Trying to fix flake8

src/griffin-stuff/IBKR/predict_price.py

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+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.callbacks import EarlyStopping
+
+# Load the training and testing data
+training_data = pd.read_csv("3_years_training_data.csv")
+testing_data = pd.read_csv("3_month_testing_data.csv")
+
+# Drop unnecessary columns
+training_data = training_data.drop(columns=["Unnamed: 0", "Date"])
+testing_data = testing_data.drop(columns=["Unnamed: 0", "Date"])
+
+# Create lagged features for the model
+def create_lagged_features(data, n_lags=3):
+    df = data.copy()
+    for lag in range(1, n_lags + 1):
+        df[f'Close_lag_{lag}'] = df['Close'].shift(lag)
+    df.dropna(inplace=True)  # Remove rows with NaN values due to shifting
+    return df
+
+# Apply lagged features to the training and testing datasets
+training_data = create_lagged_features(training_data)
+testing_data = create_lagged_features(testing_data)
+
+# Separate features and target
+X_train = training_data.drop(columns=["Close"]).values
+y_train = training_data["Close"].values
+X_test = testing_data.drop(columns=["Close"]).values
+y_test = testing_data["Close"].values
+
+# Standardize the features
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+# Build the neural network model
+model = Sequential([
+    Dense(64, activation='sigmoid', input_shape=(X_train.shape[1],)),
+    Dense(32, activation='sigmoid'),
+    Dense(16, activation='sigmoid'),
+    Dense(1)  # Output layer for regression
+])
+
+# Compile the model
+model.compile(optimizer='adam', loss='mse', metrics=['mae'])
+
+# Use early stopping to prevent overfitting
+early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
+
+# Train the model
+history = model.fit(
+    X_train, y_train,
+    epochs=100,
+    batch_size=32,
+    validation_split=0.2,
+    callbacks=[early_stopping],
+    verbose=1
+)
+
+# Evaluate the model on the test set
+y_pred = model.predict(X_test).flatten()
+mse = mean_squared_error(y_test, y_pred)
+mae = mean_absolute_error(y_test, y_pred)
+
+print(f"Neural Network MSE: {mse:.2f}")
+print(f"Neural Network MAE: {mae:.2f}")
+
+# Prepare the latest data to predict tomorrow's price
+latest_data = testing_data.tail(1).drop(columns=["Close"])
+latest_data_scaled = scaler.transform(latest_data)
+
+# Predict tomorrow's close price
+tomorrow_pred = model.predict(latest_data_scaled)
+print(f"Predicted Close Price for Tomorrow: {tomorrow_pred[0][0]:.2f}")