diff --git a/docs/source/notebooks/mmm/mmm_budget_allocation_example.ipynb b/docs/source/notebooks/mmm/mmm_budget_allocation_example.ipynb
index 89876275f..90bad1999 100644
--- a/docs/source/notebooks/mmm/mmm_budget_allocation_example.ipynb
+++ b/docs/source/notebooks/mmm/mmm_budget_allocation_example.ipynb
@@ -34,21 +34,6 @@
     "Before delving into the specifics of budget allocation, the initial step is to install the PyMC-Marketing library and ascertain its version. This step will confirm support for the budget allocation function. The following pip command can be run on your Jupyter Notebook:"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "-LnDMZnXIh-x",
-    "outputId": "8d14a8c8-d5c7-4894-8333-eb437b69cd17"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install pymc-marketing"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -119,7 +104,7 @@
    },
    "outputs": [],
    "source": [
-    "name = '/pymc-marketing/data/budget_optimizer_model.nc'\n",
+    "name = \"/pymc-marketing/data/budget_optimizer_model.nc\"\n",
     "mmm = DelayedSaturatedMMM.load(name)"
    ]
   },
@@ -308,7 +293,7 @@
    ],
    "source": [
     "sigmoid_response_curve_fig = mmm.plot_direct_contribution_curves(\n",
-    "show_fit = True, method='michaelis-menten'\n",
+    "    show_fit=True, method=\"michaelis-menten\"\n",
     ")"
    ]
   },
@@ -337,9 +322,13 @@
    },
    "outputs": [],
    "source": [
-    "sigmoid_params = mmm.compute_channel_curve_optimization_parameters_original_scale(method='sigmoid')\n",
+    "sigmoid_params = mmm.compute_channel_curve_optimization_parameters_original_scale(\n",
+    "    method=\"sigmoid\"\n",
+    ")\n",
     "\n",
-    "menten_params = mmm.compute_channel_curve_optimization_parameters_original_scale(method='michaelis-menten')"
+    "menten_params = mmm.compute_channel_curve_optimization_parameters_original_scale(\n",
+    "    method=\"michaelis-menten\"\n",
+    ")"
    ]
   },
   {
@@ -431,15 +420,15 @@
    },
    "outputs": [],
    "source": [
-    "total_budget = 5 #Imagine is 5K or 5M\n",
-    "#Define your channels\n",
-    "channels = ['x1','x2']\n",
-    "#The initial split per channel\n",
+    "total_budget = 5  # Imagine is 5K or 5M\n",
+    "# Define your channels\n",
+    "channels = [\"x1\", \"x2\"]\n",
+    "# The initial split per channel\n",
     "budget_per_channel = total_budget / len(channels)\n",
-    "#Initial budget per channel as dictionary.\n",
+    "# Initial budget per channel as dictionary.\n",
     "initial_budget_dict = {channel: budget_per_channel for channel in channels}\n",
-    "#bounds for each channel\n",
-    "min_budget, max_budget = 1,5\n",
+    "# bounds for each channel\n",
+    "min_budget, max_budget = 1, 5\n",
     "budget_bounds = {channel: [min_budget, max_budget] for channel in channels}"
    ]
   },
@@ -733,10 +722,10 @@
    ],
    "source": [
     "result_sigmoid = mmm.optimize_channel_budget_for_maximum_contribution(\n",
-    "    method = 'sigmoid', #define saturation function\n",
-    "    total_budget = total_budget,\n",
-    "    parameters = sigmoid_params,\n",
-    "    budget_bounds = budget_bounds\n",
+    "    method=\"sigmoid\",  # define saturation function\n",
+    "    total_budget=total_budget,\n",
+    "    parameters=sigmoid_params,\n",
+    "    budget_bounds=budget_bounds,\n",
     ")\n",
     "\n",
     "result_sigmoid"
@@ -1032,10 +1021,10 @@
    ],
    "source": [
     "result_menten = mmm.optimize_channel_budget_for_maximum_contribution(\n",
-    "    method = 'michaelis-menten',\n",
-    "    total_budget = total_budget,\n",
-    "    parameters = menten_params,\n",
-    "    budget_bounds = budget_bounds\n",
+    "    method=\"michaelis-menten\",\n",
+    "    total_budget=total_budget,\n",
+    "    parameters=menten_params,\n",
+    "    budget_bounds=budget_bounds,\n",
     ")\n",
     "result_menten"
    ]
@@ -1059,18 +1048,16 @@
    },
    "outputs": [],
    "source": [
-    "#Use the function `calculate_expected_contribution` to estimate\n",
-    "#the contribution of your initial budget based on the curve parameters.\n",
+    "# Use the function `calculate_expected_contribution` to estimate\n",
+    "# the contribution of your initial budget based on the curve parameters.\n",
     "initial_contribution = calculate_expected_contribution(\n",
-    "    method='sigmoid',\n",
-    "    parameters = sigmoid_params,\n",
-    "    budget = initial_budget_dict\n",
+    "    method=\"sigmoid\", parameters=sigmoid_params, budget=initial_budget_dict\n",
     ")\n",
     "\n",
     "# Initial budget & contribution dictionary\n",
     "initial_scenario = {\n",
-    "    'initial_contribution': initial_contribution,\n",
-    "    'initial_budget': initial_budget_dict\n",
+    "    \"initial_contribution\": initial_contribution,\n",
+    "    \"initial_budget\": initial_budget_dict,\n",
     "}"
    ]
   },
@@ -1114,9 +1101,11 @@
     }
    ],
    "source": [
-    "#Use the function `compare_budget_scenearios` to validate\n",
-    "#The estimated contribution from one scenario agains the other\n",
-    "figure_ = mmm.plot_budget_scenearios(base_data=initial_scenario, method='sigmoid', scenarios_data=[result_sigmoid])"
+    "# Use the function `compare_budget_scenearios` to validate\n",
+    "# The estimated contribution from one scenario agains the other\n",
+    "figure_ = mmm.plot_budget_scenearios(\n",
+    "    base_data=initial_scenario, method=\"sigmoid\", scenarios_data=[result_sigmoid]\n",
+    ")"
    ]
   },
   {
@@ -1158,30 +1147,37 @@
    },
    "outputs": [],
    "source": [
