input: Write a function to create an AI-based system in Python for predicting stock market movements using a deep learning model. The function should import necessary libraries, load the dataset, split it into training and testing data, build and train the model, and finally evaluate the model.
  assert stock_market_prediction()==None
  assert stock_market_prediction.model=='Sequential'
  assert type(stock_market_prediction.score)==float
output: def stock_market_prediction():
      import numpy as np
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from keras.models import Sequential
      from keras.layers import Dense
      from sklearn.metrics import mean_squared_error
  
      # Load dataset
      data = pd.read_csv('stock_data.csv')
      X = data.drop('target', axis=1)
      y = data['target']
  
      # Split data
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Build model
      model = Sequential()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # Compile and train model
      model.compile(optimizer='adam', loss='mean_squared_error')
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
  
      # Evaluate model
      y_pred = model.predict(X_test)
      score = mean_squared_error(y_test, y_pred)
  
      # Attach model and score to function
      stock_market_prediction.model = model
      stock_market_prediction.score = score
  
      return None