diff --git a/python/src/robyn/allocator/optimizer.py b/python/src/robyn/allocator/optimizer.py
index 7e0255270..df925d087 100644
--- a/python/src/robyn/allocator/optimizer.py
+++ b/python/src/robyn/allocator/optimizer.py
@@ -192,7 +192,7 @@ def _run_optimization(self, bounded: bool = True) -> OptimizationResult:
                     options={
                         "ftol": 1e-10,
                         "maxiter": self.params.maxeval,
-                        "disp": True,
+                        "disp": False,
                     },
                 )
 
diff --git a/python/src/robyn/data/entities/mmmdata.py b/python/src/robyn/data/entities/mmmdata.py
index 21c0b757e..deabcc565 100644
--- a/python/src/robyn/data/entities/mmmdata.py
+++ b/python/src/robyn/data/entities/mmmdata.py
@@ -10,6 +10,9 @@
     OrganicSigns,
     PaidMediaSigns,
 )
+import logging
+
+logger = logging.getLogger(__name__)
 
 
 @dataclass
@@ -168,7 +171,7 @@ def display_data(self) -> None:
         """
         Display the contents of the DataFrame.
         """
-        print(self.data)
+        logger.debug(self.data)
 
     def get_summary(self) -> pd.DataFrame:
         """
@@ -223,7 +226,7 @@ def calculate_rolling_window_indices(self) -> None:
             self.mmmdata_spec.rolling_window_start_which = closest_start_idx
             # Adjust window_start to the closest date in the data
             self.mmmdata_spec.window_start = closest_start_date
-            print(
+            logger.debug(
                 f"Adjusted window_start to the closest date in the data: {closest_start_date}"
             )
 
@@ -238,7 +241,7 @@ def calculate_rolling_window_indices(self) -> None:
             self.mmmdata_spec.rolling_window_end_which = closest_end_idx
             # Adjust window_end to the closest date in the data
             self.mmmdata_spec.window_end = closest_end_date
-            print(
+            logger.debug(
                 f"Adjusted window_end to the closest date in the data: {closest_end_date}"
             )
 
diff --git a/python/src/robyn/modeling/feature_engineering.py b/python/src/robyn/modeling/feature_engineering.py
index e2e5dddb0..c5066c6ab 100644
--- a/python/src/robyn/modeling/feature_engineering.py
+++ b/python/src/robyn/modeling/feature_engineering.py
@@ -20,6 +20,9 @@
 from robyn.data.entities.mmmdata import MMMData
 
 
+logger = logging.getLogger(__name__)
+
+
 class FeatureEngineering:
     """
     A class used to perform feature engineering for Marketing Mix Modeling (MMM) data.
@@ -34,7 +37,7 @@ def __init__(
         self.mmm_data = mmm_data
         self.hyperparameters = hyperparameters
         self.holidays_data = holidays_data
-        self.logger = logging.getLogger(__name__)
+        self.logger = logger
         self.logger.debug(
             "Initializing FeatureEngineering with MMM data and hyperparameters"
         )
diff --git a/python/src/robyn/robyn.py b/python/src/robyn/robyn.py
index b0462dfe5..150d5d800 100644
--- a/python/src/robyn/robyn.py
+++ b/python/src/robyn/robyn.py
@@ -3,7 +3,7 @@
 import logging
 from logging.handlers import RotatingFileHandler
 from pathlib import Path
-from typing import Dict, Optional, List
+from typing import Dict, Optional
 import copy
 from robyn.data.entities.mmmdata import MMMData
 from robyn.data.entities.holidays_data import HolidaysData
@@ -15,7 +15,6 @@
 from robyn.modeling.entities.modeloutputs import ModelOutputs
 from robyn.modeling.entities.modelrun_trials_config import TrialsConfig
 from robyn.modeling.entities.enums import Models, NevergradAlgorithm
-from robyn.modeling.entities.pareto_result import ParetoResult
 from robyn.modeling.model_executor import ModelExecutor
 
 from robyn.modeling.pareto.pareto_optimizer import ParetoOptimizer
@@ -34,6 +33,7 @@
 from robyn.visualization.feature_visualization import FeaturePlotter
 from robyn.visualization.model_convergence_visualizer import ModelConvergenceVisualizer
 from robyn.visualization.pareto_visualizer import ParetoVisualizer
+import matplotlib.pyplot as plt
 
 logger = logging.getLogger(__name__)
 
