Predictive Analytics with TensorFlow 7.3:Using Multilayer Perceptrons for Predictive Analytics

The video tutorial covers predictive analytics using a bank marketing dataset. It explains the dataset's structure, attributes, and preprocessing steps required for machine learning models. The tutorial then demonstrates implementing a Multi-Layer Perceptron (MLP) using TensorFlow, including data normalization, hyperparameter tuning, and model training. The video concludes with evaluating the model's performance and discussing potential improvements.

What is the primary goal of using MLP in the bank marketing dataset?

• To analyze the employment variation rate

• To determine the number of contacts needed

• To classify whether a client will subscribe to a term deposit

• To predict the age of clients

Which dataset includes all examples and 20 inputs ordered by date?

• bank_additional_full.csv

• bank_additional.csv

• bank.csv

• bank_full.csv

What is the dependent variable in the bank marketing dataset?

• Age

• Job

• Marital status

• Subscription to term deposit

Why is it necessary to convert categorical variables into numerical values?

• To increase the number of features

• To ensure compatibility with MLP and DBN classifiers

• To improve the visualization of data

• To reduce the size of the dataset

Which library is used for data preprocessing in this tutorial?

• Scikit-learn

• NumPy

• Pandas

• Matplotlib

What is the purpose of using dropout in the MLP model?

• To prevent overfitting by randomly deactivating neurons

• To increase the number of neurons

• To enhance the visualization of the model

• To speed up the training process

Which optimizer is used to minimize the training loss in the MLP model?

• SGD

• RMSProp

• Adam

• Adagrad

What is the classification accuracy achieved by the MLP model?

• 92%

• 89%

• 95%

• 79%

What is a potential alternative to MLP for achieving higher accuracy?

• Deep Belief Network

• Support Vector Machine

• Recurrent Neural Network

• Convolutional Neural Network

What does the cross-entropy loss indicate about the model's performance?

• It is increasing steadily

• It is decreasing steadily

• It is stable with minor fluctuations

• It is highly unstable