Allianz Financial Services (AFS) is a banking group offering end-to-end banking and financial solutions in South East Asia through its consumer banking, business banking, Islamic banking, investment finance and stock broking businesses as well as unit trust and asset administration, having served the financial community over the past five decades. AFS being one the largest banks in the region is planning to improve its segment business by launching a campaign to identify potential customers for various new products launched based on their past behavior? AFS is looking for both batch and real-time predictive analytics Management drives marketing team to recommend new products to Customers and upscale revenue.
Management is setting up forecasts and want to know how much more revenue can be generated by each Marketing team in the region end of every quarter.
IT team is providing information about Marketing team, their customers and prospects, previous actuals and other information. Please advise.Select 2 options.
Click on the arrows to vote for the correct answer
A. B. C. D. E. F. G.Answer : C and G.
Option A is incorrect.
ML models for binary classification problems predict a binary outcome.
https://docs.aws.amazon.com/machine-learning/latest/dg/types-of-ml-models.htmlOption B is incorrect.
ML models for multiclass classification problems allow you to generate predictions for multiple classes (predict one of more than two outcomes).
https://docs.aws.amazon.com/machine-learning/latest/dg/types-of-ml-models.htmlOption C is Correct.
ML models for regression problems predict a numeric value.
https://docs.aws.amazon.com/machine-learning/latest/dg/types-of-ml-models.htmlOption D is incorrect.
Amazon ML provides an industry-standard accuracy metric for binary classification models called Area Under the (Receiver Operating Characteristic) Curve (AUC).
https://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.htmlOption E is incorrect.
Cross-validation is a technique for evaluating ML models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data.
Use cross- validation to detect overfitting.
https://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.htmlOption F is incorrect.
The macro-average F1 score is used to evaluate the predictive accuracy of a multiclass metric.
https://docs.aws.amazon.com/machine-learning/latest/dg/multiclass-model-insights.htmlOption G is Correct.
For linear regression tasks, Amazon ML uses the industry standard root mean square error (RMSE) metric.
https://docs.aws.amazon.com/machine-learning/latest/dg/regression-model-insights.htmlThe business problem that Allianz Financial Services (AFS) is trying to solve is to improve its segment business by launching a campaign to identify potential customers for various new products launched based on their past behavior. AFS is looking for both batch and real-time predictive analytics. Management drives marketing team to recommend new products to Customers and upscale revenue. Management is setting up forecasts and want to know how much more revenue can be generated by each Marketing team in the region end of every quarter. The IT team is providing information about Marketing team, their customers and prospects, previous actuals, and other information.
To address this business problem, AFS can leverage Amazon Machine Learning (ML) service. Amazon ML is a cloud-based service that makes it easy for developers to build predictive models by using data. It is a fully-managed service that provides scalable machine learning algorithms that can be used to build predictive models.
There are a few options to consider:
A. Amazon ML uses logistic regression algorithm through Binary classification to solve the business problem.
Logistic regression is a statistical model that is used to analyze the relationship between a dependent variable and one or more independent variables. It is a binary classification algorithm that can be used to predict the probability of an event occurring. In this case, AFS can use logistic regression to predict which customers are more likely to buy the new products based on their past behavior.
B. Amazon ML uses multi-nominal logistic regression algorithm through multi-class classification to solve the business problem.
Multi-nominal logistic regression is a classification algorithm that is used when there are more than two categories in the dependent variable. In this case, AFS can use multi-nominal logistic regression to classify customers into different categories based on their past behavior and predict which category they are more likely to belong to.
C. Amazon ML uses linear regression algorithm through regression model to solve the business problem.
Linear regression is a statistical model that is used to analyze the relationship between a dependent variable and one or more independent variables. It is a regression algorithm that can be used to predict a continuous numerical value. In this case, AFS can use linear regression to predict how much revenue can be generated by each Marketing team in the region based on their past performance and other factors.
D. Amazon ML uses Area Under the (Receiver Operating Characteristic) Curve (AUC) to provide accuracy of the model.
Area Under the ROC Curve (AUC) is a metric that is used to evaluate the performance of a binary classification algorithm. It measures the ability of the algorithm to distinguish between positive and negative examples. In this case, AFS can use AUC to evaluate the performance of the logistic regression algorithm used to predict which customers are more likely to buy the new products.
E. Cross-validation is a technique for evaluating ML models by training several ML models on subsets of the available input data to detect overfitting which eventually fails to generalize the pattern.
Cross-validation is a technique used to evaluate the performance of ML models by training the model on a subset of the available data and testing it on the remaining data. It is used to detect overfitting, which occurs when the model is too complex and fits the training data too well, but fails to generalize to new data. In this case, AFS can use cross-validation to evaluate the performance of the models used to predict which customers are more likely to buy the new products.
F. Amazon ML uses macro-average F1 score to provide accuracy of the model.
The F1 score is a metric that is used to evaluate the performance of a classification algorithm. It measures the balance between precision and recall. The macro-average F1 score is the average F1 score across all classes. In this case, AFS can use the macro-average F1 score to evaluate the performance of the multi-n