@@ -288,6 +288,7 @@ def evaluate_models(
                         self.mmm_data,
                     )
                     cluster_visualizer.plot_all(display_plots, self.working_dir)
+                    plt.close("all")
             logger.info("Model evaluation complete")
 
         except Exception as e:
@@ -425,7 +426,8 @@ def generate_one_pager(self, solution_id: Optional[str] = None) -> None:
                 solution_ids=solution_id if solution_id else "all",
                 top_pareto=top_pareto,
             )
-            return figures
+            # return None instead of figures
+            return None
 
         except Exception as e:
             logging.error("One-pager generation failed: %s", str(e))
diff --git a/python/src/robyn/tutorials/tutorial1.ipynb b/python/src/robyn/tutorials/tutorial1.ipynb
index 7f2615f1a..6d8f629ab 100644
--- a/python/src/robyn/tutorials/tutorial1.ipynb
+++ b/python/src/robyn/tutorials/tutorial1.ipynb
@@ -31,7 +31,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-12-06 14:21:59,837 - robyn - INFO - Logging is set up to console only.\n"
+      "2024-12-06 17:41:13,402 - robyn - INFO - Logging is set up to console only.\n",
+      "/Users/yijuilee/robynpy_release_reviews/robynvenv_py3.10/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     }
    ],
@@ -41,16 +43,11 @@
     "sys.path.append(\"/Users/yijuilee/robynpy_release_reviews/Robyn/python/src\")\n",
     "\n",
     "import pandas as pd\n",
-    "import numpy as np\n",
     "from robyn.robyn import Robyn\n",
     "from robyn.data.entities.mmmdata import MMMData\n",
     "from robyn.data.entities.holidays_data import HolidaysData\n",
     "from robyn.data.entities.hyperparameters import Hyperparameters, ChannelHyperparameters\n",
-    "from robyn.data.entities.calibration_input import (\n",
-    "    CalibrationInput,\n",
-    "    ChannelCalibrationData,\n",
-    ")\n",
-    "from robyn.data.entities.enums import AdstockType, DependentVarType, CalibrationScope"
+    "from robyn.data.entities.enums import AdstockType, DependentVarType"
    ]
   },
   {
@@ -65,10 +62,160 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "6f14728b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Simulated Data...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>DATE</th>\n",
+       "      <th>revenue</th>\n",
+       "      <th>tv_S</th>\n",
+       "      <th>ooh_S</th>\n",
+       "      <th>print_S</th>\n",
+       "      <th>facebook_I</th>\n",
+       "      <th>search_clicks_P</th>\n",
+       "      <th>search_S</th>\n",
+       "      <th>competitor_sales_B</th>\n",
+       "      <th>facebook_S</th>\n",
+       "      <th>events</th>\n",
+       "      <th>newsletter</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2015-11-23</td>\n",
+       "      <td>2.754372e+06</td>\n",
+       "      <td>22358.346667</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12728.488889</td>\n",
+       "      <td>2.430128e+07</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>8125009</td>\n",
+       "      <td>7607.132915</td>\n",
+       "      <td>na</td>\n",
+       "      <td>19401.653846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2015-11-30</td>\n",
+       "      <td>2.584277e+06</td>\n",
+       "      <td>28613.453333</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.527033e+06</td>\n",
+       "      <td>9837.238486</td>\n",
+       "      <td>4133.333333</td>\n",
+       "      <td>7901549</td>\n",
+       "      <td>1141.952450</td>\n",
+       "      <td>na</td>\n",
+       "      <td>14791.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2015-12-07</td>\n",
