input: Write a function to create a Deep Learning model using Keras to predict stock prices. The model should use LSTM layers and Dropout for regularization. The function should return the compiled model after fitting it with the training data. If the data file 'data.csv' is not found or if the data is not in the correct format, raise a suitable exception.
  assert type(create_stock_price_predictor())==keras.engine.sequential.Sequential
  assert len(create_stock_price_predictor().layers)==7
  assert create_stock_price_predictor().optimizer.get_config()['name']=='adam'
  assert create_stock_price_predictor().loss=='mean_squared_error'
output: def create_stock_price_predictor():
      import pandas as pd
      import numpy as np
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import LSTM, Dense, Dropout
      from sklearn.preprocessing import MinMaxScaler
      import os
  
      if not os.path.exists('data.csv'):
          raise FileNotFoundError("The data file 'data.csv' is not found.")
  
      try:
          data = pd.read_csv('data.csv')
          if 'Close' not in data.columns:
              raise ValueError("The data file does not contain a 'Close' column.")
      except Exception as e:
          raise ValueError(f"The data file is not in the correct format: {e}")
  
      scaler = MinMaxScaler(feature_range=(0, 1))
      scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1))
  
      X_train, y_train = [], []
      for i in range(60, len(scaled_data)):
          X_train.append(scaled_data[i-60:i, 0])
          y_train.append(scaled_data[i, 0])
      X_train, y_train = np.array(X_train), np.array(y_train)
      X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
  
      model = Sequential()
      model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
      model.add(Dropout(0.2))
      model.add(LSTM(units=50, return_sequences=True))
      model.add(Dropout(0.2))
      model.add(LSTM(units=50))
      model.add(Dropout(0.2))
      model.add(Dense(units=1))
  
      model.compile(optimizer='adam', loss='mean_squared_error')
      model.fit(X_train, y_train, epochs=20, batch_size=32)
  
      return model