diff --git a/helpers/find_param_importance.py b/helpers/find_param_importance.py
index a23ec79082e4f8f3bc6b09ea088ba05cd21adce4..27f6cc4116b2da1be20a03d1c711e3349db67378 100644
--- a/helpers/find_param_importance.py
+++ b/helpers/find_param_importance.py
@@ -195,6 +195,34 @@ class FindParamImportance:
             if correlated_features[0][i] != correlated_features[1][i]:
                 print(feature_names[correlated_features[0][i]], feature_names[correlated_features[1][i]])
 
+    def find_correlation_with_the_output(self):
+        data = self.get_data()
+        # get the features
+        X = data.drop('target', axis=1)
+        # get the target
+        y = data['target']
+
+        # find how correlated each feature is with the target
+        correlations = []
+        for i in range(len(X.columns)):
+            correlations.append(np.corrcoef(X.iloc[:, i], y)[0, 1])
+
+        # plot the correlations and make text to fit in the plot
+        plt.figure(figsize=(10, 6))
+        plt.bar(X.columns, correlations)
+        plt.xticks(rotation='vertical')
+        plt.tight_layout()
+        # create a legend
+        plt.axhline(y=0, color='black', linestyle='--')
+        plt.axhline(y=0.15, color='red', linestyle='--')
+        plt.axhline(y=-0.15, color='red', linestyle='--')
+        plt.legend(['0', '0.15', '-0.15'])
+        plt.title('Correlation with the output')
+        plt.savefig('correlation_with_the_output.png')
+
+    
+     
+
     def find_highest_params_values(self):
         data = self.get_data()
 
@@ -244,6 +272,7 @@ class FindParamImportance:
         # cluster_labels_dbscan = self.cluster_dbscan()
         self.find_correlation()
         self.find_highest_params_values()
+        self.find_correlation_with_the_output()
 
 
 if __name__ == '__main__':