+       "      <td>2.547387e+06</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>132278.4</td>\n",
+       "      <td>453.866667</td>\n",
+       "      <td>1.665159e+07</td>\n",
+       "      <td>12044.119653</td>\n",
+       "      <td>3786.666667</td>\n",
+       "      <td>8300197</td>\n",
+       "      <td>4256.375378</td>\n",
+       "      <td>na</td>\n",
+       "      <td>14544.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2015-12-14</td>\n",
+       "      <td>2.875220e+06</td>\n",
+       "      <td>83450.306667</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17680.000000</td>\n",
+       "      <td>1.054977e+07</td>\n",
+       "      <td>12268.070319</td>\n",
+       "      <td>4253.333333</td>\n",
+       "      <td>8122883</td>\n",
+       "      <td>2800.490677</td>\n",
+       "      <td>na</td>\n",
+       "      <td>2800.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015-12-21</td>\n",
+       "      <td>2.215953e+06</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>277336.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.934090e+06</td>\n",
+       "      <td>9467.248023</td>\n",
+       "      <td>3613.333333</td>\n",
+       "      <td>7105985</td>\n",
+       "      <td>689.582605</td>\n",
+       "      <td>na</td>\n",
+       "      <td>15478.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         DATE       revenue          tv_S     ooh_S       print_S  \\\n",
+       "0  2015-11-23  2.754372e+06  22358.346667       0.0  12728.488889   \n",
+       "1  2015-11-30  2.584277e+06  28613.453333       0.0      0.000000   \n",
+       "2  2015-12-07  2.547387e+06      0.000000  132278.4    453.866667   \n",
+       "3  2015-12-14  2.875220e+06  83450.306667       0.0  17680.000000   \n",
+       "4  2015-12-21  2.215953e+06      0.000000  277336.0      0.000000   \n",
+       "\n",
+       "     facebook_I  search_clicks_P     search_S  competitor_sales_B  \\\n",
+       "0  2.430128e+07         0.000000     0.000000             8125009   \n",
+       "1  5.527033e+06      9837.238486  4133.333333             7901549   \n",
+       "2  1.665159e+07     12044.119653  3786.666667             8300197   \n",
+       "3  1.054977e+07     12268.070319  4253.333333             8122883   \n",
+       "4  2.934090e+06      9467.248023  3613.333333             7105985   \n",
+       "\n",
+       "    facebook_S events    newsletter  \n",
+       "0  7607.132915     na  19401.653846  \n",
+       "1  1141.952450     na  14791.000000  \n",
+       "2  4256.375378     na  14544.000000  \n",
+       "3  2800.490677     na   2800.000000  \n",
+       "4   689.582605     na  15478.000000  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Read the simulated data and holidays data\n",
     "dt_simulated_weekly = pd.read_csv(\"resources/dt_simulated_weekly.csv\")\n",
@@ -79,10 +226,98 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "182efb73",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Holidays Data...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ds</th>\n",
+       "      <th>holiday</th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1995-01-01</td>\n",
+       "      <td>New Year's Day</td>\n",
+       "      <td>AD</td>\n",
+       "      <td>1995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1995-01-06</td>\n",
+       "      <td>Epiphany</td>\n",
+       "      <td>AD</td>\n",
+       "      <td>1995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1995-02-28</td>\n",
+       "      <td>Carnival</td>\n",
+       "      <td>AD</td>\n",
+       "      <td>1995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1995-03-14</td>\n",
