diff --git a/.github/workflows/ruff_action.yml b/.github/workflows/ruff_action.yml
new file mode 100644
index 00000000..15b907d0
--- /dev/null
+++ b/.github/workflows/ruff_action.yml
@@ -0,0 +1,8 @@
+name: Ruff
+on: [ push, pull_request ]
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/ruff-action@v2
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index e2030306..cb3bb7a5 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -14,12 +14,6 @@ repos:
           files: (^(src|tests)/)|(^[^/]*$)
           args: [--in-place, --remove-all-unused-imports, --recursive, --ignore-init-module-imports]
 
-    - repo: https://github.com/pycqa/isort
-      rev: 5.12.0
-      hooks:
-        - id: isort
-          args: ["--profile", "black", "--filter-files"]
-
     - repo: https://github.com/kynan/nbstripout
       rev: 0.6.0
       hooks:
@@ -31,13 +25,17 @@ repos:
       - id: flake8
         args: ["--count", "--select=E9,F63,F7,F82", "--show-source", "--statistics"]
 
-    - repo: https://github.com/psf/black
-      rev: 23.10.1
-      hooks:
-      - id: black-jupyter
-        language_version: python3.11
-        language: python
-        types: [python]
+    - repo: https://github.com/astral-sh/ruff-pre-commit
+      # Ruff version.
+      rev: v0.8.2
+      hooks:
+        # Run the linter.
+        - id: ruff
+          types_or: [ python, pyi, jupyter ]
+          args: [ --fix ]
+        # Run the formatter.
+        - id: ruff-format
+          types_or: [ python, pyi, jupyter ]
 
     - repo: local
       hooks:
diff --git a/docs/examples/example2_student_admission.py b/docs/examples/example2_student_admission.py
index 046227fb..ce31f37d 100644
--- a/docs/examples/example2_student_admission.py
+++ b/docs/examples/example2_student_admission.py
@@ -81,17 +81,13 @@
 
 """
 
-import sys
-
 import gurobipy as gp
 import gurobipy_pandas as gppd
 import numpy as np
 import pandas as pd
-from matplotlib import pyplot as plt
 from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import make_pipeline
 from sklearn.preprocessing import StandardScaler
-from sklearn.tree import DecisionTreeRegressor
 
 from gurobi_ml import add_predictor_constr
 
diff --git a/docs/examples/example4_price_optimization.py b/docs/examples/example4_price_optimization.py
index ee941661..cdafe8fa 100644
--- a/docs/examples/example4_price_optimization.py
+++ b/docs/examples/example4_price_optimization.py
@@ -75,14 +75,20 @@
 # the data.
 #
 
-import warnings
-
+import gurobipy as gp
+import gurobipy_pandas as gppd
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import seaborn as sns
-import sklearn
-from sklearn import tree
+from sklearn.compose import make_column_transformer
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import r2_score
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+
+from gurobi_ml import add_predictor_constr
 
 ######################################################################
 # The dataset from HAB contains sales data for the years 2019-2022. This
@@ -310,8 +316,6 @@ def peak_season(row):
 # weights using ``Scikit-learn``.
 #
 
-from sklearn.model_selection import train_test_split
-
 X = df[["region", "price", "year", "peak"]]
 y = df["units_sold"]
 # Split the data for training and testing
@@ -330,9 +334,6 @@ def peak_season(row):
 # ``make_column_transformer``.
 #
 
-from sklearn.compose import make_column_transformer
-from sklearn.preprocessing import OneHotEncoder, StandardScaler
-
 feat_transform = make_column_transformer(
     (OneHotEncoder(drop="first"), ["region"]),
     (StandardScaler(), ["price", "year"]),
@@ -349,10 +350,6 @@ def peak_season(row):
 # Define it and train it.
 #
 
-from sklearn.linear_model import LinearRegression
-from sklearn.metrics import r2_score
-from sklearn.pipeline import make_pipeline
-
 lin_reg = make_pipeline(feat_transform, LinearRegression())
 lin_reg.fit(X_train, y_train)
 
@@ -497,9 +494,6 @@ def peak_season(row):
 # dataframe.
 #
 
-import gurobipy as gp
-import gurobipy_pandas as gppd
-
 m = gp.Model("Avocado_Price_Allocation")
 
 p = gppd.add_vars(m, data, name="price", lb=a_min, ub=a_max)
@@ -655,8 +649,6 @@ def peak_season(row):
 # to insert the constraints linking the features and the demand.
 #
 
-from gurobi_ml import add_predictor_constr
-
 pred_constr = add_predictor_constr(m, lin_reg, feats, d)
 
 pred_constr.print_stats()
diff --git a/docs/examples_userguide/example_simple.py b/docs/examples_userguide/example_simple.py
index 87a92b2e..60a2ae49 100644
--- a/docs/examples_userguide/example_simple.py
+++ b/docs/examples_userguide/example_simple.py
@@ -23,7 +23,6 @@
 import gurobipy as gp
 import numpy as np
 from sklearn.datasets import make_regression
-from sklearn.metrics import mean_squared_error
 from sklearn.neural_network import MLPRegressor
 
