Create analytics.py

src/ai/analytics.py

@@ -0,0 +1,57 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.ensemble import IsolationForest
+
+class Analytics:
+    def __init__(self, data):
+        self.data = data
+
+    def visualize_data(self):
+        """Visualize the data using various plots."""
+        plt.figure(figsize=(10, 6))
+        sns.countplot(x='risk_label', data=self.data)
+        plt.title('Distribution of Risk Labels')
+        plt.show()
+
+        plt.figure(figsize=(10, 6))
+        sns.heatmap(self.data.corr(), annot=True, fmt=".2f", cmap='coolwarm')
+        plt.title('Correlation Heatmap')
+        plt.show()
+
+    def trend_analysis(self, time_column, value_column):
+        """Analyze trends over time."""
+        self.data[time_column] = pd.to_datetime(self.data[time_column])
+        trend_data = self.data.groupby(time_column)[value_column].sum().reset_index()
+
+        plt.figure(figsize=(12, 6))
+        plt.plot(trend_data[time_column], trend_data[value_column], marker='o')
+        plt.title('Trend Analysis')
+        plt.xlabel('Time')
+        plt.ylabel(value_column)
+        plt.grid()
+        plt.show()
+
+    def detect_anomalies(self, feature_columns):
+        """Detect anomalies in the data using Isolation Forest."""
+        isolation_forest = IsolationForest(contamination=0.1)
+        self.data['anomaly'] = isolation_forest.fit_predict(self.data[feature_columns])
+        anomalies = self.data[self.data['anomaly'] == -1]
+
+        plt.figure(figsize=(10, 6))
+        plt.scatter(self.data[feature_columns[0]], self.data[feature_columns[1]], color='blue', label='Normal')
+        plt.scatter(anomalies[feature_columns[0]], anomalies[feature_columns[1]], color='red', label='Anomaly')
+        plt.title('Anomaly Detection')
+        plt.xlabel(feature_columns[0])
+        plt.ylabel(feature_columns[1])
+        plt.legend()
+        plt.show()
+
+    def generate_report(self):
+        """Generate a summaryreport of the analytics performed."""
+        summary = {
+            'total_records': len(self.data),
+            'risk_distribution': self.data['risk_label'].value_counts().to_dict(),
+            'correlation_matrix': self.data.corr().to_dict()
+        }
+        return summary