Create ai_driven_analitycs.py

ai_driven_analitycs/ai_driven_analitycs.py

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+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+class AIDrivenAnalytics:
+    def __init__(self, transaction_data_path):
+        self.transaction_data_path = transaction_data_path
+
+    def load_transaction_data(self):
+        transaction_data = pd.read_csv(self.transaction_data_path)
+        return transaction_data
+
+    def train_anomaly_detection_model(self, transaction_data):
+        X = transaction_data.drop(['is_anomaly'], axis=1)
+        y = transaction_data['is_anomaly']
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+        model = RandomForestClassifier(n_estimators=100, random_state=42)
+        model.fit(X_train, y_train)
+        return model
+
+    def detect_anomalies(self, transaction_data, model):
+        predictions = model.predict(transaction_data)
+        return predictions
+
+    def provide_personalized_recommendations(self, user_data, transaction_data):
+        # Implement personalized recommendation system using collaborative filtering or similar techniques
+        pass
+
+# Example usage:
+transaction_data_path = 'path/to/transaction_data.csv'
+ai_driven_analytics = AIDrivenAnalytics(transaction_data_path)
+
+transaction_data = ai_driven_analytics.load_transaction_data()
+model = ai_driven_analytics.train_anomaly_detection_model(transaction_data)
+predictions = ai_driven_analytics.detect_anomalies(transaction_data, model)
+print(predictions)
+
+user_data = pd.DataFrame({'user_id': [1, 2, 3], 'transaction_history': ['...']})
+personalized_recommendations = ai_driven_analytics.provide_personalized_recommendations(user_data, transaction_data)
+print(personalized_recommendations)