 from gurobi_ml import add_predictor_constr
diff --git a/notebooks/Functions_approx/2DPeakFunction skorch polynomials.ipynb b/notebooks/Functions_approx/2DPeakFunction skorch polynomials.ipynb
index 3099abdb..392ed16f 100644
--- a/notebooks/Functions_approx/2DPeakFunction skorch polynomials.ipynb	
+++ b/notebooks/Functions_approx/2DPeakFunction skorch polynomials.ipynb	
@@ -112,6 +112,8 @@
     "# Create a small class to apply polynomial features and convert\n",
     "\n",
     "from sklearn.base import BaseEstimator, TransformerMixin\n",
+    "\n",
+    "\n",
     "class MyPolynomialFeatures(BaseEstimator, TransformerMixin):\n",
     "    def __init__(self):\n",
     "        self.poly_feat = PolynomialFeatures()\n",
@@ -136,12 +138,12 @@
     "hs = 16\n",
     "nn_regression = NeuralNetRegressor(\n",
     "    torch.nn.Sequential(\n",
-    "    torch.nn.Linear(6, hs),\n",
-    "    torch.nn.ReLU(),\n",
-    "    torch.nn.Linear(hs, hs),\n",
-    "    torch.nn.ReLU(),\n",
-    "    torch.nn.Linear(hs, 1),\n",
-    "),\n",
+    "        torch.nn.Linear(6, hs),\n",
+    "        torch.nn.ReLU(),\n",
+    "        torch.nn.Linear(hs, hs),\n",
+    "        torch.nn.ReLU(),\n",
+    "        torch.nn.Linear(hs, 1),\n",
+    "    ),\n",
     "    max_epochs=20,\n",
     "    lr=0.1,\n",
     "    iterator_train__shuffle=True,\n",
@@ -246,7 +248,10 @@
     "#   the function to add a pytorch model on the pytorch model\n",
     "# - Register that function by associating it to the NeuralNetRegressor class\n",
     "def add_skorch_constr(gp_model, skorch_model, input_vars, output_vars=None, **kwargs):\n",
-    "    return add_sequential_constr(gp_model, skorch_model.module, input_vars, output_vars, **kwargs)\n",
+    "    return add_sequential_constr(\n",
+    "        gp_model, skorch_model.module, input_vars, output_vars, **kwargs\n",
+    "    )\n",
+    "\n",
     "\n",
     "register_predictor_constr(NeuralNetRegressor, add_skorch_constr)"
    ]
@@ -260,7 +265,10 @@
    "source": [
     "# Now do the same for the polynomial features\n",
     "def add_my_polynomial_features_constr(gp_model, poly_feat, input_vars, **kwargs):\n",
-    "    return add_polynomial_features_constr(gp_model, poly_feat.poly_feat, input_vars, **kwargs)\n",
+    "    return add_polynomial_features_constr(\n",
+    "        gp_model, poly_feat.poly_feat, input_vars, **kwargs\n",
+    "    )\n",
+    "\n",
     "\n",
     "register_predictor_constr(MyPolynomialFeatures, add_my_polynomial_features_constr)"
    ]
diff --git a/notebooks/Functions_approx/2DPeakFunction skorch.ipynb b/notebooks/Functions_approx/2DPeakFunction skorch.ipynb
index 60df2652..bdf27c97 100644
--- a/notebooks/Functions_approx/2DPeakFunction skorch.ipynb	
+++ b/notebooks/Functions_approx/2DPeakFunction skorch.ipynb	
@@ -32,12 +32,9 @@
     "from matplotlib import cm\n",
     "from matplotlib import pyplot as plt\n",
     "from sklearn import metrics\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import PolynomialFeatures\n",
     "\n",
     "from gurobi_ml import add_predictor_constr, register_predictor_constr\n",
     "\n",
-    "from gurobi_ml.sklearn import add_polynomial_features_constr\n",
     "from gurobi_ml.torch import add_sequential_constr"
    ]
   },
@@ -106,12 +103,12 @@
     "hs = 30\n",
     "nn_regression = NeuralNetRegressor(\n",
     "    torch.nn.Sequential(\n",
-    "    torch.nn.Linear(2, hs),\n",
-    "    torch.nn.ReLU(),\n",
-    "    torch.nn.Linear(hs, hs),\n",
-    "    torch.nn.ReLU(),\n",
-    "    torch.nn.Linear(hs, 1),\n",
-    "),\n",
+    "        torch.nn.Linear(2, hs),\n",
+    "        torch.nn.ReLU(),\n",
+    "        torch.nn.Linear(hs, hs),\n",
+    "        torch.nn.ReLU(),\n",
+    "        torch.nn.Linear(hs, 1),\n",
+    "    ),\n",
     "    max_epochs=20,\n",
     "    lr=0.1,\n",
     "    iterator_train__shuffle=True,\n",
@@ -214,7 +211,10 @@
     "#   the function to add a pytorch model on the pytorch model\n",
     "# - Register that function by associating it to the NeuralNetRegressor class\n",
     "def add_skorch_constr(gp_model, skorch_model, input_vars, output_vars=None, **kwargs):\n",
-    "    return add_sequential_constr(gp_model, skorch_model.module, input_vars, output_vars, **kwargs)\n",
+    "    return add_sequential_constr(\n",
+    "        gp_model, skorch_model.module, input_vars, output_vars, **kwargs\n",
+    "    )\n",
+    "\n",
     "\n",
     "register_predictor_constr(NeuralNetRegressor, add_skorch_constr)"
    ]
diff --git a/notebooks/Functions_approx/Parabola.ipynb b/notebooks/Functions_approx/Parabola.ipynb
index 39717e0a..fdd48260 100644
--- a/notebooks/Functions_approx/Parabola.ipynb