+       "      <td>Constitution Day</td>\n",
+       "      <td>AD</td>\n",
+       "      <td>1995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1995-04-14</td>\n",
+       "      <td>Good Friday</td>\n",
+       "      <td>AD</td>\n",
+       "      <td>1995</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           ds           holiday country  year\n",
+       "0  1995-01-01    New Year's Day      AD  1995\n",
+       "1  1995-01-06          Epiphany      AD  1995\n",
+       "2  1995-02-28          Carnival      AD  1995\n",
+       "3  1995-03-14  Constitution Day      AD  1995\n",
+       "4  1995-04-14       Good Friday      AD  1995"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "dt_prophet_holidays = pd.read_csv(\"resources/dt_prophet_holidays.csv\")\n",
     "\n",
@@ -102,10 +337,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "3a272787",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: Initialized Robyn in output\n",
+      "2024-12-06 17:41:15,096 - robyn.robyn - INFO - Initialized Robyn in output\n",
+      "INFO: Validating input data\n",
+      "2024-12-06 17:41:15,099 - robyn.robyn - INFO - Validating input data\n",
+      "2024-12-06 17:41:15,100 - robyn.data.validation.mmmdata_validation - INFO - Starting complete MMMData validation\n",
+      "2024-12-06 17:41:15,101 - robyn.data.validation.mmmdata_validation - INFO - Missing and infinite value check passed successfully\n",
+      "2024-12-06 17:41:15,103 - robyn.data.validation.mmmdata_validation - INFO - No-variance check passed successfully\n",
+      "2024-12-06 17:41:15,103 - robyn.data.validation.mmmdata_validation - INFO - Variable names validation passed successfully\n",
+      "2024-12-06 17:41:15,104 - robyn.data.validation.mmmdata_validation - INFO - Date variable validation passed successfully\n",
+      "2024-12-06 17:41:15,104 - robyn.data.validation.mmmdata_validation - INFO - Dependent variable validation passed successfully\n",
+      "2024-12-06 17:41:15,104 - robyn.data.validation.mmmdata_validation - INFO - All validations passed successfully\n",
+      "2024-12-06 17:41:15,104 - robyn.data.validation.holidays_data_validation - INFO - Starting complete validation process\n",
+      "2024-12-06 17:41:15,111 - robyn.data.validation.holidays_data_validation - INFO - Holidays validation completed. Status: True\n",
+      "2024-12-06 17:41:15,113 - robyn.data.validation.holidays_data_validation - INFO - Prophet validation completed. Status: True\n",
+      "2024-12-06 17:41:15,114 - robyn.data.validation.holidays_data_validation - INFO - Validation complete. Overall status: True\n",
+      "2024-12-06 17:41:15,114 - robyn.data.validation.hyperparameter_validation - INFO - Starting validation process\n",
+      "2024-12-06 17:41:15,114 - robyn.data.validation.hyperparameter_validation - INFO - Starting hyperparameters validation\n",
+      "2024-12-06 17:41:15,114 - robyn.data.validation.hyperparameter_validation - INFO - Hyperparameter validation completed. Status: True\n",
+      "2024-12-06 17:41:15,115 - robyn.data.validation.hyperparameter_validation - INFO - Validation completed with status: True\n",
+      "INFO: Data initialization complete\n",
+      "2024-12-06 17:41:15,115 - robyn.robyn - INFO - Data initialization complete\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Robyn initialized successfully!\n"
+     ]
+    }
+   ],
    "source": [
     "# Initialize Robyn\n",
     "robyn = Robyn(working_dir=\"output\")\n",
@@ -172,21 +442,6 @@
     "    train_size=[0.5, 0.8],\n",
     ")\n",
     "\n",
-    "\n",