-    "#Initialize two variables to save the results and base conditions for each scenario.\n",
+    "# Initialize two variables to save the results and base conditions for each scenario.\n",
     "scenarios_result = []\n",
     "scenarios_base = []\n",
     "\n",
     "for scenario in np.array([0.6, 0.8, 1.2, 1.8]):\n",
-    "  scenarios_result.append(\n",
-    "    mmm.optimize_channel_budget_for_maximum_contribution(\n",
-    "        method = 'sigmoid', #define saturation function\n",
-    "        total_budget = total_budget * scenario,\n",
-    "        parameters = sigmoid_params,\n",
-    "        budget_bounds = {channel: [1, total_budget * scenario] for channel in channels}\n",
-    "    ).to_dict()\n",
-    "  )\n",
-    "\n",
-    "  scenarios_base.append(\n",
-    "    {'initial_contribution': calculate_expected_contribution(\n",
-    "      method='sigmoid', #define saturation function\n",
-    "      parameters = sigmoid_params,\n",
-    "      budget = {channel: total_budget * scenario / len(channels) for channel in channels}\n",
-    "      ),\n",
-    "\n",
-    "      'initial_budget': {channel: total_budget * scenario / len(channels) for channel in channels}\n",
-    "    }\n",
-    "  )"
+    "    scenarios_result.append(\n",
+    "        mmm.optimize_channel_budget_for_maximum_contribution(\n",
+    "            method=\"sigmoid\",  # define saturation function\n",
+    "            total_budget=total_budget * scenario,\n",
+    "            parameters=sigmoid_params,\n",
+    "            budget_bounds={\n",
+    "                channel: [1, total_budget * scenario] for channel in channels\n",
+    "            },\n",
+    "        ).to_dict()\n",
+    "    )\n",
+    "\n",
+    "    scenarios_base.append(\n",
+    "        {\n",
+    "            \"initial_contribution\": calculate_expected_contribution(\n",
+    "                method=\"sigmoid\",  # define saturation function\n",
+    "                parameters=sigmoid_params,\n",
+    "                budget={\n",
+    "                    channel: total_budget * scenario / len(channels)\n",
+    "                    for channel in channels\n",
+    "                },\n",
+    "            ),\n",
+    "            \"initial_budget\": {\n",
+    "                channel: total_budget * scenario / len(channels) for channel in channels\n",
+    "            },\n",
+    "        }\n",
+    "    )"
    ]
   },
   {
@@ -1222,9 +1218,11 @@
     }
    ],
    "source": [
-    "#Use the function `compare_budget_scenearios` to validate\n",
-    "#The estimated contribution from one scenario agains the other\n",
-    "_figure = mmm.plot_budget_scenearios(base_data=initial_scenario, method='sigmoid', scenarios_data=scenarios_result)"
+    "# Use the function `compare_budget_scenearios` to validate\n",
+    "# The estimated contribution from one scenario agains the other\n",
+    "_figure = mmm.plot_budget_scenearios(\n",
+    "    base_data=initial_scenario, method=\"sigmoid\", scenarios_data=scenarios_result\n",
+    ")"
    ]
   },
   {
@@ -1526,12 +1524,10 @@
    ],
    "source": [
     "platform_base_optimization = mmm.optimize_channel_budget_for_maximum_contribution(\n",
-    "    method = 'sigmoid',\n",
-    "    total_budget = total_budget,\n",
-    "    parameters = sigmoid_params,\n",
-    "    budget_bounds = {'x1':[0,1.5],\n",
-    "                     'x2':[0,1.5]\n",
-    "                     }\n",
+    "    method=\"sigmoid\",\n",
+    "    total_budget=total_budget,\n",
+    "    parameters=sigmoid_params,\n",
+    "    budget_bounds={\"x1\": [0, 1.5], \"x2\": [0, 1.5]},\n",
     ")\n",
     "\n",
     "platform_base_optimization"
@@ -1580,7 +1576,7 @@
    ],
    "source": [
     "sigmoid_response_curve_fig = mmm.plot_direct_contribution_curves(\n",
-    "show_fit = True, method='sigmoid', xlim_max=3\n",
+    "    show_fit=True, method=\"sigmoid\", xlim_max=3\n",
     ")"
    ]
   },
@@ -1874,12 +1870,10 @@
    ],
    "source": [
     "platform_base_optimization = mmm.optimize_channel_budget_for_maximum_contribution(\n",
-    "    method = 'sigmoid',\n",
-    "    total_budget = total_budget,\n",
-    "    parameters = sigmoid_params,\n",
-    "    budget_bounds = {'x1':[0,1.2],\n",
-    "                     'x2':[0,1.5]\n",
-    "                     }\n",
+    "    method=\"sigmoid\",\n",
+    "    total_budget=total_budget,\n",
+    "    parameters=sigmoid_params,\n",
+    "    budget_bounds={\"x1\": [0, 1.2], \"x2\": [0, 1.5]},\n",
     ")\n",
     "\n",
     "platform_base_optimization"
@@ -1923,7 +1917,11 @@
     }
    ],
    "source": [
-    "_figure = mmm.plot_budget_scenearios(base_data=initial_scenario, method='sigmoid', scenarios_data=[platform_base_optimization])"
+    "_figure = mmm.plot_budget_scenearios(\n",
+    "    base_data=initial_scenario,\n",
+    "    method=\"sigmoid\",\n",
+    "    scenarios_data=[platform_base_optimization],\n",
+    ")"
    ]
   },
   {
@@ -1952,15 +1950,6 @@
     "\n",
     "Consequently, your engagements, feedback, and thoughts are not merely welcomed but actively solicited to make this tool as practical and universally applicable as possible."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Hy7ueyLb4F4Q"
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/pymc_marketing/clv/distributions.py b/pymc_marketing/clv/distributions.py
index e7c03f49f..d1bdebbae 100644
--- a/pymc_marketing/clv/distributions.py
+++ b/pymc_marketing/clv/distributions.py
@@ -350,7 +350,9 @@ class ParetoNBD(PositiveContinuous):
     Population-level distribution class for a continuous, non-contractual, Pareto/NBD process,
     based on Schmittlein, et al. in [2]_.
 
