"))
-
-    selector = PowerShap(model=model, power_iterations=5, automatic=False)
-
-    selector.fit(X, y, loss="mse", optimizer="adam", batch_size=16, epochs=5)
-    _ = selector.transform(X)
diff --git a/tests/test_pipeline_powershap.py b/tests/test_pipeline_powershap.py
new file mode 100644
index 0000000..d6db8f0
--- /dev/null
+++ b/tests/test_pipeline_powershap.py
@@ -0,0 +1,204 @@
+__author__ = "Jeroen Van Der Donckt, Jarne Verhaeghe"
+
+import numpy as np
+import pandas as pd
+from catboost import CatBoostClassifier, CatBoostRegressor
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+
+from powershap import PowerShap
+
+from .conftest import dummy_classification, dummy_regression
+from sklearn.pipeline import Pipeline
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import FunctionTransformer
+
+def test_pipeline_catboost_class_powershap(dummy_classification):
+    X, y = dummy_classification
+    n_informative = sum([c.startswith("informative") for c in X.columns])
+    assert n_informative > 0, "No informative columns in the dummy data!"
+
+    DummyScaler = FunctionTransformer(lambda x: x)
+
+    selector = PowerShap(
+        model=make_pipeline(DummyScaler, CatBoostClassifier(n_estimators=250, verbose=0)), power_iterations=15, automatic=False
+    )
+
+    selector.fit(X, y)
+    selected_feats = selector.transform(X)
+
+    assert len(selected_feats.columns) == n_informative
+    assert all([c.startswith("informative") for c in selected_feats.columns])
+
+
+def test_pipeline_catboost_regr_powershap(dummy_regression):
+    X, y = dummy_regression
+    n_informative = sum([c.startswith("informative") for c in X.columns])
+    assert n_informative > 0, "No informative columns in the dummy data!"
+
+    DummyScaler = FunctionTransformer(lambda x: x)
+
+    selector = PowerShap(
+        model=make_pipeline(DummyScaler, CatBoostRegressor(n_estimators=250, verbose=0)), power_iterations=15, automatic=False
+    )
+
+    selector.fit(X, y)
+    selected_feats = selector.transform(X)
+
+    assert len(selected_feats.columns) == n_informative
+    assert all([c.startswith("informative") for c in selected_feats.columns])
+
+
+def test_pipeline_catboost_handle_nans(dummy_classification):
+    X, y = dummy_classification
+    X.iloc[:5] = None
+    X["nan_col"] = None
+    assert np.any(pd.isna(X))
+    n_informative = sum([c.startswith("informative") for c in X.columns])
+    assert n_informative > 0, "No informative columns in the dummy data!"
+
+    DummyScaler = FunctionTransformer(lambda x: x)
+
+    selector = PowerShap(
+        model=make_pipeline(DummyScaler, CatBoostClassifier(n_estimators=10, verbose=0)), power_iterations=15
+    )
+
+    selector.fit(X, y)
+    selected_feats = selector.transform(X)
+
+    assert len(selected_feats.columns) == n_informative
+    assert all([c.startswith("informative") for c in selected_feats.columns])
+
+
+def test_pipeline_catboost_handle_infs(dummy_classification):
+    X, y = dummy_classification
+    X.iloc[:5] = np.Inf
+    X["inf_col"] = np.Inf
+    assert np.any(X.isin([np.inf, -np.inf]))
+    n_informative = sum([c.startswith("informative") for c in X.columns])
+    assert n_informative > 0, "No informative columns in the dummy data!"
+
+    DummyScaler = FunctionTransformer(lambda x: x)
+
+    selector = PowerShap(
+        model=make_pipeline(DummyScaler, CatBoostClassifier(n_estimators=10, verbose=0)), power_iterations=15
+    )
+
+    selector.fit(X, y)
+    selected_feats = selector.transform(X)
+
+    assert len(selected_feats.columns) == n_informative
+    assert all([c.startswith("informative") for c in selected_feats.columns])
+
+
+def test_pipeline_catboost_handle_infs_nans(dummy_classification):
+    X, y = dummy_classification
+    X.iloc[:5] = np.Inf
+    X.iloc[5:10] = None
+    X["inf_col"] = np.Inf
+    X["nan_col"] = None
+    assert np.any(X.isin([np.inf, -np.inf]))
+    assert np.any(pd.isna(X))
+    n_informative = sum([c.startswith("informative") for c in X.columns])
+    assert n_informative > 0, "No informative columns in the dummy data!"
+
+    DummyScaler = FunctionTransformer(lambda x: x)
+
+    selector = PowerShap(
+        model=make_pipeline(DummyScaler, CatBoostClassifier(n_estimators=10, verbose=0)), power_iterations=15
