diff --git a/src/stabilizer/QuantumEconomicModelingSystem.py b/src/stabilizer/QuantumEconomicModelingSystem.py
index d9a3e1c..5be26a1 100644
--- a/src/stabilizer/QuantumEconomicModelingSystem.py
+++ b/src/stabilizer/QuantumEconomicModelingSystem.py
@@ -1,12 +1,38 @@
+import numpy as np
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+import matplotlib.pyplot as plt
+import seaborn as sns
+
 class QuantumEconomicModelingSystem:
     def __init__(self):
         self.economic_simulation_parameters = {
             'target_valuation': 314.159,
-            'global_economic_integration_depth': 0.95  # 95% integration potential
+            'global_economic_integration_depth': 0.95,  # 95% integration potential
+            'quantum_factor': 1.618,  # Example quantum factor for simulations
+            'market_trends_data': self._fetch_market_trends()
         }
-    
+        self.model = LinearRegression()
+
+    def _fetch_market_trends(self):
+        # Simulate fetching market trends data
+        # In a real scenario, this would pull from a live database or API
+        return pd.DataFrame({
+            'year': np.arange(2000, 2024),
+            'market_growth_rate': np.random.uniform(1, 10, 24)  # Random growth rates
+        })
+
+    def _calculate_market_potential(self):
+        # Use machine learning to predict future market potential based on historical data
+        X = self.economic_simulation_parameters['market_trends_data']['year'].values.reshape(-1, 1)
+        y = self.economic_simulation_parameters['market_trends_data']['market_growth_rate'].values
+        self.model.fit(X, y)
+        future_years = np.array([[2025], [2026], [2027]])
+        predicted_growth = self.model.predict(future_years)
+        return predicted_growth
+
     def simulate_global_economic_scenarios(self):
-        return {
+        scenarios = {
             'economic_integration_scenarios': [
                 'conservative_adoption',
                 'moderate_expansion',
@@ -14,3 +40,20 @@ def simulate_global_economic_scenarios(self):
             ],
             'projected_global_market_penetration': self._calculate_market_potential()
         }
+        self._visualize_scenarios(scenarios)
+        return scenarios
+
+    def _visualize_scenarios(self, scenarios):
+        plt.figure(figsize=(10, 6))
+        sns.lineplot(x=np.arange(2025, 2028), y=scenarios['projected_global_market_penetration'], marker='o')
+        plt.title('Projected Global Market Penetration')
+        plt.xlabel('Year')
+        plt.ylabel('Market Growth Rate (%)')
+        plt.xticks(np.arange(2025, 2028))
+        plt.grid()
+        plt.show()
+
+# Example usage
+quantum_economic_model = QuantumEconomicModelingSystem()
+results = quantum_economic_model.simulate_global_economic_scenarios()
+print(results)