Update app.py

app.py

@@ -5,6 +5,7 @@ from sklearn.linear_model import LinearRegression
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.preprocessing import StandardScaler
 from sklearn.model_selection import train_test_split
+from sklearn.impute import SimpleImputer
 from datetime import datetime, timedelta
 import numpy as np
 
@@ -45,6 +46,10 @@ stock_data.dropna(inplace=True)
 X = stock_data[['Open', 'High', 'Low', 'Volume', 'MA_10', 'MA_50', 'RSI', 'Return']]
 y = stock_data['Close']
 
+# Handle missing values
+imputer = SimpleImputer(strategy='mean')
+X = imputer.fit_transform(X)
+
 # Split the data
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
@@ -63,10 +68,21 @@ rf_model.fit(X_train_scaled, y_train)
 
 # Predict future prices using ensemble method
 future_dates = [stock_data['Date'].iloc[-1] + timedelta(days=x) for x in range(1, 15)]
-future_df = pd.DataFrame(index=future_dates, columns=X.columns)
+future_df = pd.DataFrame(index=future_dates, columns=stock_data.columns)
+future_df['Open'] = stock_data['Open'].iloc[-1]
+future_df['High'] = stock_data['High'].iloc[-1]
+future_df['Low'] = stock_data['Low'].iloc[-1]
+future_df['Volume'] = stock_data['Volume'].iloc[-1]
+future_df['MA_10'] = stock_data['MA_10'].iloc[-1]
+future_df['MA_50'] = stock_data['MA_50'].iloc[-1]
+future_df['RSI'] = stock_data['RSI'].iloc[-1]
+future_df['Return'] = stock_data['Return'].iloc[-1]
 future_df = future_df.fillna(method='ffill')
 
-future_X_scaled = scaler.transform(future_df)
+# Handle missing values in future data
+future_X = imputer.transform(future_df[['Open', 'High', 'Low', 'Volume', 'MA_10', 'MA_50', 'RSI', 'Return']])
+future_X_scaled = scaler.transform(future_X)
+
 lr_predictions = lr_model.predict(future_X_scaled)
 rf_predictions = rf_model.predict(future_X_scaled)