+++ b/notebooks/Functions_approx/Parabola.ipynb
@@ -276,9 +276,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(\n",
-    "    f\"Maximal error in predicted values in solution {np.max(pconstr.get_error())}\"\n",
-    ")"
+    "print(f\"Maximal error in predicted values in solution {np.max(pconstr.get_error())}\")"
    ]
   },
   {
diff --git a/notebooks/Janos/Decision Tree.ipynb b/notebooks/Janos/Decision Tree.ipynb
index a7e3d09a..aefe2b4b 100644
--- a/notebooks/Janos/Decision Tree.ipynb	
+++ b/notebooks/Janos/Decision Tree.ipynb	
@@ -135,10 +135,10 @@
    "source": [
     "m = gp.Model()\n",
     "\n",
-    "y = gppd.add_vars(m, studentsdata, name='enroll_probability')\n",
+    "y = gppd.add_vars(m, studentsdata, name=\"enroll_probability\")\n",
     "\n",
     "# Add variable for merit\n",
-    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name='merit')\n",
+    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name=\"merit\")\n",
     "\n",
     "# Keep only features\n",
     "studentsdata = studentsdata.loc[:, features]\n",
diff --git a/notebooks/Janos/Gradient Boosting Regression.ipynb b/notebooks/Janos/Gradient Boosting Regression.ipynb
index 12d9ea72..06e1ceab 100644
--- a/notebooks/Janos/Gradient Boosting Regression.ipynb	
+++ b/notebooks/Janos/Gradient Boosting Regression.ipynb	
@@ -131,10 +131,10 @@
    "source": [
     "m = gp.Model()\n",
     "\n",
-    "y = gppd.add_vars(m, studentsdata, name='enroll_probability')\n",
+    "y = gppd.add_vars(m, studentsdata, name=\"enroll_probability\")\n",
     "\n",
     "# Add variable for merit\n",
-    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name='merit')\n",
+    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name=\"merit\")\n",
     "\n",
     "# Keep only features\n",
     "studentsdata = studentsdata.loc[:, features]\n",
diff --git a/notebooks/Janos/LightGBM.ipynb b/notebooks/Janos/LightGBM.ipynb
index af941411..1cde8137 100644
--- a/notebooks/Janos/LightGBM.ipynb
+++ b/notebooks/Janos/LightGBM.ipynb
@@ -23,12 +23,10 @@
    "source": [
     "import gurobipy as gp\n",
     "import numpy as np\n",
-    "import math\n",
     "import pandas as pd\n",
     "import gurobipy_pandas as gppd\n",
     "from matplotlib import pyplot as plt\n",
-    "import lightgbm as lgb\n",
-    "from sklearn.metrics import r2_score"
+    "import lightgbm as lgb"
    ]
   },
   {
@@ -41,7 +39,8 @@
     "%load_ext autoreload\n",
     "%autoreload 2\n",
     "import sys\n",
-    "sys.path.append('../../src')"
+    "\n",
+    "sys.path.append(\"../../src\")"
    ]
   },
   {
@@ -52,8 +51,7 @@
    "outputs": [],
    "source": [
     "from gurobi_ml import add_predictor_constr\n",
-    "from gurobi_ml import lightgbm\n",
-    "import gurobi_ml as gml"
+    "from gurobi_ml import lightgbm"
    ]
   },
   {
@@ -167,10 +165,10 @@
    "source": [
     "m = gp.Model()\n",
     "\n",
-    "y = gppd.add_vars(m, studentsdata, lb=-1e100, name='enroll_probability')\n",
+    "y = gppd.add_vars(m, studentsdata, lb=-1e100, name=\"enroll_probability\")\n",
     "\n",
     "# Add variable for merit\n",
-    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name='merit')\n",
+    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name=\"merit\")\n",
     "\n",
     "# Keep only features\n",
     "studentsdata = studentsdata.loc[:, features]\n",
@@ -255,7 +253,9 @@
     "pred_constr.remove()\n",
     "\n",
     "# Add new constraint setting epsilon to 1e-5\n",
-    "pred_constr = lightgbm.add_lgbmregressor_constr(m, regression, studentsdata, y, epsilon=1e-4)\n",
+    "pred_constr = lightgbm.add_lgbmregressor_constr(\n",
+    "    m, regression, studentsdata, y, epsilon=1e-4\n",
+    ")\n",
     "\n",
     "pred_constr.print_stats()\n",
     "\n",
diff --git a/notebooks/Janos/Random Forest.ipynb b/notebooks/Janos/Random Forest.ipynb
index fdda5129..d389e0dd 100644
--- a/notebooks/Janos/Random Forest.ipynb	
+++ b/notebooks/Janos/Random Forest.ipynb	
@@ -130,10 +130,10 @@
    "source": [
     "m = gp.Model()\n",
     "\n",
-    "y = gppd.add_vars(m, studentsdata, name='enroll_probability')\n",
+    "y = gppd.add_vars(m, studentsdata, name=\"enroll_probability\")\n",
     "\n",
     "# Add variable for merit\n",
-    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name='merit')\n",
+    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name=\"merit\")\n",
     "\n",
     "# Keep only features\n",
     "studentsdata = studentsdata.loc[:, features]\n",
diff --git a/notebooks/Janos/XGBoost.ipynb b/notebooks/Janos/XGBoost.ipynb
index 80dd9363..4294b4f7 100644
--- a/notebooks/Janos/XGBoost.ipynb
+++ b/notebooks/Janos/XGBoost.ipynb
@@ -23,12 +23,10 @@
    "source": [