-    The likelihood function is derived from equations (22) and (23) of [3]_, with terms rearranged for numerical stability.
+    The likelihood function is derived from equations (22) and (23) of [3]_, with terms
+    rearranged for numerical stability.
+
     The modified expression is provided below:
 
     .. math::
@@ -389,7 +391,7 @@ class ParetoNBD(PositiveContinuous):
     .. [3] Fader, Peter & G. S. Hardie, Bruce (2005).
            "A Note on Deriving the Pareto/NBD Model and Related Expressions."
            http://brucehardie.com/notes/009/pareto_nbd_derivations_2005-11-05.pdf
-    """
+    """  # noqa: E501
 
     rv_op = pareto_nbd
 
diff --git a/pymc_marketing/clv/models/beta_geo.py b/pymc_marketing/clv/models/beta_geo.py
index 2e6104770..dc78a8f1b 100644
--- a/pymc_marketing/clv/models/beta_geo.py
+++ b/pymc_marketing/clv/models/beta_geo.py
@@ -37,10 +37,12 @@ class BetaGeoModel(CLVModel):
         DataFrame containing the following columns:
             * `frequency`: number of repeat purchases (with possible values 0, 1, 2, ...)
             * `recency`: time between the first and the last purchase (with possible values 0, 1, 2, ...)
-            * `T`: time between the first purchase and the end of the observation period (with possible values 0, 1, 2, ...)
+            * `T`: time between the first purchase and the end of the observation
+                period (with possible values 0, 1, 2, ...)
             * `customer_id`: unique customer identifier
     model_config: dict, optional
-        Dictionary of model prior parameters. If not provided, the model will use default priors specified in the `default_model_config` class attribute.
+        Dictionary of model prior parameters. If not provided, the model will use default priors specified in
+        the `default_model_config` class attribute.
     sampler_config: dict, optional
         Dictionary of sampler parameters. Defaults to None.
 
diff --git a/pymc_marketing/clv/models/gamma_gamma.py b/pymc_marketing/clv/models/gamma_gamma.py
index 3bd2c8fda..efbd5b159 100644
--- a/pymc_marketing/clv/models/gamma_gamma.py
+++ b/pymc_marketing/clv/models/gamma_gamma.py
@@ -52,8 +52,8 @@ def expected_customer_spend(
 
         Eq 5 from [1], p.3
 
-        Adapted from: https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/lifetimes/fitters/gamma_gamma_fitter.py#L117  # noqa: E501
-        """
+        Adapted from: https://github.com/CamDavidsonPilon/lifetimes/blob/aae339c5437ec31717309ba0ec394427e19753c4/lifetimes/fitters/gamma_gamma_fitter.py#L117
+        """  # noqa: E501
 
         mean_transaction_value, frequency = to_xarray(
             customer_id, mean_transaction_value, frequency
@@ -162,7 +162,8 @@ class GammaGammaModel(BaseGammaGammaModel):
             - mean_transaction_value: Mean transaction value of each customer.
             - frequency: Number of transactions observed for each customer.
     model_config: dict, optional
-        Dictionary of model prior parameters. If not provided, the model will use default priors specified in the `default_model_config` class attribute.
+        Dictionary of model prior parameters. If not provided, the model will use default priors specified in the
+        `default_model_config` class attribute.
     sampler_config: dict, optional
         Dictionary of sampler parameters. Defaults to None.
 
@@ -295,7 +296,8 @@ class GammaGammaModelIndividual(BaseGammaGammaModel):
         coming from the same customer.
             - individual_transaction_value: Value of individual transactions.
     model_config: dict, optional
-        Dictionary of model prior parameters. If not provided, the model will use default priors specified in the `default_model_config` class attribute.
+        Dictionary of model prior parameters. If not provided, the model will use default priors specified in the
+        `default_model_config` class attribute.
     sampler_config: dict, optional
         Dictionary of sampler parameters. Defaults to None.
 
diff --git a/pymc_marketing/clv/models/pareto_nbd.py b/pymc_marketing/clv/models/pareto_nbd.py
index 5fec84e39..7bdf58ae3 100644
--- a/pymc_marketing/clv/models/pareto_nbd.py
+++ b/pymc_marketing/clv/models/pareto_nbd.py
@@ -68,7 +68,8 @@ class ParetoNBDModel(CLVModel):
         DataFrame containing the following columns:
             * `frequency`: number of repeat purchases
             * `recency`: time between the first and the last purchase
-            * `T`: time between the first purchase and the end of the observation period; model assumptions require T >= recency
+            * `T`: time between the first purchase and the end of the observation period.
+                Model assumptions require T >= recency
             * `customer_id`: unique customer identifier
         Along with optional covariate columns.
 
