Create linear_regression.py

blockchain_integration/pi_network/pi-stablecoin/pi-coin-stabilization/ai_models/linear_regression.py

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+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LinearRegression
+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 LinearRegressionModel:
+    def __init__(self, data, target_variable, test_size=0.2, random_state=42):
+        self.data = data
+        self.target_variable = target_variable
+        self.test_size = test_size
+        self.random_state = random_state
+        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 linear regression model
+        pipeline = Pipeline([
+            ('linear_regression', LinearRegression())
+        ])
+
+        # 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