     "import gurobipy as gp\n",
     "import numpy as np\n",
-    "import math\n",
     "import pandas as pd\n",
     "import gurobipy_pandas as gppd\n",
     "from matplotlib import pyplot as plt\n",
     "import xgboost as xgb\n",
-    "from sklearn.metrics import r2_score\n",
     "\n",
     "from gurobi_ml import add_predictor_constr"
    ]
@@ -84,7 +82,9 @@
    "source": [
     "# Run our regression\n",
     "n_estimators = 20\n",
-    "regression = xgb.XGBRegressor(n_estimators=n_estimators, max_depth=5, random_state=1, booster=\"gbtree\")\n",
+    "regression = xgb.XGBRegressor(\n",
+    "    n_estimators=n_estimators, max_depth=5, random_state=1, booster=\"gbtree\"\n",
+    ")\n",
     "\n",
     "regression.fit(X=historical_data.loc[:, features], y=historical_data.loc[:, target])"
    ]
@@ -135,10 +135,10 @@
    "source": [
     "m = gp.Model()\n",
     "\n",
-    "y = gppd.add_vars(m, studentsdata, name='enroll_probability')\n",
+    "y = gppd.add_vars(m, studentsdata, name=\"enroll_probability\")\n",
     "\n",
     "# Add variable for merit\n",
-    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name='merit')\n",
+    "studentsdata = studentsdata.gppd.add_vars(m, lb=0.0, ub=2.5, name=\"merit\")\n",
     "\n",
     "# Keep only features\n",
     "studentsdata = studentsdata.loc[:, features]\n",
diff --git a/notebooks/adversarial/adversarial_pytorch.ipynb b/notebooks/adversarial/adversarial_pytorch.ipynb
index abef74fd..e2efe4b9 100644
--- a/notebooks/adversarial/adversarial_pytorch.ipynb
+++ b/notebooks/adversarial/adversarial_pytorch.ipynb
@@ -35,7 +35,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "from matplotlib import pyplot as plt\n",
     "\n",
     "import torch\n",
diff --git a/notebooks/price-optimization/Compare Models.ipynb b/notebooks/price-optimization/Compare Models.ipynb
index 821fb31d..d458de23 100644
--- a/notebooks/price-optimization/Compare Models.ipynb	
+++ b/notebooks/price-optimization/Compare Models.ipynb	
@@ -27,7 +27,6 @@
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "import numpy as np\n",
     "\n",
     "import gurobipy as gp\n",
     "from gurobipy import GRB\n",
@@ -125,7 +124,7 @@
     "    (StandardScaler(), [\"price\"]),\n",
     "    (\"passthrough\", [\"peak\"]),\n",
     "    verbose_feature_names_out=False,\n",
-    "    remainder='drop'\n",
+    "    remainder=\"drop\",\n",
     ")\n",
     "\n",
     "\n",
@@ -194,19 +193,27 @@
     "from sklearn.base import clone\n",
     "from xgboost import XGBRegressor\n",
     "from time import time\n",
-    "args={\"random_state\":1}\n",
-    "regressions = {\"Linear Regression\": {\"regressor\":LinearRegression() },\n",
-    "               \"MLP Regression\": {\"regressor\": MLPRegressor([8]*2, max_iter=1000, **args)},\n",
-    "               \"Decision Tree\": {\"regressor\": DecisionTreeRegressor(max_leaf_nodes=50, **args)},\n",
-    "               \"Random Forest\": {\"regressor\": RandomForestRegressor(n_estimators=10, max_leaf_nodes=100, **args)},\n",
-    "               \"Gradient Boosting\":\n",
-    "               {\"regressor\" : GradientBoostingRegressor(n_estimators=20, **args)},\n",
-    "              \"XGB Regressor\": {\"regressor\": XGBRegressor(n_estimators=20, **args)}}\n",
+    "\n",
+    "args = {\"random_state\": 1}\n",
+    "regressions = {\n",
+    "    \"Linear Regression\": {\"regressor\": LinearRegression()},\n",
+    "    \"MLP Regression\": {\"regressor\": MLPRegressor([8] * 2, max_iter=1000, **args)},\n",
+    "    \"Decision Tree\": {\"regressor\": DecisionTreeRegressor(max_leaf_nodes=50, **args)},\n",
+    "    \"Random Forest\": {\n",
+    "        \"regressor\": RandomForestRegressor(n_estimators=10, max_leaf_nodes=100, **args)\n",
+    "    },\n",
+    "    \"Gradient Boosting\": {\n",
+    "        \"regressor\": GradientBoostingRegressor(n_estimators=20, **args)\n",
+    "    },\n",
+    "    \"XGB Regressor\": {\"regressor\": XGBRegressor(n_estimators=20, **args)},\n",
+    "}\n",
     "\n",
     "# Add polynomial features for linear regression and MLP\n",
     "regressions_poly = {}\n",
     "for regression in [\"Linear Regression\", \"MLP Regression\"]:\n",
-    "    data = {\"regressor\": (PolynomialFeatures(),clone(regressions[regression][\"regressor\"]))}\n",
+    "    data = {\n",
+    "        \"regressor\": (PolynomialFeatures(), clone(regressions[regression][\"regressor\"]))\n",
+    "    }\n",
     "    regressions_poly[f\"{regression} polynomial feats\"] = data\n",
     "# Merge dictionary of polynomial features\n",
     "regressions |= regressions_poly"
@@ -230,14 +237,12 @@
     "for regression, data in regressions.items():\n",
     "    regressor = data[\"regressor\"]\n",