@@ -328,7 +329,10 @@ def fit(self, fit_method: str = "map", **kwargs):  # type: ignore
             # Suppress annoying warning
             with warnings.catch_warnings():
                 warnings.filterwarnings(
-                    message="Optimization Warning: The Op hyp2f1 does not provide a C implementation. As well as being potentially slow, this also disables loop fusion.",
+                    message="""
+                    Optimization Warning: The Op hyp2f1 does not provide a C implementation.
+                    As well as being potentially slow, this also disables loop fusion.
+                    """,
                     action="ignore",
                     category=UserWarning,
                 )
@@ -460,7 +464,8 @@ def expected_purchases(
                 * `customer_id`: unique customer identifier
                 * `frequency`: number of repeat purchases
                 * `recency`: time between the first and the last purchase
-                * `T`: time between the first purchase and the end of the observation period, model assumptions require T >= recency
+                * `T`: time between the first purchase and the end of the observation period.
+                    Model assumptions require T >= recency
                 * `future_t`: Number of time periods to predict expected purchases.
                 * covariates: Purchase and dropout covariate columns if original model had any.
             If not provided, the method will use the fit dataset.
@@ -532,7 +537,8 @@ def expected_probability_alive(
                 * `customer_id`: unique customer identifier
                 * `frequency`: number of repeat purchases
                 * `recency`: time between the first and the last purchase
-                * `T`: time between the first purchase and the end of the observation period, model assumptions require T >= recency
+                * `T`: time between the first purchase and the end of the observation period.
+                    Model assumptions require T >= recency
                 * `future_t`: Number of time periods in the future to estimate alive probability; defaults to 0.
                 * covariates: Purchase and dropout covariate columns if original model had any.
             If not provided, the method will use the fit dataset.
@@ -602,9 +608,11 @@ def expected_purchase_probability(
                 * `customer_id`: unique customer identifier
                 * `frequency`: number of repeat purchases
                 * `recency`: time between the first and the last purchase
-                * `T`: time between the first purchase and the end of the observation period, model assumptions require T >= recency
+                * `T`: time between the first purchase and the end of the observation period.
+                    Model assumptions require T >= recency
                 * `future_t`: Number of time periods to predict expected purchases.
-                * `n_purchases`: Number of purchases to predict probability for. Currently restricted to the same number for all customers.
+                * `n_purchases`: Number of purchases to predict probability for.
+                    Currently restricted to the same number for all customers.
                 * covariates: Purchase and dropout covariate columns if original model had any.
             If not provided, the method will use the fit dataset.
         n_purchases: int, optional
@@ -822,7 +830,8 @@ def distribution_new_customer(
             "recency_frequency",
         ),
     ) -> xarray.Dataset:
-        """Utility function for posterior predictive sampling of dropout, purchase rate and frequency/recency of new customers.
+        """Utility function for posterior predictive sampling of dropout, purchase rate
+        and frequency/recency of new customers.
 
         In a model with covariates, if `data` is not specified, the dataset used for fitting will be used.
         A prediction will be computed for a new customer with each set of covariates.
diff --git a/pymc_marketing/clv/models/shifted_beta_geo.py b/pymc_marketing/clv/models/shifted_beta_geo.py
index d814eb984..c0dc673e5 100644
--- a/pymc_marketing/clv/models/shifted_beta_geo.py
+++ b/pymc_marketing/clv/models/shifted_beta_geo.py
@@ -31,7 +31,8 @@ class ShiftedBetaGeoModelIndividual(CLVModel):
         observation period
             * `T`: Maximum observed time period (starting at 0)
     model_config: dict, optional
-        Dictionary of model prior parameters. If not provided, the model will use default priors specified in the `default_model_config` class attribute.
+        Dictionary of model prior parameters. If not provided, the model will use default priors specified in the
+        `default_model_config` class attribute.
     sampler_config: dict, optional
         Dictionary of sampler parameters. Defaults to None.
 
diff --git a/pymc_marketing/clv/utils.py b/pymc_marketing/clv/utils.py
index 111e8a697..a6b91d4f5 100644
--- a/pymc_marketing/clv/utils.py
+++ b/pymc_marketing/clv/utils.py
@@ -511,9 +511,9 @@ def rfm_train_test_split(
     ]
 
     if training_transactions.empty:
-        raise ValueError(
-            "No data available. Check `test_transactions` and  `train_period_end` and confirm values in `transactions` occur prior to those time periods."
-        )
+        error_msg = """No data available. Check `test_transactions` and `train_period_end`
+        and confirm values in `transactions` occur prior to those time periods."""
+        raise ValueError(error_msg)
 
     training_rfm_data = rfm_summary(
         training_transactions,
@@ -535,9 +535,9 @@ def rfm_train_test_split(
     ].copy()
 
     if test_transactions.empty:
-        raise ValueError(
-            "No data available. Check `test_transactions` and  `train_period_end` and confirm values in `transactions` occur prior to those time periods."
-        )
+        error_msg = """No data available. Check `test_transactions` and `train_period_end`
+        and confirm values in `transactions` occur prior to those time periods."""
+        raise ValueError(error_msg)
 
     test_transactions[datetime_col] = test_transactions[datetime_col].dt.to_period(
         time_unit
diff --git a/pymc_marketing/mmm/base.py b/pymc_marketing/mmm/base.py
index 3043e6f01..cf39d31a4 100644
--- a/pymc_marketing/mmm/base.py
+++ b/pymc_marketing/mmm/base.py
@@ -89,13 +89,15 @@ def validation_methods(
         A property that provides validation methods for features ("X") and the target variable ("y").
 
