input: Write a function to create a Deep Learning model using Keras to predict stock prices. The function should return the compiled model after training it with the given dataset. The dataset will be loaded from 'data.csv' and should be split into training and testing sets. The model should be trained for 50 epochs with a batch size of 32.
  
  assert create_stock_price_model() != None
  assert create_stock_price_model().optimizer.get_config()['name'] == 'adam'
  assert create_stock_price_model().loss == 'mean_squared_error'
output: def create_stock_price_model():
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      data = pd.read_csv('data.csv')
      X = data.drop('target', axis=1)
      y = data['target']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = Sequential()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1))
  
      model.compile(optimizer='adam', loss='mean_squared_error')
      model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))
  
      return model