Create asset_management_model.py

Author: KOSASIH

models/asset_management_model.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, classification_report
+import joblib
+
+# Load your dataset
+# For demonstration, we will create a synthetic dataset
+# In a real scenario, you would load your dataset from a CSV or database
+data = {
+    'asset_type': ['stock', 'bond', 'stock', 'bond', 'stock', 'real estate', 'real estate', 'bond', 'stock', 'real estate'],
+    'risk_level': [1, 0, 1, 0, 1, 2, 2, 0, 1, 2],  # 0: Low, 1: Medium, 2: High
+    'return': [10, 5, 12, 3, 15, 8, 9, 4, 11, 7]  # Example feature
+}
+
+df = pd.DataFrame(data)
+
+# Encode categorical variables
+df['asset_type'] = df['asset_type'].astype('category').cat.codes
+
+# Features and target variable
+X = df[['asset_type', 'risk_level', 'return']]
+y = df['asset_type']  # Predict asset type
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Model selection and hyperparameter tuning using GridSearchCV
+param_grid = {
+    'n_estimators': [50, 100, 200],
+    'max_depth': [None, 10, 20, 30],
+    'min_samples_split': [2, 5, 10]
+}
+
+# Using Random Forest Classifier for better performance
+model = RandomForestClassifier(random_state=42)
+
+# Grid search for hyperparameter tuning
+grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, scoring='accuracy', verbose=2, n_jobs=-1)
+grid_search.fit(X_train, y_train)
+
+# Best model from grid search
+best_model = grid_search.best_estimator_
+
+# Evaluate the model
+y_pred = best_model.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+report = classification_report(y_test, y_pred)
+
+print(f'Accuracy: {accuracy}')
+print('Classification Report:')
+print(report)
+
+# Save the model
+joblib.dump(best_model, 'models/asset_management_model.pkl')
+print('Asset management model saved as asset_management_model.pkl')