         This property scans the methods of the object and returns those marked for validation.
-        The methods are marked by having a _tags dictionary attribute, with either "validation_X" or "validation_y" set to True.
-        The "validation_X" tag indicates a method used for validating features, and "validation_y" indicates a method used for validating the target variable.
+        The methods are marked by having a _tags dictionary attribute,with either "validation_X" or "validation_y"
+        set to True. The "validation_X" tag indicates a method used for validating features, and "validation_y"
+        indicates a method used for validating the target variable.
 
         Returns
         -------
         tuple of list of Callable[["BaseMMM", pd.DataFrame], None]
-            A tuple where the first element is a list of methods for "X" validation, and the second element is a list of methods for "y" validation.
+            A tuple where the first element is a list of methods for "X" validation, and the second element is
+            a list of methods for "y" validation.
 
         """
         return (
@@ -164,13 +166,15 @@ def preprocessing_methods(
         A property that provides preprocessing methods for features ("X") and the target variable ("y").
 
         This property scans the methods of the object and returns those marked for preprocessing.
-        The methods are marked by having a _tags dictionary attribute, with either "preprocessing_X" or "preprocessing_y" set to True.
-        The "preprocessing_X" tag indicates a method used for preprocessing features, and "preprocessing_y" indicates a method used for preprocessing the target variable.
+        The methods are marked by having a _tags dictionary attribute, with either "preprocessing_X"
+        or "preprocessing_y" set to True. The "preprocessing_X" tag indicates a method used for preprocessing
+        features, and "preprocessing_y" indicates a method used for preprocessing the target variable.
 
         Returns
         -------
         tuple of list of Callable[["BaseMMM", pd.DataFrame], pd.DataFrame]
-            A tuple where the first element is a list of methods for "X" preprocessing, and the second element is a list of methods for "y" preprocessing.
+            A tuple where the first element is a list of methods for "X" preprocessing, and the second element is a
+            list of methods for "y" preprocessing.
         """
         return (
             [
@@ -191,8 +195,9 @@ def preprocess(
         """
         Preprocess the provided data according to the specified target.
 
-        This method applies preprocessing methods to the data ("X" or "y"), which are specified in the preprocessing_methods property of this object.
-        It iteratively applies each method in the appropriate list (either for "X" or "y") to the data.
+        This method applies preprocessing methods to the data ("X" or "y"), which are specified in the
+        preprocessing_methods property of this object. It iteratively applies each method in the appropriate
+        list (either for "X" or "y") to the data.
 
         Parameters
         ----------
@@ -506,12 +511,14 @@ def _estimate_budget_contribution_fit(
         and calculates the lower and upper bounds of the contribution fit.
 
         The function is used in the `plot_budget_scenearios` function to estimate the contribution fit for each channel
-        and budget scenario. The estimated fit is then used to plot the contribution optimization bounds for each scenario.
+        and budget scenario. The estimated fit is then used to plot the contribution optimization bounds
+        for each scenario.
 
         Parameters
         ----------
         method : str
-            The method used to fit the contribution & spent non-linear relationship. It can be either 'sigmoid' or 'michaelis-menten'.
+            The method used to fit the contribution & spent non-linear relationship.
+            It can be either 'sigmoid' or 'michaelis-menten'.
         channel : str
             The name of the channel for which the contribution fit is being estimated.
         budget : float
@@ -575,7 +582,8 @@ def _plot_scenario(
         ax : matplotlib.axes.Axes
             The axes on which to plot the scenario.
         data : dict
-            Dictionary containing the data for the scenario. Keys are the names of the channels and values are the corresponding values.
+            Dictionary containing the data for the scenario.
+            Keys are the names of the channels and values are the corresponding values.
         label : str
             Label for the scenario.
         color : str
@@ -585,9 +593,11 @@ def _plot_scenario(
         bar_width: float
             Bar width.
         upper_bound : dict, optional
-            Dictionary containing the upper bounds for the data. Keys should match those in the `data` dictionary. Only used if `contribution` is True.
+            Dictionary containing the upper bounds for the data. Keys should match those in the `data` dictionary.
+            Only used if `contribution` is True.
         lower_bound : dict, optional
-            Dictionary containing the lower bounds for the data. Keys should match those in the `data` dictionary. Only used if `contribution` is True.
+            Dictionary containing the lower bounds for the data. Keys should match those in the `data` dictionary.
+            Only used if `contribution` is True.
         contribution : bool, optional
             If True, plot the upper and lower bounds for the data. Default is False.
 
@@ -731,13 +741,15 @@ def _plot_response_curve_fit(
         """
         Plot the curve fit for the given channel based on the estimation of the parameters.
 
-        The function computes the mean channel contributions, estimates the parameters based on the specified method (either 'sigmoid' or 'michaelis-menten'), and plots
-        the curve fit. An inflection point on the curve is also highlighted.
+        The function computes the mean channel contributions, estimates the parameters based on the specified method
+        (either 'sigmoid' or 'michaelis-menten'), and plots the curve fit. An inflection point on the curve is
+        also highlighted.
 
         Parameters
         ----------
         x : np.ndarray
-            The x-axis data, usually representing the amount of input (e.g., substrate concentration in enzymology terms).
+            The x-axis data, usually representing the amount of
+            input (e.g., substrate concentration in enzymology terms).
         ax : plt.Axes
             The matplotlib axes object where the plot should be drawn.
         channel : str
@@ -747,7 +759,8 @@ def _plot_response_curve_fit(
         xlim_max: int
             The maximum value to be plot on the X-axis
         method: str
-            The method used to fit the contribution & spent non-linear relationship. It can be either 'sigmoid' or 'michaelis-menten'.
+            The method used to fit the contribution & spent non-linear relationship.
+            It can be either 'sigmoid' or 'michaelis-menten'.
 
