diff --git a/projects/PiNetAI/data/init.py b/projects/PiNetAI/data/init.py
new file mode 100644
index 000000000..093bc5a68
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+++ b/projects/PiNetAI/data/init.py
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+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Load data
+node_data = pd.read_csv('data/node_data.csv')
+transaction_data = pd.read_csv('data/transaction_data.csv')
+
+# Merge data
+data = pd.merge(node_data, transaction_data, on='node_id')
+
+# Preprocess data
+data['timestamp'] = pd.to_datetime(data['timestamp'])
+data['amount'] = data['amount'].astype(float)
+
+# Split data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(data.drop('category', axis=1), data['category'], test_size=0.2, random_state=42)
+
+# Train random forest classifier
+rf = RandomForestClassifier(n_estimators=100, random_state=42)
+rf.fit(X_train, y_train)
+
+# Make predictions
+y_pred = rf.predict(X_test)
+
+# Evaluate model
+accuracy = accuracy_score(y_test, y_pred)
+print('Accuracy:', accuracy)
+print('Classification Report:')
+print(classification_report(y_test, y_pred))
+print('Confusion Matrix:')
+print(confusion_matrix(y_test, y_pred))
+
+# Visualize results
+sns.set_style('whitegrid')
+plt.figure(figsize=(10, 6))
+sns.countplot(x='category', data=data)
+plt.title('Category Distribution')
+plt.xlabel('Category')
+plt.ylabel('Count')
+plt.show()
+
+# Save model
+rf.save('model.pkl')