input: Write a python function to split a dataset into training and testing sets and train a linear regression model.
  ```python
  def train_linear_regression_model(data, target_column, test_size=0.3, random_state=101):
      X = data.drop([target_column], axis=1)
      y = data[target_column]
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
      model = LinearRegression()
      model.fit(X_train, y_train)
      accuracy = model.score(X_test, y_test)
      return model, accuracy
  
  # Test cases
  data = pd.DataFrame({
      'Feature1': np.random.rand(100),
      'Feature2': np.random.rand(100),
      'Price': np.random.rand(100)
  })
  
  model, accuracy = train_linear_regression_model(data, 'Price')
  assert 0 <= accuracy <= 1
  
  data = pd.DataFrame({
      'Feature1': np.random.rand(200),
      'Feature2': np.random.rand(200),
      'Price': np.random.rand(200)
  })
  
  model, accuracy = train_linear_regression_model(data, 'Price', test_size=0.2)
  assert 0 <= accuracy <= 1
output: import pandas as pd
  import numpy as np
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LinearRegression
  
  def train_linear_regression_model(data, target_column, test_size=0.3, random_state=101):
      X = data.drop([target_column], axis=1)
      y = data[target_column]
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
      model = LinearRegression()
      model.fit(X_train, y_train)
      accuracy = model.score(X_test, y_test)
      return model, accuracy
  
  # Test cases
  data = pd.DataFrame({
      'Feature1': np.random.rand(100),
      'Feature2': np.random.rand(100),
      'Price': np.random.rand(100)
  })
  
  model, accuracy = train_linear_regression_model(data, 'Price')
  assert 0 <= accuracy <= 1
  
  data = pd.DataFrame({
      'Feature1': np.random.rand(200),
      'Feature2': np.random.rand(200),
      'Price': np.random.rand(200)
  })
  
  model, accuracy = train_linear_regression_model(data, 'Price', test_size=0.2)
  assert 0 <= accuracy <= 1