         Returns
         -------
@@ -855,18 +868,21 @@ def optimize_channel_budget_for_maximum_contribution(
         parameters: Dict[str, Tuple[float, float]],
     ) -> pd.DataFrame:
         """
-        Experimental: Optimize the allocation of a given total budget across multiple channels to maximize the expected contribution.
+        Experimental: Optimize the allocation of a given total budget across multiple
+        channels to maximize the expected contribution.
 
         The optimization is based on the method provided, where each channel's contribution
         follows a saturating function of its allocated budget. The function seeks the budget allocation
-        that maximizes the total expected contribution across all channels. The method can be either 'sigmoid' or 'michaelis-menten'.
+        that maximizes the total expected contribution across all channels.
+        The method can be either 'sigmoid' or 'michaelis-menten'.
 
         Parameters
         ----------
         total_budget : int, required
             The total budget to be distributed across channels.
         method : str, required
-            The method used to fit the contribution & spent non-linear relationship. It can be either 'sigmoid' or 'michaelis-menten'.
+            The method used to fit the contribution & spent non-linear relationship.
+            It can be either 'sigmoid' or 'michaelis-menten'.
         parameters : Dict, required
             A dictionary where keys are channel names and values are tuples (L, k) representing the
             parameters for each channel based on the method used.
@@ -913,13 +929,15 @@ def compute_channel_curve_optimization_parameters_original_scale(
         """
         Experimental: Estimate the parameters for the saturating function of each channel's contribution.
 
-        The function estimates the parameters (alpha, constant) for each channel based on the specified method (either 'sigmoid' or 'michaelis-menten').
-        These parameters represent the maximum possible contribution (alpha) and the constant parameter which vary their definition based on the function (constant) for each channel.
+        The function estimates the parameters (alpha, constant) for each channel based on the specified method
+        (either 'sigmoid' or 'michaelis-menten'). These parameters represent the maximum possible contribution (alpha)
+        and the constant parameter which vary their definition based on the function (constant) for each channel.
 
         Parameters
         ----------
         method : str, required
-            The method used to fit the contribution & spent non-linear relationship. It can be either 'sigmoid' or 'michaelis-menten'.
+            The method used to fit the contribution & spent non-linear relationship.
+            It can be either 'sigmoid' or 'michaelis-menten'.
 
         Returns
         -------
@@ -967,7 +985,8 @@ def plot_direct_contribution_curves(
         xlim_max : int, optional
             The maximum value to be plot on the X-axis. If not provided, the maximum value in the data will be used.
         method : str, optional
-            The method used to fit the contribution & spent non-linear relationship. It can be either 'sigmoid' or 'michaelis-menten'. Defaults to 'sigmoid'.
+            The method used to fit the contribution & spent non-linear relationship.
+            It can be either 'sigmoid' or 'michaelis-menten'. Defaults to 'sigmoid'.
         channels : List[str], optional
             A list of channels to plot. If not provided, all channels will be plotted.
         same_axes : bool, optional
diff --git a/pymc_marketing/mmm/delayed_saturated_mmm.py b/pymc_marketing/mmm/delayed_saturated_mmm.py
index 8645f63d9..85e8707e0 100644
--- a/pymc_marketing/mmm/delayed_saturated_mmm.py
+++ b/pymc_marketing/mmm/delayed_saturated_mmm.py
@@ -63,9 +63,11 @@ def __init__(
         channel_columns : List[str]
             Column names of the media channel variables.
         model_config : Dictionary, optional
-            dictionary of parameters that initialise model configuration. Class-default defined by the user default_model_config method.
+            dictionary of parameters that initialise model configuration.
+            Class-default defined by the user default_model_config method.
         sampler_config : Dictionary, optional
-            dictionary of parameters that initialise sampler configuration. Class-default defined by the user default_sampler_config method.
+            dictionary of parameters that initialise sampler configuration.
+            Class-default defined by the user default_sampler_config method.
         validate_data : bool, optional
             Whether to validate the data before fitting to model, by default True.
         control_columns : Optional[List[str]], optional
@@ -210,7 +212,10 @@ def _create_likelihood_distribution(
 
         if dist["dist"] not in allowed_distributions:
             raise ValueError(
-                f"The distribution used for the likelihood is not allowed. Please, use one of the following distributions: {allowed_distributions}."
+                f"""
+                The distribution used for the likelihood is not allowed.
+                Please, use one of the following distributions: {allowed_distributions}.
+                """
             )
 
         # Validate that 'kwargs' is present and is a dictionary
@@ -242,7 +247,10 @@ def _create_likelihood_distribution(
                 parameter_distributions[param] = param_config
             else:
                 raise ValueError(
-                    f"Invalid parameter configuration for '{param}'. It must be either a dictionary with a 'dist' key or a numeric value."
+                    f"""
+                    Invalid parameter configuration for '{param}'.
+                    It must be either a dictionary with a 'dist' key or a numeric value.
+                    """
                 )
 
         # Extract the likelihood distribution name and instantiate it
@@ -593,9 +601,9 @@ def load(cls, fname: str):
         model.build_model(X, y)
         # All previously used data is in idata.
         if model.id != idata.attrs["id"]:
-            raise ValueError(
-                f"The file '{fname}' does not contain an inference data of the same model or configuration as '{cls._model_type}'"
-            )
+            error_msg = f"""The file '{fname}' does not contain an inference data of the same model
+        or configuration as '{cls._model_type}'"""
+            raise ValueError(error_msg)
 
         return model
 
@@ -689,8 +697,9 @@ def identity(x):
     @classmethod
     def _model_config_formatting(cls, model_config: Dict) -> Dict:
         """
-        Because of json serialization, model_config values that were originally tuples or numpy are being encoded as lists.
-        This function converts them back to tuples and numpy arrays to ensure correct id encoding.
+        Because of json serialization, model_config values that were originally tuples
+        or numpy are being encoded as lists. This function converts them back to tuples
+        and numpy arrays to ensure correct id encoding.
         """
 
         def format_nested_dict(d: Dict) -> Dict:
@@ -736,12 +745,14 @@ class DelayedSaturatedMMM(
     This enable us to have a more stable model and better convergence. If control variables are present, we do not scale them!
     If needed please do it before passing the data to the model.
 
