From d49c5d77ee190c86bd78a9548d9fe934139eaea8 Mon Sep 17 00:00:00 2001
From: KOSASIH <kosasihg88@gmail.com>
Date: Mon, 19 Aug 2024 14:19:36 +0700
Subject: [PATCH] Create decision_trees.py

---
 .../ai_models/decision_trees.py               | 64 +++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 blockchain_integration/pi_network/pi-stablecoin/pi-coin-stabilization/ai_models/decision_trees.py

diff --git a/blockchain_integration/pi_network/pi-stablecoin/pi-coin-stabilization/ai_models/decision_trees.py b/blockchain_integration/pi_network/pi-stablecoin/pi-coin-stabilization/ai_models/decision_trees.py
new file mode 100644
index 000000000..9602c3929
--- /dev/null
+++ b/blockchain_integration/pi_network/pi-stablecoin/pi-coin-stabilization/ai_models/decision_trees.py
@@ -0,0 +1,64 @@
+import pandas as pd
+import numpy as np
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+
+class DecisionTreeModel:
+    def __init__(self, data, target_variable, test_size=0.2, random_state=42, max_depth=5):
+        self.data = data
+        self.target_variable = target_variable
+        self.test_size = test_size
+        self.random_state = random_state
+        self.max_depth = max_depth
+        self.model = None
+        self.X_train = None
+        self.X_test = None
+        self.y_train = None
+        self.y_test = None
+
+    def preprocess_data(self):
+        # Drop missing values
+        self.data.dropna(inplace=True)
+
+        # Scale features using StandardScaler
+        scaler = StandardScaler()
+        self.data[['feature1', 'feature2', ...]] = scaler.fit_transform(self.data[['feature1', 'feature2', ...]])
+
+        # Split data into training and testing sets
+        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.data.drop([self.target_variable], axis=1),
+                                                                              self.data[self.target_variable],
+                                                                              test_size=self.test_size,
+                                                                              random_state=self.random_state)
+
+    def train_model(self):
+        # Create pipeline with decision tree regressor
+        pipeline = Pipeline([
+            ('decision_tree', DecisionTreeRegressor(max_depth=self.max_depth))
+        ])
+
+        # Train model
+        pipeline.fit(self.X_train, self.y_train)
+
+        # Set model
+        self.model = pipeline
+
+    def evaluate_model(self):
+        # Make predictions on test set
+        y_pred = self.model.predict(self.X_test)
+
+        # Calculate mean squared error
+        mse = mean_squared_error(self.y_test, y_pred)
+
+        # Calculate R-squared score
+        r2 = r2_score(self.y_test, y_pred)
+
+        return mse, r2
+
+    def make_predictions(self, input_features):
+        # Make predictions using trained model
+        predictions = self.model.predict(input_features)
+
+        return predictions