     "    if isinstance(regressor, tuple):\n",
-    "        lin_reg = make_pipeline(feat_transform,\n",
-    "                                *regressor)\n",
+    "        lin_reg = make_pipeline(feat_transform, *regressor)\n",
     "    else:\n",
-    "        lin_reg = make_pipeline(feat_transform,\n",
-    "                                regressor)\n",
+    "        lin_reg = make_pipeline(feat_transform, regressor)\n",
     "    train_start = time()\n",
     "    lin_reg.fit(X_train, y_train)\n",
-    "    data[(\"Learning\",\"time\")] = time() - train_start\n",
+    "    data[(\"Learning\", \"time\")] = time() - train_start\n",
     "    data[\"pipeline\"] = lin_reg\n",
     "\n",
     "    # Get R^2 from test data\n",
@@ -247,7 +252,9 @@
     "    r2_train = r2_score(y_train, y_pred)\n",
     "    data[(\"Learning\", \"R2 test\")] = r2_test\n",
     "    data[(\"Learning\", \"R2 train\")] = r2_train\n",
-    "    print(f\"{regression:<18} R^2 value in the test set is {r2_test:.3f} training {r2_train:.3f}\")"
+    "    print(\n",
+    "        f\"{regression:<18} R^2 value in the test set is {r2_test:.3f} training {r2_train:.3f}\"\n",
+    "    )"
    ]
   },
   {
@@ -282,7 +289,8 @@
     "        \"Southeast\": 0.2,\n",
     "        \"West\": 0.2,\n",
     "        \"Plains\": 0.2,\n",
-    "    }, name='transport_cost'\n",
+    "    },\n",
+    "    name=\"transport_cost\",\n",
     ")\n",
     "\n",
     "c_transport = c_transport.loc[regions]\n",
@@ -292,9 +300,14 @@
     "a_min = 0  # minimum avocado price in each region\n",
     "a_max = 2  # maximum avocado price in each region\n",
     "\n",
-    "data = pd.concat([c_transport,\n",
-    "                  df.groupby(\"region\")[\"units_sold\"].min().rename('min_delivery'),\n",
-    "                  df.groupby(\"region\")[\"units_sold\"].max().rename('max_delivery')], axis=1)"
+    "data = pd.concat(\n",
+    "    [\n",
+    "        c_transport,\n",
+    "        df.groupby(\"region\")[\"units_sold\"].min().rename(\"min_delivery\"),\n",
+    "        df.groupby(\"region\")[\"units_sold\"].max().rename(\"max_delivery\"),\n",
+    "    ],\n",
+    "    axis=1,\n",
+    ")"
    ]
   },
   {
@@ -317,8 +330,8 @@
     "m = gp.Model(\"Avocado_Price_Allocation\")\n",
     "\n",
     "p = gppd.add_vars(m, data, name=\"price\", lb=a_min, ub=a_max)\n",
-    "d = gppd.add_vars(m, data, name=\"demand\") # Add variables for the regression\n",
-    "w = m.addVar(name=\"w\") # excess wasteage\n",
+    "d = gppd.add_vars(m, data, name=\"demand\")  # Add variables for the regression\n",
+    "w = m.addVar(name=\"w\")  # excess wasteage\n",
     "m.update()\n",
     "\n",
     "m.setObjective((p * d).sum() - c_waste * w - (c_transport * d).sum())\n",
@@ -336,12 +349,7 @@
    "outputs": [],
    "source": [
     "feats = pd.DataFrame(\n",
-    "    data={\n",
-    "        \"peak\": peak_or_not,\n",
-    "        \"region\": regions,\n",
-    "        \"price\": p\n",
-    "    },\n",
-    "    index=regions\n",
+    "    data={\"peak\": peak_or_not, \"region\": regions, \"price\": p}, index=regions\n",
     ")\n",
     "feats = feats[[\"region\", \"price\", \"peak\"]]"
    ]
@@ -369,10 +377,10 @@
     "        data[(\"Optimization\", \"value\")] = m.ObjVal\n",
     "        data[(\"Optimization\", \"viol\")] = m.MaxVio\n",
     "        data[(\"Optimization\", \"error\")] = pred_constr.get_error().max()\n",
-    "    except:\n",
-    "        data[(\"Optimization\", \"value\")] = float('nan')\n",
-    "        data[(\"Optimization\", \"viol\")] = float('nan')\n",
-    "        data[(\"Optimization\", \"error\")] = float('nan')\n",
+    "    except gp.GurobiError:\n",
+    "        data[(\"Optimization\", \"value\")] = float(\"nan\")\n",
+    "        data[(\"Optimization\", \"viol\")] = float(\"nan\")\n",
+    "        data[(\"Optimization\", \"error\")] = float(\"nan\")\n",
     "        break\n",
     "        pass\n",
     "    pred_constr.remove()"
@@ -385,7 +393,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "res = pd.DataFrame.from_dict(regressions, orient='index').drop([\"regressor\", \"pipeline\"], axis=1)"
+    "res = pd.DataFrame.from_dict(regressions, orient=\"index\").drop(\n",
+    "    [\"regressor\", \"pipeline\"], axis=1\n",
+    ")"
    ]
   },
   {
@@ -395,7 +405,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "res.columns = pd.MultiIndex.from_tuples(res.columns)\n"
+    "res.columns = pd.MultiIndex.from_tuples(res.columns)"
    ]
   },
   {
diff --git a/src/gurobi_ml/__init__.py b/src/gurobi_ml/__init__.py
index deada134..08c3ac2a 100644
--- a/src/gurobi_ml/__init__.py
+++ b/src/gurobi_ml/__init__.py
@@ -30,9 +30,11 @@
 