-    2. We allow to add yearly seasonality controls as Fourier modes. You can use the `yearly_seasonality` parameter to specify the number of Fourier modes to include.
+    2. We allow to add yearly seasonality controls as Fourier modes.
+    You can use the `yearly_seasonality` parameter to specify the number of Fourier modes to include.
 
     3. This class also allow us to calibrate the model using:
 
-    - Custom priors for the parameters via the `model_config` parameter. You can also set the likelihood distribution.
-    - Adding lift tests to the likelihood function via the :meth:`add_lift_test_measurements <pymc_marketing.mmm.delayed_saturated_mmm.DelayedSaturatedMMM.add_lift_test_measurements>` method.
+        * Custom priors for the parameters via the `model_config` parameter. You can also set the likelihood distribution.
+
+        * Adding lift tests to the likelihood function via the :meth:`add_lift_test_measurements <pymc_marketing.mmm.delayed_saturated_mmm.DelayedSaturatedMMM.add_lift_test_measurements>` method.
 
     For details on a vanilla implementation in PyMC, see [2]_.
 
@@ -830,7 +841,7 @@ class DelayedSaturatedMMM(
     ----------
     .. [1] Jin, Yuxue, et al. “Bayesian methods for media mix modeling with carryover and shape effects.” (2017).
     .. [2] Orduz, J. `"Media Effect Estimation with PyMC: Adstock, Saturation & Diminishing Returns" <https://juanitorduz.github.io/pymc_mmm/>`_.
-    """
+    """  # noqa: E501
 
     def channel_contributions_forward_pass(
         self, channel_data: npt.NDArray[np.float_]
@@ -1237,8 +1248,8 @@ def sample_posterior_predictive(
             data in order to carry over costs with the adstock transformation.
             Assumes that X_pred are the next predictions following the training data.
             Defaults to False.
-        original_scale: Boolean determining whether to return the predictions in the original scale of the target variable.
-            Defaults to True.
+        original_scale: Boolean determining whether to return the predictions in the original scale
+            of the target variable. Defaults to True.
         **sample_posterior_predictive_kwargs: Additional arguments to pass to pymc.sample_posterior_predictive
 
         Returns
diff --git a/pymc_marketing/mmm/transformers.py b/pymc_marketing/mmm/transformers.py
index a4e8b515d..066aeaf63 100644
--- a/pymc_marketing/mmm/transformers.py
+++ b/pymc_marketing/mmm/transformers.py
@@ -65,11 +65,14 @@ def batched_convolution(
     axis : int
         The axis of ``x`` along witch to apply the convolution
     mode : ConvMode, optional
-        The convolution mode determines how the convolution is applied at the boundaries of the input signal, denoted as "x." The default mode is ConvMode.Before.
+        The convolution mode determines how the convolution is applied at the boundaries
+        of the input signal, denoted as "x." The default mode is ConvMode.Before.
 
         - ConvMode.After: Applies the convolution with the "Adstock" effect, resulting in a trailing decay effect.
-        - ConvMode.Before: Applies the convolution with the "Excitement" effect, creating a leading effect similar to the wow factor.
-        - ConvMode.Overlap: Applies the convolution with both "Pull-Forward" and "Pull-Backward" effects, where the effect overlaps with both preceding and succeeding elements.
+        - ConvMode.Before: Applies the convolution with the "Excitement" effect, creating a leading effect
+            similar to the wow factor.
+        - ConvMode.Overlap: Applies the convolution with both "Pull-Forward" and "Pull-Backward" effects,
+            where the effect overlaps with both preceding and succeeding elements.
 
     Returns
     -------
@@ -285,7 +288,8 @@ def weibull_adstock(
 ):
     R"""Weibull Adstocking Transformation.
 
-    This transformation is similar to geometric adstock transformation but has more degrees of freedom, adding more flexibility.
+    This transformation is similar to geometric adstock transformation but has more
+    degrees of freedom, adding more flexibility.
 
     .. plot::
         :context: close-figs
@@ -509,7 +513,7 @@ def tanh_saturation(
     References
     ----------
     See https://www.pymc-labs.io/blog-posts/reducing-customer-acquisition-costs-how-we-helped-optimizing-hellofreshs-marketing-budget/ # noqa: E501
-    """
+    """  # noqa: E501
     return b * pt.tanh(x / (b * c))
 
 
@@ -673,7 +677,9 @@ def michaelis_menten(
     r"""
     Evaluate the Michaelis-Menten function for given values of x, alpha, and lambda.
 
-    The Michaelis-Menten function models enzyme kinetics and describes how the rate of a chemical reaction increases with substrate concentration until it reaches its maximum value.
+    The Michaelis-Menten function models enzyme kinetics and describes how the rate of
+    a chemical reaction increases with substrate concentration until it reaches its
+    maximum value.
 
     .. math::
         \alpha \cdot \frac{x}{\lambda + x}
diff --git a/pymc_marketing/mmm/utils.py b/pymc_marketing/mmm/utils.py
index 3d9b89398..489db6395 100644
--- a/pymc_marketing/mmm/utils.py
+++ b/pymc_marketing/mmm/utils.py
@@ -216,8 +216,8 @@ def standardize_scenarios_dict_keys(d: Dict, keywords: List[str]):
     """
     Standardize the keys in a dictionary based on a list of keywords.
 
