From 32b541277ed7c2d7beb021f3b67a7f371944226b Mon Sep 17 00:00:00 2001
From: KOSASIH <kosasihg88@gmail.com>
Date: Wed, 28 Aug 2024 10:17:50 +0700
Subject: [PATCH] Update visualization.py

---
 .../visualization.py                          | 33 +++++++++++++++++--
 1 file changed, 30 insertions(+), 3 deletions(-)

diff --git a/ai-powered-risk-management-system/visualization.py b/ai-powered-risk-management-system/visualization.py
index 6cf0e276c..d3cbf971c 100644
--- a/ai-powered-risk-management-system/visualization.py
+++ b/ai-powered-risk-management-system/visualization.py
@@ -1,14 +1,33 @@
 import matplotlib.pyplot as plt
 import seaborn as sns
+import pandas as pd
 
 def visualize_risk_scores(fraud_score, liquidity_score, stability_score):
-    # Visualize risk scores using a heatmap
+    """
+    Visualize risk scores using a heatmap.
+
+    Args:
+        fraud_score (float): Fraud detection score
+        liquidity_score (float): Liquidity risk assessment score
+        stability_score (float): Network stability evaluation score
+
+    Returns:
+        None
+    """
     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
+    """
+    Visualize market trends using a line chart.
+
+    Args:
+        market_data (pd.DataFrame): Market data
+
+    Returns:
+        None
+    """
     plt.plot(market_data['date'], market_data['price'])
     plt.xlabel('Date')
     plt.ylabel('Price')
@@ -16,7 +35,15 @@ def visualize_market_trends(market_data):
     plt.show()
 
 def visualize_user_behavior(user_behavior_data):
-    # Visualize user behavior using a bar chart
+    """
+    Visualize user behavior using a bar chart.
+
+    Args:
+        user_behavior_data (pd.DataFrame): User behavior data
+
+    Returns:
+        None
+    """
     plt.bar(user_behavior_data['user_id'], user_behavior_data['transaction_count'])
     plt.xlabel('User ID')
     plt.ylabel('Transaction Count')