 from gurobipy import gurobi
 
-from ._version import __version__
-from .add_predictor import add_predictor_constr
-from .register_user_predictor import register_predictor_constr
+from ._version import __version__ as __version__
+from .add_predictor import add_predictor_constr as add_predictor_constr
+from .register_user_predictor import (
+    register_predictor_constr as register_predictor_constr,
+)
 
 MIN_GRB_VERSION = 10
 if gurobi.version()[0] < MIN_GRB_VERSION:
diff --git a/src/gurobi_ml/_version.py b/src/gurobi_ml/_version.py
index 596d4b7b..974d6bdd 100644
--- a/src/gurobi_ml/_version.py
+++ b/src/gurobi_ml/_version.py
@@ -14,6 +14,7 @@
 # ==============================================================================
 
 """To get version and git hash of built package."""
+
 from importlib import metadata
 
 try:
diff --git a/src/gurobi_ml/keras/__init__.py b/src/gurobi_ml/keras/__init__.py
index 13dd2f06..046acf9e 100644
--- a/src/gurobi_ml/keras/__init__.py
+++ b/src/gurobi_ml/keras/__init__.py
@@ -13,4 +13,5 @@
 # limitations under the License.
 # ==============================================================================
 
-from .keras import KerasNetworkConstr, add_keras_constr
+from .keras import KerasNetworkConstr as KerasNetworkConstr
+from .keras import add_keras_constr as add_keras_constr
diff --git a/src/gurobi_ml/lightgbm/__init__.py b/src/gurobi_ml/lightgbm/__init__.py
index 3d56a89f..b46f3487 100644
--- a/src/gurobi_ml/lightgbm/__init__.py
+++ b/src/gurobi_ml/lightgbm/__init__.py
@@ -13,4 +13,5 @@
 # limitations under the License.
 # ==============================================================================
 
-from .lgbm_regressor import add_lgbm_booster_constr, add_lgbmregressor_constr
+from .lgbm_regressor import add_lgbm_booster_constr as add_lgbm_booster_constr
+from .lgbm_regressor import add_lgbmregressor_constr as add_lgbmregressor_constr
diff --git a/src/gurobi_ml/lightgbm/lgbm_regressor.py b/src/gurobi_ml/lightgbm/lgbm_regressor.py
index a06e64a1..26a321a7 100644
--- a/src/gurobi_ml/lightgbm/lgbm_regressor.py
+++ b/src/gurobi_ml/lightgbm/lgbm_regressor.py
@@ -18,7 +18,6 @@
 into a :external+gurobi:py:class:`Model`.
 """
 
-
 import numpy as np
 from gurobipy import GRB
 
diff --git a/src/gurobi_ml/lightgbm_sklearn_api.py b/src/gurobi_ml/lightgbm_sklearn_api.py
index 56958738..aed3a317 100644
--- a/src/gurobi_ml/lightgbm_sklearn_api.py
+++ b/src/gurobi_ml/lightgbm_sklearn_api.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 
-""" Convertor for LigthGBM's Scikit Learn compatible object
+"""Convertor for LigthGBM's Scikit Learn compatible object
 
 This is to be able to use it in pipelines.
 """
diff --git a/src/gurobi_ml/modeling/__init__.py b/src/gurobi_ml/modeling/__init__.py
index c0d66fc1..eaeda8d1 100644
--- a/src/gurobi_ml/modeling/__init__.py
+++ b/src/gurobi_ml/modeling/__init__.py
@@ -13,4 +13,4 @@
 # limitations under the License.
 # ==============================================================================
 
-from .base_predictor_constr import AbstractPredictorConstr
+from .base_predictor_constr import AbstractPredictorConstr as AbstractPredictorConstr
diff --git a/src/gurobi_ml/modeling/decision_tree/__init__.py b/src/gurobi_ml/modeling/decision_tree/__init__.py
index 5f01f37a..f552f8e4 100644
--- a/src/gurobi_ml/modeling/decision_tree/__init__.py
+++ b/src/gurobi_ml/modeling/decision_tree/__init__.py
@@ -13,4 +13,4 @@
 # limitations under the License.
 # ==============================================================================
 
-from .decision_tree_model import AbstractTreeEstimator
+from .decision_tree_model import AbstractTreeEstimator as AbstractTreeEstimator
diff --git a/src/gurobi_ml/modeling/decision_tree/decision_tree_model.py b/src/gurobi_ml/modeling/decision_tree/decision_tree_model.py
index c8f1e7cb..b0421583 100644
--- a/src/gurobi_ml/modeling/decision_tree/decision_tree_model.py
+++ b/src/gurobi_ml/modeling/decision_tree/decision_tree_model.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 
-""" Utilities for modeling decision trees """
+"""Utilities for modeling decision trees"""
 
 from warnings import warn
 
diff --git a/src/gurobi_ml/modeling/neuralnet/__init__.py b/src/gurobi_ml/modeling/neuralnet/__init__.py
index d9054278..ef9e9611 100644
--- a/src/gurobi_ml/modeling/neuralnet/__init__.py
+++ b/src/gurobi_ml/modeling/neuralnet/__init__.py
@@ -13,4 +13,4 @@
 # limitations under the License.
 # ==============================================================================
 
-from .neural_net import BaseNNConstr
+from .neural_net import BaseNNConstr as BaseNNConstr
diff --git a/src/gurobi_ml/modeling/neuralnet/neural_net.py b/src/gurobi_ml/modeling/neuralnet/neural_net.py
index 8869b497..dc70e39c 100644
--- a/src/gurobi_ml/modeling/neuralnet/neural_net.py
+++ b/src/gurobi_ml/modeling/neuralnet/neural_net.py
@@ -15,7 +15,6 @@
 
 """Bases classes for modeling neural network layers."""
 