-    This function iterates over the keys in the dictionary and the keywords. If a keyword is found in a key (case-insensitive),
-    the key is replaced with the keyword.
+    This function iterates over the keys in the dictionary and the keywords.
+    If a keyword is found in a key (case-insensitive), the key is replaced with the keyword.
 
     Parameters
     ----------
diff --git a/pymc_marketing/model_builder.py b/pymc_marketing/model_builder.py
index 875786f55..325145584 100644
--- a/pymc_marketing/model_builder.py
+++ b/pymc_marketing/model_builder.py
@@ -65,9 +65,11 @@ def __init__(
         data : Dictionary, optional
             It is the data we need to train the model on.
         model_config : Dictionary, optional
-            dictionary of parameters that initialise model configuration. Class-default defined by the user default_model_config method.
+            dictionary of parameters that initialise model configuration.
+            Class-default defined by the user default_model_config method.
         sampler_config : Dictionary, optional
-            dictionary of parameters that initialise sampler configuration. Class-default defined by the user default_sampler_config method.
+            dictionary of parameters that initialise sampler configuration.
+            Class-default defined by the user default_sampler_config method.
         Examples
         --------
         >>> class MyModel(ModelBuilder):
@@ -349,8 +351,9 @@ def save(self, fname: str) -> None:
     @classmethod
     def _model_config_formatting(cls, model_config: Dict) -> Dict:
         """
-        Because of json serialization, model_config values that were originally tuples or numpy are being encoded as lists.
-        This function converts them back to tuples and numpy arrays to ensure correct id encoding.
+        Because of json serialization, model_config values that were originally tuples
+        or numpy are being encoded as lists. This function converts them back to tuples
+        and numpy arrays to ensure correct id encoding.
         """
         for key in model_config:
             if isinstance(model_config[key], dict):
@@ -412,9 +415,9 @@ def load(cls, fname: str):
         # All previously used data is in idata.
 
         if model.id != idata.attrs["id"]:
-            raise ValueError(
-                f"The file '{fname}' does not contain an inference data of the same model or configuration as '{cls._model_type}'"
-            )
+            error_msg = f"""The file '{fname}' does not contain an inference data of the same model
+            or configuration as '{cls._model_type}'"""
+            raise ValueError(error_msg)
 
         return model
 
@@ -442,7 +445,8 @@ def fit(
             Specifies whether the fit progressbar should be displayed
         predictor_names: Optional[List[str]] = None,
             Allows for custom naming of predictors given in a form of 2dArray
-            allows for naming of predictors when given in a form of np.ndarray, if not provided the predictors will be named like predictor1, predictor2...
+            Allows for naming of predictors when given in a form of np.ndarray, if not provided
+            the predictors will be named like predictor1, predictor2...
         random_seed : Optional[RandomState]
             Provides sampler with initial random seed for obtaining reproducible samples
         **kwargs : Any
@@ -714,8 +718,8 @@ def id(self) -> str:
         """
         Generate a unique hash value for the model.
 
-        The hash value is created using the last 16 characters of the SHA256 hash encoding, based on the model configuration,
-        version, and model type.
+        The hash value is created using the last 16 characters of the SHA256 hash encoding,
+        based on the model configuration, version, and model type.
 
         Returns
         -------
diff --git a/pyproject.toml b/pyproject.toml
index 8e5b4e708..b27f53130 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -60,9 +60,9 @@ repository = "https://github.com/pymc-labs/pymc-marketing"
 
 [tool.ruff.lint]
 select = ["E", "F", "I", "W"]
-ignore = [
-    "E501", # Line too long
-]
+
+[tool.ruff.lint.pycodestyle]
+max-line-length = 120
 
 [tool.pytest.ini_options]
 addopts = [
diff --git a/tests/clv/test_utils.py b/tests/clv/test_utils.py
index ea468e3d1..798f8f6c7 100644
--- a/tests/clv/test_utils.py
+++ b/tests/clv/test_utils.py
@@ -763,10 +763,10 @@ def test_rfm_train_test_split_throws_better_error_if_test_period_end_is_too_earl
 
         test_end = "2014-02-07"
 
-        with pytest.raises(
-            ValueError,
-            match="No data available. Check `test_transactions` and  `train_period_end` and confirm values in `transactions` occur prior to those time periods.",
-        ):
+        error_msg = """No data available. Check `test_transactions` and `train_period_end`
+        and confirm values in `transactions` occur prior to those time periods."""
+
+        with pytest.raises(ValueError, match=error_msg):
             rfm_train_test_split(
                 transaction_data, "id", "date", train_end, test_period_end=test_end
             )
diff --git a/tests/mmm/test_delayed_saturated_mmm.py b/tests/mmm/test_delayed_saturated_mmm.py
index d7827c61b..eaca2768b 100644
--- a/tests/mmm/test_delayed_saturated_mmm.py
+++ b/tests/mmm/test_delayed_saturated_mmm.py
@@ -537,10 +537,11 @@ def mock_property(self):
         DSMMM.save("test_model")
         # Apply the monkeypatch for the property
         monkeypatch.setattr(DelayedSaturatedMMM, "id", property(mock_property))
-        with pytest.raises(
-            ValueError,
-            match="The file 'test_model' does not contain an inference data of the same model or configuration as 'DelayedSaturatedMMM'",
-        ):
+
+        error_msg = """The file 'test_model' does not contain an inference data of the same model
+        or configuration as 'DelayedSaturatedMMM'"""
+
+        with pytest.raises(ValueError, match=error_msg):
             DelayedSaturatedMMM.load("test_model")
         os.remove("test_model")