input: Write a function to create an AI-based system in Python for predicting stock market movements. The function should load data from 'data.csv', split it into training and testing sets, build a deep learning model using TensorFlow, train the model, and evaluate its performance.
  assert predict_stock_market_movement()==None
  assert predict_stock_market_movement()==None
  assert predict_stock_market_movement()==None
  assert predict_stock_market_movement()==None
  assert predict_stock_market_movement()==None
output: def predict_stock_market_movement():
      import pandas as pd
      import numpy as np
      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, activation='sigmoid'))
  
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
  
      loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
      return None