From 9fa99081263515840757470eedd6784b58078ac5 Mon Sep 17 00:00:00 2001 From: KOSASIH Date: Wed, 4 Sep 2024 18:56:51 +0700 Subject: [PATCH] Create ai_driven_analitycs.py --- ai_driven_analitycs/ai_driven_analitycs.py | 41 ++++++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100644 ai_driven_analitycs/ai_driven_analitycs.py diff --git a/ai_driven_analitycs/ai_driven_analitycs.py b/ai_driven_analitycs/ai_driven_analitycs.py new file mode 100644 index 000000000..bd74f1153 --- /dev/null +++ b/ai_driven_analitycs/ai_driven_analitycs.py @@ -0,0 +1,41 @@ +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)