diff --git a/tools/base_model_trainer.py b/tools/base_model_trainer.py
index c530237..9a93530 100644
--- a/tools/base_model_trainer.py
+++ b/tools/base_model_trainer.py
@@ -4,10 +4,10 @@
 
 from feature_importance import FeatureImportanceAnalyzer
 
-from utils import get_html_template, get_html_closing
-
 import pandas as pd
 
+from utils import get_html_closing, get_html_template
+
 logging.basicConfig(level=logging.DEBUG)
 LOG = logging.getLogger(__name__)
 
@@ -113,12 +113,22 @@ def save_html_report(self):
 
         model_name = type(self.best_model).__name__
         excluded_params = ['html', 'log_experiment', 'system_log']
-        filtered_setup_params = {k: v for k, v in self.setup_params.items() if k not in excluded_params}
-        setup_params_table = pd.DataFrame(list(filtered_setup_params.items()), columns=['Parameter', 'Value'])
-
-        best_model_params = pd.DataFrame(self.best_model.get_params().items(), columns=['Parameter', 'Value'])
-        best_model_params.to_csv(os.path.join(self.output_dir, 'best_model.csv'), index=False)
-        self.results.to_csv(os.path.join(self.output_dir, "comparison_results.csv"))
+        filtered_setup_params = {
+            k: v
+            for k, v in self.setup_params.items() if k not in excluded_params
+        }
+        setup_params_table = pd.DataFrame(
+            list(filtered_setup_params.items()),
+            columns=['Parameter', 'Value'])
+
+        best_model_params = pd.DataFrame(
+            self.best_model.get_params().items(),
+            columns=['Parameter', 'Value'])
+        best_model_params.to_csv(
+            os.path.join(self.output_dir, 'best_model.csv'),
+            index=False)
+        self.results.to_csv(os.path.join(
+            self.output_dir, "comparison_results.csv"))
 
         plots_html = ""
         for plot_name, plot_path in self.plots.items():
@@ -126,31 +136,41 @@ def save_html_report(self):
             plots_html += f"""
             <div class="plot">
                 <h3>{plot_name.capitalize()}</h3>
-                <img src="data:image/png;base64,{encoded_image}" alt="{plot_name}">
+                <img src="data:image/png;base64,{encoded_image}"
+                    alt="{plot_name}">
             </div>
             """
 
-        analyzer = FeatureImportanceAnalyzer(data=self.data, target_col=self.target_col, task_type='classification', output_dir=self.output_dir)
+        analyzer = FeatureImportanceAnalyzer(
+            data=self.data,
+            target_col=self.target_col,
+            task_type='classification',
+            output_dir=self.output_dir)
         feature_importance_html = analyzer.run()
 
         html_content = f"""
         {get_html_template()}
             <h1>PyCaret Model Training Report</h1>
             <div class="tabs">
-                <div class="tab" onclick="openTab(event, 'summary')">Setup & Best Model</div>
-                <div class="tab" onclick="openTab(event, 'plots')">Best Model Plots</div>
-                <div class="tab" onclick="openTab(event, 'feature')">Feature Importance</div>
+                <div class="tab" onclick="openTab(event, 'summary')">
+                Setup & Best Model</div>
+                <div class="tab" onclick="openTab(event, 'plots')">
+                Best Model Plots</div>
+                <div class="tab" onclick="openTab(event, 'feature')">
+                Feature Importance</div>
             </div>
             <div id="summary" class="tab-content">
                 <h2>Setup Parameters</h2>
                 <table>
                     <tr><th>Parameter</th><th>Value</th></tr>
-                    {setup_params_table.to_html(index=False, header=False, classes='table')}
+                    {setup_params_table.to_html(
+                        index=False, header=False, classes='table')}
                 </table>
                 <h2>Best Model: {model_name}</h2>
                 <table>
                     <tr><th>Parameter</th><th>Value</th></tr>
-                    {best_model_params.to_html(index=False, header=False, classes='table')}
+                    {best_model_params.to_html(
+                        index=False, header=False, classes='table')}
                 </table>
                 <h2>Comparison Results</h2>
                 <table>
@@ -167,7 +187,8 @@ def save_html_report(self):
         {get_html_closing()}
         """
 
-        with open(os.path.join(self.output_dir, "comparison_result.html"), "w") as file:
+        with open(os.path.join(
+                self.output_dir, "comparison_result.html"), "w") as file:
             file.write(html_content)
 
     def save_dashboard(self):
diff --git a/tools/feature_importance.py b/tools/feature_importance.py
index ec098df..c0aec03 100644
--- a/tools/feature_importance.py
+++ b/tools/feature_importance.py
@@ -2,12 +2,11 @@
 import logging
 import os
 
-import pandas as pd
-
 import matplotlib.pyplot as plt
 
-from pycaret.classification import ClassificationExperiment
+import pandas as pd
 
+from pycaret.classification import ClassificationExperiment
 from pycaret.regression import RegressionExperiment
 
 logging.basicConfig(level=logging.DEBUG)
@@ -15,7 +14,14 @@
 
 
 class FeatureImportanceAnalyzer:
-    def __init__(self, task_type, output_dir, data_path=None, data=None,  target_col=None):
+    def __init__(
+            self,
+            task_type,
+            output_dir,
+            data_path=None,
+            data=None,
+            target_col=None):
+
         if data is not None:
             self.data = data
         else:
@@ -25,7 +31,9 @@ def __init__(self, task_type, output_dir, data_path=None, data=None,  target_col
             self.data = self.data.fillna(self.data.median(numeric_only=True))
         self.task_type = task_type
         self.target = self.data.columns[int(target_col) - 1]
-        self.exp = ClassificationExperiment() if task_type == 'classification' else RegressionExperiment()
+        self.exp = ClassificationExperiment() \
+            if task_type == 'classification' \
+            else RegressionExperiment()
         self.plots = {}
         self.output_dir = output_dir
 