+    )
+
+    selector.fit(X, y)
+    selected_feats = selector.transform(X)
+
+    assert len(selected_feats.columns) == n_informative
+    assert all([c.startswith("informative") for c in selected_feats.columns])
+
+
+def test_pipeline_catboost_handle_strings(dummy_classification):
+    X, y = dummy_classification
+    X["cat"] = "miauw"
+    n_informative = sum([c.startswith("informative") for c in X.columns])
+    assert n_informative > 0, "No informative columns in the dummy data!"
+
+    DummyScaler = FunctionTransformer(lambda x: x)
+
+    selector = PowerShap(
+        model=make_pipeline(DummyScaler,CatBoostClassifier(n_estimators=30, verbose=0, cat_features=[X.shape[1] - 1])), 
+        power_iterations=15
+    )
+
+    selector.fit(X, y)
+    selected_feats = selector.transform(X)
+
+    assert len(selected_feats.columns) == n_informative
+    assert all([c.startswith("informative") for c in selected_feats.columns])
+
+
+def test_pipeline_ensemble_class_powershap(dummy_classification):
+    X, y = dummy_classification
+    n_informative = sum([c.startswith("informative") for c in X.columns])
+    assert n_informative > 0, "No informative columns in the dummy data!"
+
+    DummyScaler = FunctionTransformer(lambda x: x)
+    
+    selector = PowerShap(
+        model=make_pipeline(DummyScaler,RandomForestClassifier(n_estimators=25)), power_iterations=15, automatic=False
+    )
+
+    selector.fit(X, y)
+    selected_feats = selector.transform(X)
+
+    assert len(selected_feats.columns) >= n_informative
+    assert sum([c.startswith("informative") for c in selected_feats.columns]) == n_informative
+
+
+def test_pipeline_ensemble_regr_powershap(dummy_regression):
+    X, y = dummy_regression
+    n_informative = sum([c.startswith("informative") for c in X.columns])
+    assert n_informative > 0, "No informative columns in the dummy data!"
+
+    DummyScaler = FunctionTransformer(lambda x: x)
+    
+    selector = PowerShap(
+        model=make_pipeline(DummyScaler,RandomForestRegressor(n_estimators=25)), power_iterations=15, automatic=False
+    )
+
+    selector.fit(X, y)
+    selected_feats = selector.transform(X)
+
+    assert len(selected_feats.columns) >= n_informative
+    assert sum([c.startswith("informative") for c in selected_feats.columns]) == n_informative
+
+def test_pipeline_catboost_class_standardscaler_powershap(dummy_classification):
+    from sklearn.preprocessing import StandardScaler
+
+    X, y = dummy_classification
+    n_informative = sum([c.startswith("informative") for c in X.columns])
+    assert n_informative > 0, "No informative columns in the dummy data!"
+
+    selector = PowerShap(
+        model=make_pipeline(StandardScaler(), CatBoostClassifier(n_estimators=250, verbose=0)), power_iterations=15, automatic=False
+    )
+
+    selector.fit(X, y)
+    selected_feats = selector.transform(X)
+
+    assert len(selected_feats.columns) == n_informative
+    assert all([c.startswith("informative") for c in selected_feats.columns])
+
+def test_pipeline_catboost_class_maxabsscalerr_robustscaler_powershap(dummy_classification):
+    from sklearn.preprocessing import MaxAbsScaler, RobustScaler
+
+    X, y = dummy_classification
+    n_informative = sum([c.startswith("informative") for c in X.columns])
+    assert n_informative > 0, "No informative columns in the dummy data!"
+
+    selector = PowerShap(
+        model=make_pipeline(MaxAbsScaler(), RobustScaler(), CatBoostClassifier(n_estimators=250, verbose=0)), power_iterations=15, automatic=False
+    )
+
+    selector.fit(X, y)
+    selected_feats = selector.transform(X)
+
+    assert len(selected_feats.columns) == n_informative
+    assert all([c.startswith("informative") for c in selected_feats.columns])
diff --git a/tests/test_shap_explainer.py b/tests/test_shap_explainer.py
index 6ff0f05..af02623 100644
--- a/tests/test_shap_explainer.py
+++ b/tests/test_shap_explainer.py
@@ -5,11 +5,12 @@
 from powershap.shap_wrappers import ShapExplainerFactory
 from powershap.shap_wrappers.shap_explainer import (
     CatboostExplainer,
-    DeepLearningExplainer,
+    # DeepLearningExplainer,
     EnsembleExplainer,
     LGBMExplainer,
     LinearExplainer,
     XGBoostExplainer,
+    PipelineExplainer,
 )
 
