Create pi_weather_forecaster.py

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

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+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+from sklearn.preprocessing import StandardScaler
+
+class PiWeatherForecaster:
+    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.X_train = None
+        self.X_test = None
+        self.y_train = None
+        self.y_test = None
+        self.models = []
+
+    def preprocess_data(self):
+        # Scale the data using StandardScaler
+        scaler = StandardScaler()
+        self.data[self.data.columns] = scaler.fit_transform(self.data)
+
+        # 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_models(self):
+        # Create and train multiple models
+        models = [
+            RandomForestRegressor(n_estimators=100),
+            # Add more models here
+        ]
+
+        for model in models:
+            model.fit(self.X_train, self.y_train)
+            self.models.append(model)
+
+    def evaluate_models(self):
+        # Evaluate each model using mean squared error and mean absolute error
+        results = []
+        for model in self.models:
+            y_pred = model.predict(self.X_test)
+            mse = mean_squared_error(self.y_test, y_pred)
+            mae = mean_absolute_error(self.y_test, y_pred)
+            results.append((model.__class__.__name__, mse, mae))
+
+        return results
+
+    def make_predictions(self, input_data):
+        # Make predictions using the best model
+        best_model = min(self.models, key=lambda x: x.score(self.X_test, self.y_test))
+        predictions = best_model.predict(input_data)
+        return predictions