Create visualization.py

ai-powered-risk-management-system/visualization.py

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+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def visualize_risk_scores(fraud_score, liquidity_score, stability_score):
+    # Visualize risk scores using a heatmap
+    risk_scores = pd.DataFrame({'Fraud Score': [fraud_score], 'Liquidity Score': [liquidity_score], 'Stability Score': [stability_score]})
+    sns.heatmap(risk_scores, annot=True, cmap='coolwarm', square=True)
+    plt.show()
+
+def visualize_market_trends(market_data):
+    # Visualize market trends using a line chart
+    plt.plot(market_data['date'], market_data['price'])
+    plt.xlabel('Date')
+    plt.ylabel('Price')
+    plt.title('Market Trends')
+    plt.show()
+
+def visualize_user_behavior(user_behavior_data):
+    # Visualize user behavior using a bar chart
+    plt.bar(user_behavior_data['user_id'], user_behavior_data['transaction_count'])
+    plt.xlabel('User ID')
+    plt.ylabel('Transaction Count')
+    plt.title('User Behavior')
+    plt.show()