-
 from .._var_utils import _default_name
 from ..base_predictor_constr import AbstractPredictorConstr
 from .activations import Identity, ReLU
diff --git a/src/gurobi_ml/register_user_predictor.py b/src/gurobi_ml/register_user_predictor.py
index 77129a0c..7d81b903 100644
--- a/src/gurobi_ml/register_user_predictor.py
+++ b/src/gurobi_ml/register_user_predictor.py
@@ -15,6 +15,7 @@
 
 
 """Module for user to register a predictor for the function add_predictor_constr."""
+
 USER_PREDICTORS = {}
 
 
diff --git a/src/gurobi_ml/registered_predictors.py b/src/gurobi_ml/registered_predictors.py
index 51adbb58..30d9f2cd 100644
--- a/src/gurobi_ml/registered_predictors.py
+++ b/src/gurobi_ml/registered_predictors.py
@@ -14,6 +14,7 @@
 # ==============================================================================
 
 """Define generic function that can add any known trained predictor."""
+
 import sys
 
 from .register_user_predictor import user_predictors
@@ -78,7 +79,6 @@ def lightgbm_convertors():
         import lightgbm as lgb  # pylint: disable=import-outside-toplevel
 
         from .lightgbm import (  # pylint: disable=import-outside-toplevel
-            add_lgbm_booster_constr,
             add_lgbmregressor_constr,
         )
 
diff --git a/src/gurobi_ml/sklearn/__init__.py b/src/gurobi_ml/sklearn/__init__.py
index e873fce0..1c7bc20a 100644
--- a/src/gurobi_ml/sklearn/__init__.py
+++ b/src/gurobi_ml/sklearn/__init__.py
@@ -13,18 +13,30 @@
 # limitations under the License.
 # ==============================================================================
 
-from .column_transformer import add_column_transformer_constr
-from .decision_tree_regressor import add_decision_tree_regressor_constr
-from .gradient_boosting_regressor import add_gradient_boosting_regressor_constr
-from .linear_regression import add_linear_regression_constr
-from .logistic_regression import add_logistic_regression_constr
-from .mlpregressor import add_mlp_regressor_constr
-from .pipeline import add_pipeline_constr
-from .pls_regression import add_pls_regression_constr
-from .predictors_list import sklearn_predictors
+from .column_transformer import (
+    add_column_transformer_constr as add_column_transformer_constr,
+)
+from .decision_tree_regressor import (
+    add_decision_tree_regressor_constr as add_decision_tree_regressor_constr,
+)
+from .gradient_boosting_regressor import (
+    add_gradient_boosting_regressor_constr as add_gradient_boosting_regressor_constr,
+)
+from .linear_regression import (
+    add_linear_regression_constr as add_linear_regression_constr,
+)
+from .logistic_regression import (
+    add_logistic_regression_constr as add_logistic_regression_constr,
+)
+from .mlpregressor import add_mlp_regressor_constr as add_mlp_regressor_constr
+from .pipeline import add_pipeline_constr as add_pipeline_constr
+from .pls_regression import add_pls_regression_constr as add_pls_regression_constr
+from .predictors_list import sklearn_predictors as sklearn_predictors
 from .preprocessing import (
-    add_polynomial_features_constr,
-    add_standard_scaler_constr,
-    sklearn_transformers,
+    add_polynomial_features_constr as add_polynomial_features_constr,
+)
+from .preprocessing import add_standard_scaler_constr as add_standard_scaler_constr
+from .preprocessing import sklearn_transformers as sklearn_transformers
+from .random_forest_regressor import (
+    add_random_forest_regressor_constr as add_random_forest_regressor_constr,
 )
-from .random_forest_regressor import add_random_forest_regressor_constr
diff --git a/src/gurobi_ml/sklearn/base_regressions.py b/src/gurobi_ml/sklearn/base_regressions.py
index bad7cf81..7fbec281 100644
--- a/src/gurobi_ml/sklearn/base_regressions.py
+++ b/src/gurobi_ml/sklearn/base_regressions.py
@@ -21,7 +21,6 @@
    - :external+sklearn:py:class:`sklearn.linear_model.Lasso`
 """
 
-
 from ..modeling import AbstractPredictorConstr
 from .skgetter import SKgetter
 
diff --git a/src/gurobi_ml/sklearn/column_transformer.py b/src/gurobi_ml/sklearn/column_transformer.py
index 69f6267e..d24503d3 100644
--- a/src/gurobi_ml/sklearn/column_transformer.py
+++ b/src/gurobi_ml/sklearn/column_transformer.py
@@ -78,7 +78,7 @@ def _mip_model(self, **kwargs):
         """Do the transformation on x."""
         column_transform = self.transformer
         _input = self._input
-        transformers = {k.lower(): v for k, v in sklearn_transformers().items()}
+        {k.lower(): v for k, v in sklearn_transformers().items()}
         transformed = []
         for name, trans, cols in column_transform.transformers_:
             if len(cols) == 0:
diff --git a/src/gurobi_ml/sklearn/decision_tree_regressor.py b/src/gurobi_ml/sklearn/decision_tree_regressor.py
index db3dbdc9..cdcffa61 100644
--- a/src/gurobi_ml/sklearn/decision_tree_regressor.py
+++ b/src/gurobi_ml/sklearn/decision_tree_regressor.py
@@ -18,7 +18,6 @@
 in a :external+gurobi:py:class:`Model`.
 """
 