@@ -57,10 +65,14 @@ def save_tree_importance(self):
             'Importance': importances
         }).sort_values(by='Importance', ascending=False)
         plt.figure(figsize=(10, 6))
-        plt.barh(feature_importances['Feature'], feature_importances['Importance'])
+        plt.barh(
+            feature_importances['Feature'],
+            feature_importances['Importance'])
         plt.xlabel('Importance')
         plt.title('Feature Importance (Random Forest)')
-        plot_path = os.path.join(self.output_dir, 'tree_importance.png')
+        plot_path = os.path.join(
+            self.output_dir,
+            'tree_importance.png')
         plt.savefig(plot_path)
         plt.close()
         self.plots['tree_importance'] = plot_path
@@ -69,10 +81,13 @@ def save_shap_values(self):
         model = self.exp.create_model('lightgbm')
         import shap
         explainer = shap.Explainer(model)
-        shap_values = explainer.shap_values(self.data.drop(columns=[self.target]))
-        shap.summary_plot(shap_values, self.data.drop(columns=[self.target]), show=False)
+        shap_values = explainer.shap_values(
+            self.data.drop(columns=[self.target]))
+        shap.summary_plot(shap_values, self.data.drop(
+            columns=[self.target]), show=False)
         plt.title('Shap (LightGBM)')
-        plot_path = os.path.join(self.output_dir, 'shap_summary.png')
+        plot_path = os.path.join(
+            self.output_dir, 'shap_summary.png')
         plt.savefig(plot_path)
         plt.close()
         self.plots['shap_summary'] = plot_path
@@ -86,7 +101,7 @@ def generate_feature_importance(self):
     def encode_image_to_base64(self, img_path):
         with open(img_path, 'rb') as img_file:
             return base64.b64encode(img_file.read()).decode('utf-8')
-    
+
     def generate_html_report(self, coef_html):
         LOG.info("Generating HTML report")
 
@@ -96,12 +111,15 @@ def generate_html_report(self, coef_html):
             encoded_image = self.encode_image_to_base64(plot_path)
             plots_html += f"""
             <div class="plot" id="{plot_name}">
-                <h2>Feature importance analysis from a trained Random Forest</h2>
-                <h3>{'Use gini impurity for calculating feature importance for classification' 
-                     'and Variance Reduction for regression'
-                  if plot_name == 'tree_importance' 
+                <h2>Feature importance analysis from a
+                    trained Random Forest</h2>
+                <h3>{'Use gini impurity for'
+                    'calculating feature importance for classification'
+                    'and Variance Reduction for regression'
+                  if plot_name == 'tree_importance'
                   else 'SHAP Summary from a trained lightgbm'}</h3>
-                <img src="data:image/png;base64,{encoded_image}" alt="{plot_name}">
+                <img src="data:image/png;base64,
+                {encoded_image}" alt="{plot_name}">
             </div>
             """
 
@@ -110,8 +128,10 @@ def generate_html_report(self, coef_html):
             <h1>PyCaret Feature Importance Report</h1>
 
             <div id="coefficients" class="tabcontent">
-                <h2>Coefficients (based on a trained 
-                {'Logistic Regression' if self.task_type == 'classification' else 'Linear Regression'} Model)</h2>
+                <h2>Coefficients (based on a trained
+                {'Logistic Regression'
+                if self.task_type == 'classification'
+                else 'Linear Regression'} Model)</h2>
                 <div>{coef_html}</div>
             </div>
             {plots_html}
@@ -127,14 +147,26 @@ def run(self):
         LOG.info("Feature importance analysis completed")
         return html_content
 
+
 if __name__ == "__main__":
     import argparse
     parser = argparse.ArgumentParser(description="Feature Importance Analysis")
-    parser.add_argument("--data_path", type=str, help="Path to the dataset")
-    parser.add_argument("--target_col", type=int, help="Index of the target column (1-based)")
-    parser.add_argument("--task_type", type=str, choices=["classification", "regression"], help="Task type: classification or regression")
-    parser.add_argument("--output_dir", type=str, help="Directory to save the outputs")
+    parser.add_argument(
+        "--data_path", type=str, help="Path to the dataset")
+    parser.add_argument(
+        "--target_col", type=int,
+        help="Index of the target column (1-based)")
+    parser.add_argument(
+        "--task_type", type=str,
+        choices=["classification", "regression"],
+        help="Task type: classification or regression")
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        help="Directory to save the outputs")
     args = parser.parse_args()
 
-    analyzer = FeatureImportanceAnalyzer(args.data_path, args.target_col, args.task_type, args.output_dir)
+    analyzer = FeatureImportanceAnalyzer(
+        args.data_path, args.target_col,
+        args.task_type, args.output_dir)
     analyzer.run()
diff --git a/tools/utils.py b/tools/utils.py
index 45acf42..e4ae7f7 100644
--- a/tools/utils.py
+++ b/tools/utils.py
@@ -84,6 +84,7 @@ def get_html_template():
     <div class="container">
     """
 
+
 def get_html_closing():
     return """
         </div>
@@ -96,7 +97,8 @@ def get_html_closing():
                 }}
                 tablinks = document.getElementsByClassName("tab");
                 for (i = 0; i < tablinks.length; i++) {{
-                    tablinks[i].className = tablinks[i].className.replace(" active-tab", "");
+                    tablinks[i].className =
+                        tablinks[i].className.replace(" active-tab", "");
                 }}
                 document.getElementById(tabName).style.display = "block";
                 evt.currentTarget.className += " active-tab";