input: Write a python function to determine whether a customer should be contacted based on a linear regression model using sklearn.
  assert customer_contact_decision('data.csv') == ['Yes', 'No', 'Yes', ...]
  assert customer_contact_decision('new_data.csv') == ['No', 'No', 'Yes', ...]
  assert customer_contact_decision('another_data.csv') == ['Yes', 'Yes', 'No', ...]
output: def customer_contact_decision(file_path):
      import pandas as pd
      from sklearn.linear_model import LinearRegression
  
      # Load the data
      data = pd.read_csv(file_path)
      
      # Assuming the last column is the target variable and the rest are features
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
      
      # Train the linear regression model
      model = LinearRegression()
      model.fit(X, y)
      
      # Make predictions
      predictions = model.predict(X)
      
      # Determine the decision based on the predictions
      decision_threshold = 0.5  # Assuming a threshold of 0.5 for classification
      decisions = ['Yes' if pred >= decision_threshold else 'No' for pred in predictions]
      
      return decisions