Create market_analysis.py

src/analytics/market_analysis.py

@@ -0,0 +1,78 @@
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error
+import joblib
+import os
+
+class MarketAnalysis:
+    def __init__(self, model_file='market_analysis_model.pkl'):
+        self.model_file = model_file
+        self.model = None
+        self.load_model()
+
+    def load_model(self):
+        """Load the pre-trained market analysis model from a file."""
+        if os.path.exists(self.model_file):
+            self.model = joblib.load(self.model_file)
+            print("Market analysis model loaded successfully.")
+        else:
+            print("No pre-trained model found. Please train a model first.")
+
+    def train_model(self, data_file):
+        """Train a market analysis model using historical market data."""
+        # Load the dataset
+        data = pd.read_csv(data_file)
+
+        # Preprocess the data
+        X = data.drop('target_price', axis=1)  # Features
+        y = data['target_price']  # Target variable
+
+        # 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)
+
+        # Train a Random Forest Regressor
+        self.model = RandomForestRegressor(n_estimators=100, random_state=42)
+        self.model.fit(X_train, y_train)
+
+        # Evaluate the model
+        y_pred = self.model.predict(X_test)
+        mse = mean_squared_error(y_test, y_pred)
+        print(f"Model trained with Mean Squared Error: {mse:.2f}")
+
+        # Save the trained model
+        joblib.dump(self.model, self.model_file)
+        print(f"Model trained and saved to {self.model_file}.")
+
+    def predict_price(self, features):
+        """Predict the market price based on input features."""
+        if self.model is None:
+            return "Model not trained. Please train the model first."
+
+        # Convert features to DataFrame
+        features_df = pd.DataFrame([features])
+
+        # Make a prediction
+        predicted_price = self.model.predict(features_df)
+        return predicted_price[0]
+
+# Example usage
+if __name__ == "__main__":
+    market_analyzer = MarketAnalysis()
+
+    # Train the model with historical data
+    # Uncomment the line below to train the model with your dataset
+    # market_analyzer.train_model('historical_market_data.csv')
+
+    # Example features for prediction
+    example_features = {
+        'supply': 1000000,         # Example feature
+        'demand': 500000,          # Example feature
+        'market_sentiment': 0.8,   # Example feature
+        'trading_volume': 200000,   # Example feature
+        'volatility': 0.05         # Example feature
+    }
+
+    predicted_price = market_analyzer.predict_price(example_features)
+    print(f"Predicted Market Price: ${predicted_price:.2f}")