Keras ValueError: cannot reshape array of size

I’m facing an error which I can’t understand using Keras for a prediction task.

Here is my code:

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import LSTM, Dense, Dropout
import matplotlib.pyplot as plt
import finpy_tse as fpy

# Define the stock symbol and time period
stock_symbol="وغدیر"
start_date="1400-01-01"
end_date="1402-01-01"

# Download stock price data from the Tehran Stock Exchange using finpy_tse
stock_data = fpy.Get_Price_History(stock_symbol, start_date, end_date)

# Print the fetched data
print("Fetched data from the site:")
print(stock_data)

data = pd.DataFrame(stock_data)

# Download stock price data from the Tehran Stock Exchange using finpy_tse
stock_data = fpy.Get_Price_History(stock_symbol, start_date, end_date)
data = pd.DataFrame(stock_data)

# Print columns to inspect the structure of the DataFrame
print(data.columns)

# Feature engineering: Creating additional features if needed
# Assuming the 'Close' or 'Final' and 'Volume' columns are present in the DataFrame
if 'Close' in data.columns and 'Volume' in data.columns:
    data['DailyReturn'] = data['Close'].pct_change()
    data['VolumeChange'] = data['Volume'].pct_change()

    # Drop missing values
    data = data.dropna()

    # Check if the dataset is empty after dropping missing values
    if data.empty:
        print("Empty dataset after dropping missing values. Unable to proceed.")
    else:
        # Use multiple features for training
        features = ['DailyReturn', 'VolumeChange']
        dataset = data[features].values

        # Check if the dataset is empty after feature engineering
        if dataset.shape[0] == 0:
            print("Empty dataset after feature engineering. Unable to proceed.")
        else:
            # Normalize the data only if the dataset is not empty
            scaler = MinMaxScaler(feature_range=(0, 1))
            dataset_scaled = scaler.fit_transform(dataset)

            # Continue with the rest of your code...

            # Create a time series dataset
            def create_dataset(dataset, time_steps=1):
                X, y = [], []
                for i in range(len(dataset) - time_steps):
                    a = dataset[i:(i + time_steps), :]
                    X.append(a)
                    y.append(dataset[i + time_steps, 0])  # Predicting DailyReturn
                return np.array(X), np.array(y)

            # Define the time steps (you can adjust this parameter)
            time_steps = 10

            # Create the time series dataset
            X, y = create_dataset(dataset_scaled, time_steps)

            # Print shape and size for debugging
            print("Before reshaping - X shape:", X.shape, "X size:", X.size)

            # Reshape data for LSTM model [samples, time steps, features]
            X = np.reshape(X, (1, X.shape[0], X.shape[1]))

            # Build the LSTM model with dropout for better generalization
            model = Sequential()
            model.add(LSTM(units=100, return_sequences=True, input_shape=(X.shape[1], X.shape[2])))
            model.add(Dropout(0.2))
            model.add(LSTM(units=100, return_sequences=True))
            model.add(Dropout(0.2))
            model.add(LSTM(units=50, return_sequences=False))
            model.add(Dropout(0.2))
            model.add(Dense(units=1))
            model.compile(optimizer="adam", loss="mean_squared_error")

            # Train the model
            model.fit(X, y, epochs=10, batch_size=32, validation_split=0.1)

            # Test the model on new data
            test_data = dataset_scaled[-time_steps:]
            test_data = np.reshape(test_data, (1, time_steps, len(features)))
            predicted_price = model.predict(test_data)

            # Inverse transform and reshape the predicted_price
            predicted_price = scaler.inverse_transform(np.reshape(predicted_price, (1, time_steps, len(features))))
            print("Shapes for debugging:")
            print("test_data shape:", test_data.shape)
            print("predicted_price shape:", predicted_price.shape)

            # Reshape and inverse transform
            predicted_price = scaler.inverse_transform(np.reshape(predicted_price, (time_steps, len(features))))

            # Assuming you are interested in the first column of the prediction
            predicted_price = predicted_price[0, :, 0]

            # Visualize the results
            plt.plot(data['Close'].values, label="Actual Stock Price")
            plt.plot(np.arange(len(data['Close']), len(data['Close']) + time_steps), predicted_price, marker="o", color="red",
                     label="Predicted Stock Price")
            plt.xlabel('Days')
            plt.ylabel('Stock Price')
            plt.title(f'Stock Price Prediction for {stock_symbol} using LSTM')
            plt.legend()
            plt.show()
else:
    print("The 'Close' or 'Volume' columns are not present in the DataFrame.")
    print("Please check your stock price data or provide a DataFrame with these columns.")

and the error is:

Traceback (most recent call last): File
“…:/Users/…../PycharmProjects/untitled1/…..py”, line 76, in

X = np.reshape(X, (1, X.shape[0], X.shape[1])) File “<array_function internals>”, line 180, in reshape File
“….:\Users…..\PycharmProjects\untitled1\venv\lib\site-packages\numpy\core\fromnumeric.py”,
line 298, in reshape
return _wrapfunc(a, ‘reshape’, newshape, order=order) File “….:\Users….\PycharmProjects\untitled1\venv\lib\site-packages\numpy\core\fromnumeric.py”,
line 57, in _wrapfunc
return bound(*args, **kwds) ValueError: cannot reshape array of size 9200 into shape (1,460,10)