diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 7bc6e7ad6..dc3621f0e 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -16,7 +16,7 @@ jobs:
           "3.9",
           "3.10",
           "3.11",
-          # "3.12", # Numba not available for Python 3.12: https://github.com/numba/numba/issues/9197
+          "3.12",
         ]
     runs-on: ${{ matrix.os }}
     steps:
diff --git a/pyproject.toml b/pyproject.toml
index 824739835..9e11af6aa 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "scikit-lego"
-version = "0.8.0"
+version = "0.8.1"
 description="A collection of lego bricks for scikit-learn pipelines"
 
 license = {file = "LICENSE"}
@@ -23,6 +23,7 @@ dependencies = [
     "pandas>=1.1.5",
     "scikit-learn>=1.0",
     "importlib-metadata >= 1.0; python_version < '3.8'",
+    "importlib-resources; python_version < '3.9'",
 ]
 
 classifiers = [
@@ -31,6 +32,7 @@ classifiers = [
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
     "License :: OSI Approved :: MIT License",
     "Topic :: Scientific/Engineering",
     "Topic :: Scientific/Engineering :: Artificial Intelligence",
diff --git a/sklego/datasets.py b/sklego/datasets.py
index f62f1dfa8..0fb0d9ffe 100644
--- a/sklego/datasets.py
+++ b/sklego/datasets.py
@@ -1,8 +1,14 @@
-import os
+import sys
 
 import numpy as np
 import pandas as pd
-from pkg_resources import resource_filename
+
+if sys.version_info >= (3, 9):
+    import importlib.resources as importlib_resources  # pragma: no cover
+else:
+    import importlib_resources as importlib_resources  # pragma: no cover
+
+
 from sklearn.datasets import fetch_openml
 
 
@@ -92,7 +98,7 @@ def load_penguins(return_X_y=False, as_frame=False):
 
         (Accessed 2020-06-08).
     """
-    filepath = resource_filename("sklego", os.path.join("data", "penguins.zip"))
+    filepath = importlib_resources.files("sklego") / "data" / "penguins.zip"
     df = pd.read_csv(filepath)
     if as_frame:
         return df
@@ -151,7 +157,7 @@ def load_arrests(return_X_y=False, as_frame=False):
 
     - Personal communication from Michael Friendly, York University.
     """
-    filepath = resource_filename("sklego", os.path.join("data", "arrests.zip"))
+    filepath = importlib_resources.files("sklego") / "data" / "arrests.zip"
     df = pd.read_csv(filepath)
     if as_frame:
         return df
@@ -198,7 +204,7 @@ def load_chicken(return_X_y=False, as_frame=False):
     - Crowder, M. and Hand, D. (1990), Analysis of Repeated Measures, Chapman and Hall (example 5.3)
     - Hand, D. and Crowder, M. (1996), Practical Longitudinal Data Analysis, Chapman and Hall (table A.2)
     """
-    filepath = resource_filename("sklego", os.path.join("data", "chickweight.zip"))
+    filepath = importlib_resources.files("sklego") / "data" / "chickweight.zip"
     df = pd.read_csv(filepath)
     if as_frame:
         return df
@@ -244,7 +250,7 @@ def load_abalone(return_X_y=False, as_frame=False):
 
         Sea Fisheries Division, Technical Report No. 48 (ISSN 1034-3288)
     """
-    filepath = resource_filename("sklego", os.path.join("data", "abalone.zip"))
+    filepath = importlib_resources.files("sklego") / "data" / "abalone.zip"
     df = pd.read_csv(filepath)
     if as_frame:
         return df
@@ -294,7 +300,7 @@ def load_heroes(return_X_y=False, as_frame=False):
     # Index(['name', 'attack_type', 'role', 'health', 'attack', 'attack_spd'], dtype='object')
     ```
     """
-    filepath = resource_filename("sklego", os.path.join("data", "heroes.zip"))
+    filepath = importlib_resources.files("sklego") / "data" / "heroes.zip"
     df = pd.read_csv(filepath)
     if as_frame:
         return df
@@ -351,7 +357,7 @@ def load_hearts(return_X_y=False, as_frame=False):
     The documentation of the dataset can be viewed at:
     https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/heart-disease.names
     """
-    filepath = resource_filename("sklego", os.path.join("data", "hearts.zip"))
+    filepath = importlib_resources.files("sklego") / "data" / "hearts.zip"
     df = pd.read_csv(filepath)
     if as_frame:
         return df