-    "## Calibration is not supported yet\n",
-    "# Create CalibrationInput (using dummy data for demonstration)\n",
-    "# calibration_input = CalibrationInput({\n",
-    "#     \"tv_spend\": ChannelCalibrationData(\n",
-    "#         lift_start_date=pd.Timestamp(\"2022-03-01\"),\n",
-    "#         lift_end_date=pd.Timestamp(\"2022-03-15\"),\n",
-    "#         lift_abs=10000,\n",
-    "#         spend=50000,\n",
-    "#         confidence=0.9,\n",
-    "#         metric=\"revenue\",\n",
-    "#         calibration_scope=CalibrationScope.IMMEDIATE\n",
-    "#     )\n",
-    "# })\n",
-    "\n",
     "# Initialize Robyn\n",
     "robyn.initialize(\n",
     "    mmm_data=mmm_data,\n",
@@ -207,13 +462,69 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "72917fc5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: Performing feature engineering\n",
+      "2024-12-06 17:41:15,125 - robyn.robyn - INFO - Performing feature engineering\n",
+      "2024-12-06 17:41:15,126 - robyn.modeling.feature_engineering - INFO - Starting feature engineering process\n",
+      "2024-12-06 17:41:15,127 - robyn.modeling.feature_engineering - INFO - Starting Prophet decomposition\n",
+      "2024-12-06 17:41:15,128 - robyn.modeling.feature_engineering - INFO - Starting Prophet decomposition\n",
+      "2024-12-06 17:41:15,867 - cmdstanpy - DEBUG - input tempfile: /var/folders/gm/g5cpl7110m96nfd1qr1xwnwc0000gn/T/tmpw0hzz39w/k22wcyvw.json\n",
+      "2024-12-06 17:41:15,879 - cmdstanpy - DEBUG - input tempfile: /var/folders/gm/g5cpl7110m96nfd1qr1xwnwc0000gn/T/tmpw0hzz39w/ed8vl3cg.json\n",
+      "2024-12-06 17:41:15,882 - cmdstanpy - DEBUG - idx 0\n",
+      "2024-12-06 17:41:15,883 - cmdstanpy - DEBUG - running CmdStan, num_threads: None\n",
+      "2024-12-06 17:41:15,883 - cmdstanpy - DEBUG - CmdStan args: ['/Users/yijuilee/robynpy_release_reviews/robynvenv_py3.10/lib/python3.10/site-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=62786', 'data', 'file=/var/folders/gm/g5cpl7110m96nfd1qr1xwnwc0000gn/T/tmpw0hzz39w/k22wcyvw.json', 'init=/var/folders/gm/g5cpl7110m96nfd1qr1xwnwc0000gn/T/tmpw0hzz39w/ed8vl3cg.json', 'output', 'file=/var/folders/gm/g5cpl7110m96nfd1qr1xwnwc0000gn/T/tmpw0hzz39w/prophet_modele15jij3n/prophet_model-20241206174115.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
+      "17:41:15 - cmdstanpy - INFO - Chain [1] start processing\n",
+      "2024-12-06 17:41:15,883 - cmdstanpy - INFO - Chain [1] start processing\n",
+      "17:41:15 - cmdstanpy - INFO - Chain [1] done processing\n",
+      "2024-12-06 17:41:15,915 - cmdstanpy - INFO - Chain [1] done processing\n",
+      "2024-12-06 17:41:15,982 - robyn.modeling.feature_engineering - INFO - Prophet decomposition complete\n",
+      "2024-12-06 17:41:15,984 - robyn.modeling.feature_engineering - INFO - Starting model runs for paid media variables with different exposure metrics\n",
+      "2024-12-06 17:41:15,985 - robyn.modeling.feature_engineering - INFO - Fitting spend-exposure model for facebook_I\n",
+      "2024-12-06 17:41:15,997 - robyn.modeling.feature_engineering - INFO - Fitting spend-exposure model for search_clicks_P\n",
+      "2024-12-06 17:41:16,005 - robyn.modeling.feature_engineering - INFO - Completed model runs for 2 channels\n",
+      "2024-12-06 17:41:16,007 - robyn.modeling.feature_engineering - INFO - Feature engineering complete\n",
+      "2024-12-06 17:41:16,008 - robyn.modeling.feature_engineering - INFO - Filled 0 missing values\n",
+      "2024-12-06 17:41:16,009 - robyn.visualization.feature_visualization - INFO - Initializing FeaturePlotter\n",