-
 from ..modeling.decision_tree import AbstractTreeEstimator
 from .skgetter import SKgetter
 
diff --git a/src/gurobi_ml/sklearn/mlpregressor.py b/src/gurobi_ml/sklearn/mlpregressor.py
index 745a31e2..bfaf863d 100644
--- a/src/gurobi_ml/sklearn/mlpregressor.py
+++ b/src/gurobi_ml/sklearn/mlpregressor.py
@@ -16,6 +16,7 @@
 """Module for formulating a :external+sklearn:py:class:`sklearn.neural_network.MLPRegressor` in a
 :external+gurobi:py:class:`Model`.
 """
+
 from ..exceptions import NoModel
 from ..modeling.neuralnet import BaseNNConstr
 from .skgetter import SKgetter
diff --git a/src/gurobi_ml/sklearn/pipeline.py b/src/gurobi_ml/sklearn/pipeline.py
index 33a4a391..0830c518 100644
--- a/src/gurobi_ml/sklearn/pipeline.py
+++ b/src/gurobi_ml/sklearn/pipeline.py
@@ -17,7 +17,6 @@
 in a :external+gurobi:py:class:`Model`.
 """
 
-
 from ..lightgbm_sklearn_api import lightgbm_sklearn_convertors
 from ..modeling.base_predictor_constr import AbstractPredictorConstr
 from ..modeling.get_convertor import get_convertor
diff --git a/src/gurobi_ml/sklearn/pls_regression.py b/src/gurobi_ml/sklearn/pls_regression.py
index 6c9af64f..a7b67cd0 100644
--- a/src/gurobi_ml/sklearn/pls_regression.py
+++ b/src/gurobi_ml/sklearn/pls_regression.py
@@ -15,7 +15,6 @@
 
 """Module for formulating :external+sklearn:py:class:`sklearn.cross_decomposition.PLSRegression` in a gurobipy model."""
 
-
 from ..modeling import AbstractPredictorConstr
 from .skgetter import SKgetter
 
diff --git a/src/gurobi_ml/torch/__init__.py b/src/gurobi_ml/torch/__init__.py
index 8656280e..4d7fc1bc 100644
--- a/src/gurobi_ml/torch/__init__.py
+++ b/src/gurobi_ml/torch/__init__.py
@@ -13,4 +13,4 @@
 # limitations under the License.
 # ==============================================================================
 
-from .sequential import add_sequential_constr
+from .sequential import add_sequential_constr as add_sequential_constr
diff --git a/src/gurobi_ml/xgboost/__init__.py b/src/gurobi_ml/xgboost/__init__.py
index 859c6d65..5a74ea31 100644
--- a/src/gurobi_ml/xgboost/__init__.py
+++ b/src/gurobi_ml/xgboost/__init__.py
@@ -13,4 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 
-from .xgboost_regressor import add_xgboost_regressor_constr, add_xgbregressor_constr
+from .xgboost_regressor import (
+    add_xgboost_regressor_constr as add_xgboost_regressor_constr,
+)
+from .xgboost_regressor import add_xgbregressor_constr as add_xgbregressor_constr
diff --git a/src/gurobi_ml/xgboost/xgboost_regressor.py b/src/gurobi_ml/xgboost/xgboost_regressor.py
index 04b08df4..d286d214 100644
--- a/src/gurobi_ml/xgboost/xgboost_regressor.py
+++ b/src/gurobi_ml/xgboost/xgboost_regressor.py
@@ -18,7 +18,6 @@
 into a :external+gurobi:py:class:`Model`.
 """
 
-
 import json
 
 import numpy as np
diff --git a/src/gurobi_ml/xgboost_sklearn_api.py b/src/gurobi_ml/xgboost_sklearn_api.py
index fc7e358b..2b285e42 100644
--- a/src/gurobi_ml/xgboost_sklearn_api.py
+++ b/src/gurobi_ml/xgboost_sklearn_api.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 
-""" Convertor for XGBoost's Scikit Learn compatible object
+"""Convertor for XGBoost's Scikit Learn compatible object
 
 This is to be able to use it in pipelines.
 """
diff --git a/tests/test_sklearn/test_register_constr.py b/tests/test_sklearn/test_register_constr.py
index f0174e0c..99941cee 100644
--- a/tests/test_sklearn/test_register_constr.py
+++ b/tests/test_sklearn/test_register_constr.py
@@ -1,5 +1,5 @@
 # Copyright © 2023 Gurobi Optimization, LLC
-""" Test register_predictor_constr function"""
+"""Test register_predictor_constr function"""
 
 import unittest
 
diff --git a/tests/test_sklearn/test_remove.py b/tests/test_sklearn/test_remove.py
index 579f3abd..085128c7 100644
--- a/tests/test_sklearn/test_remove.py
+++ b/tests/test_sklearn/test_remove.py
@@ -1,4 +1,5 @@
-""" Test for adding and removing predictor objects"""
+"""Test for adding and removing predictor objects"""
+
 import random
 import unittest