 
@@ -110,12 +111,73 @@ def test_get_ensemble_explainer():
         explainer = ShapExplainerFactory.get_explainer(model_class())
         assert isinstance(explainer, EnsembleExplainer)
 
+def test_get_pipeline_explainer():
+    from sklearn.linear_model import (
+        LinearRegression,
+        LogisticRegression,
+        LogisticRegressionCV,
+        PassiveAggressiveClassifier,
+        Perceptron,
+        Ridge,
+        RidgeClassifier,
+        RidgeClassifierCV,
+        RidgeCV,
+        SGDClassifier,
+        SGDRegressor,
+    )
+    from sklearn.ensemble import (
+        ExtraTreesClassifier,
+        ExtraTreesRegressor,
+        GradientBoostingClassifier,
+        GradientBoostingRegressor,
+        RandomForestClassifier,
+        RandomForestRegressor,
+    )
+    from catboost import CatBoostClassifier, CatBoostRegressor
+    from lightgbm import LGBMClassifier, LGBMRegressor
+    from xgboost import XGBClassifier, XGBRegressor
+
+    model_classes = [
+        LogisticRegression,
+        LogisticRegressionCV,
+        PassiveAggressiveClassifier,
+        Perceptron,
+        RidgeClassifier,
+        RidgeClassifierCV,
+        SGDClassifier,
+        LinearRegression,
+        Ridge,
+        RidgeCV,
+        SGDRegressor,
+        RandomForestClassifier,
+        GradientBoostingClassifier,
+        ExtraTreesClassifier,
+        RandomForestRegressor,
+        GradientBoostingRegressor,
+        ExtraTreesRegressor,
+        XGBClassifier, XGBRegressor,
+        LGBMClassifier, LGBMRegressor,
+        CatBoostClassifier, CatBoostRegressor,
+    ]
+    from sklearn.pipeline import Pipeline
+    from sklearn.pipeline import make_pipeline
+    from sklearn.preprocessing import FunctionTransformer
+
+
+
+    for model_class in model_classes:
+        DummyScaler = FunctionTransformer(lambda x: x)
+        explainer = ShapExplainerFactory.get_explainer(make_pipeline(DummyScaler, model_class()))
+
+        assert isinstance(explainer, PipelineExplainer)
+
+
 
-def test_get_deep_learning_explainer():
-    import tensorflow as tf
+# def test_get_deep_learning_explainer():
+#     import tensorflow as tf
 
-    explainer = ShapExplainerFactory.get_explainer(tf.keras.Sequential())
-    assert isinstance(explainer, DeepLearningExplainer)
+#     explainer = ShapExplainerFactory.get_explainer(tf.keras.Sequential())
+#     assert isinstance(explainer, DeepLearningExplainer)
 
 
 def test_value_error_get_explainer():
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..4f3b5ad
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,1882 @@
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+    "python_full_version == '3.11.*'",
+    "python_full_version == '3.10.*'",
+    "python_full_version < '3.10'",
+]
+
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+    { name = "mypy-extensions" },
+    { name = "pathspec" },
+    { name = "platformdirs" },
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