+      "2024-12-06 17:41:16,010 - robyn.visualization.feature_visualization - INFO - Generating all plots\n",
+      "2024-12-06 17:41:16,010 - robyn.visualization.feature_visualization - INFO - Generating spend-exposure plot for channel: facebook_I\n",
+      "2024-12-06 17:41:16,010 - robyn.visualization.feature_visualization - INFO - Found result for channel facebook_I\n",
+      "2024-12-06 17:41:16,141 - robyn.visualization.feature_visualization - INFO - Successfully generated spend-exposure plot for channel facebook_I\n",
+      "2024-12-06 17:41:16,141 - robyn.visualization.feature_visualization - INFO - Generating spend-exposure plot for channel: search_clicks_P\n",
+      "2024-12-06 17:41:16,142 - robyn.visualization.feature_visualization - INFO - Found result for channel search_clicks_P\n",
+      "2024-12-06 17:41:16,296 - robyn.visualization.feature_visualization - INFO - Successfully generated spend-exposure plot for channel search_clicks_P\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
-    "robyn.feature_engineering()"
+    "robyn.feature_engineering();"
    ]
   },
   {
@@ -228,10 +539,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "08cc2ddb",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO: Training models\n",
+      "2024-12-06 17:41:16,487 - robyn.robyn - INFO - Training models\n",
+      "2024-12-06 17:41:16,492 - robyn.modeling.base_model_executor - INFO - Initializing BaseModelExecutor\n",
+      "2024-12-06 17:41:16,493 - robyn.modeling.model_executor - INFO - Starting model execution with model_name=Models.RIDGE\n",
+      "2024-12-06 17:41:16,493 - robyn.modeling.base_model_executor - INFO - Input validation successful\n",
+      "2024-12-06 17:41:16,494 - robyn.modeling.base_model_executor - INFO - Preparing hyperparameters\n",
+      "2024-12-06 17:41:16,495 - robyn.modeling.base_model_executor - INFO - Completed hyperparameter preparation with 20 parameters to optimize\n",
+      "2024-12-06 17:41:16,495 - robyn.modeling.model_executor - INFO - Initializing Ridge model builder\n",
+      "2024-12-06 17:41:16,495 - robyn.modeling.model_executor - INFO - Building models with configured parameters\n",
+      "2024-12-06 17:41:16,496 - robyn.modeling.ridge.ridge_data_builder - INFO - Collecting hyperparameters for optimization...\n",
+      "Running trial 1 of total 5 trials: 100%|███████████████████████████████████\n",
+      "2024-12-06 17:43:06,915 - robyn.modeling.ridge.ridge_evaluate_model - INFO -  Finished in 1.84 mins\n",
+      "Running trial 2 of total 5 trials: 100%|███████████████████████████████████\n",
+      "2024-12-06 17:44:51,560 - robyn.modeling.ridge.ridge_evaluate_model - INFO -  Finished in 1.74 mins\n",
+      "Running trial 3 of total 5 trials: 100%|███████████████████████████████████\n",
+      "2024-12-06 17:46:36,271 - robyn.modeling.ridge.ridge_evaluate_model - INFO -  Finished in 1.74 mins\n",
+      "Running trial 4 of total 5 trials:  67%|███████████████████████▌           "
+     ]
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "from robyn.modeling.entities.enums import Models, NevergradAlgorithm\n",
@@ -334,27 +669,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "from robyn.allocator.entities.allocation_params import AllocatorParams\n",
-    "from robyn.allocator.entities.allocation_result import (\n",
-    "    AllocationResult,\n",
-    "    OptimOutData,\n",
-    "    MainPoints,\n",
-    ")\n",
-    "from robyn.allocator.entities.optimization_result import OptimizationResult\n",
-    "from robyn.allocator.entities.constraints import Constraints\n",
-    "from robyn.allocator.optimizer import BudgetAllocator\n",
