An ensemble model is a model that uses multiple dierent models to build a better model. E.g., a random forest classifier uses multiple decision trees to "vote" for the best answer. For this exercise, assume that an ensemble model uses majority vote to predict a binary target and that each individual model is independent of every other model. Use Python code to answer the following questions. (a) If an ensemble model is composed of 15 individual models that each have 63% accuracy, what is the ensemble model accuracy? (b) What would the accuracy of the 15 individual models need to be in order for the ensemble model to have 95% accuracy? Find the smallest whole number percentage so this is satisfied. (c) If each individual model has 63% accuracy, how many individual models are needed for the ensemble model to have 95% accuracy? Find the smallest odd number of models (so there is a clear majority), so that this is satisfied. Hint: The binom package from scipy.stats is useful for this problem, but make sure you are using the correct functions for your calculations