     "from robyn.allocator.constants import (\n",
     "    SCENARIO_MAX_RESPONSE,\n",
-    "    ALGO_SLSQP_AUGLAG,\n",
     "    CONSTRAINT_MODE_EQ,\n",
-    "    DEFAULT_CONSTRAINT_MULTIPLIER,\n",
-    "    DATE_RANGE_ALL,\n",
     ")\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
     "\n",
-    "# Create allocator parameters matching R Example 1\n",
+    "# Create allocator parameters\n",
     "allocator_params = AllocatorParams(\n",
     "    scenario=SCENARIO_MAX_RESPONSE,\n",
     "    total_budget=None,  # When None, uses total spend in date_range\n",
@@ -373,9 +694,7 @@
     "allocation_result = robyn.optimize_budget(\n",
     "    allocator_params=allocator_params,\n",
     "    select_model=None,\n",
-    ")\n",
-    "# Display the allocation result\n",
-    "print(allocation_result)"
+    ")"
    ]
   }
  ],
diff --git a/python/src/robyn/tutorials/tutorial1_src.ipynb b/python/src/robyn/tutorials/tutorial1_src.ipynb
index 340fc14f0..697b9c143 100644
--- a/python/src/robyn/tutorials/tutorial1_src.ipynb
+++ b/python/src/robyn/tutorials/tutorial1_src.ipynb
@@ -260,49 +260,6 @@
     "print(\"Model training complete.\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import Image, display\n",
-    "import base64\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import pandas as pd\n",
-    "\n",
-    "\n",
-    "# 1. Display the MOO Distribution Plot\n",
-    "if \"moo_distrb_plot\" in output_models.convergence:\n",
-    "    moo_distrb_plot = output_models.convergence[\"moo_distrb_plot\"]\n",
-    "    display(Image(data=base64.b64decode(moo_distrb_plot)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 2. Display the MOO Cloud Plot\n",
-    "if \"moo_cloud_plot\" in output_models.convergence:\n",
-    "    moo_cloud_plot = output_models.convergence[\"moo_cloud_plot\"]\n",
-    "    display(Image(data=base64.b64decode(moo_cloud_plot)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# # 4. Display time series validation and convergence plots\n",
-    "# if \"ts_validation_plot\" in output_models.convergence:\n",
-    "#     ts_validation_plot = output_models.convergence[\"ts_validation_plot\"]\n",
-    "#     display(Image(data=base64.b64decode(ts_validation_plot)))"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -525,12 +482,10 @@
     "from robyn.visualization.allocator_visualizer import (\n",
     "    AllocatorPlotter,\n",
     ")\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
+    "\n",
     "# Initialize plotter with just the essential data\n",
     "plotter = AllocatorPlotter(\n",
-    "    allocation_result=max_response_result,\n",
-    "    budget_allocator=max_response_allocator\n",
+    "    allocation_result=max_response_result, budget_allocator=max_response_allocator\n",
     ")\n",
     "\n",
     "# Generate all plots\n",
diff --git a/python/src/robyn/visualization/base_visualizer.py b/python/src/robyn/visualization/base_visualizer.py
index 0971e0067..4088a9c9d 100644
--- a/python/src/robyn/visualization/base_visualizer.py
+++ b/python/src/robyn/visualization/base_visualizer.py
@@ -12,7 +12,6 @@
 import base64
 import io
 
-# Configure logger
 logger = logging.getLogger(__name__)
 
 
diff --git a/python/src/robyn/visualization/feature_visualization.py b/python/src/robyn/visualization/feature_visualization.py
index 64d671767..711d9fda2 100644
--- a/python/src/robyn/visualization/feature_visualization.py
+++ b/python/src/robyn/visualization/feature_visualization.py
@@ -11,6 +11,7 @@
 from robyn.modeling.feature_engineering import FeaturizedMMMData
 from robyn.visualization.base_visualizer import BaseVisualizer
 
+
 logger = logging.getLogger(__name__)