Predictive Analytics with TensorFlow 3.1: Taking Decisions Based on Data – Titanic Example

The video tutorial covers decision-making challenges and the importance of data in supporting decisions. It explains the data value chain, including data integration and exploration, and highlights the need for predictive analytics. The tutorial uses the Titanic data set to demonstrate survival analysis and data visualization techniques. Finally, it provides a guide to installing and configuring TensorFlow for further data analysis.

What is the primary goal of the decision support process as discussed in the video?

• To support data management

• To collect as much data as possible

• To make the right decision at the right time

• To visualize data effectively

Which field in the Titanic dataset represents the socio-economic status of a passenger?

• Embarked

• Age

• Survival

• Pclass

What is the purpose of exploratory data analysis in the context of the Titanic dataset?

• To predict the exact number of survivors

• To understand the data distribution and relationships

• To install TensorFlow

• To create a new dataset

Which graph is used to show the distribution of passenger classes in the Titanic dataset?

• Pie chart

• Line graph

• Bar diagram

• Kernel density estimate

What is the first step in installing TensorFlow as mentioned in the video?

• Download the Titanic dataset

• Use pip install command

• Import necessary libraries

• Open a new Python notebook

Which command is used to upgrade an existing TensorFlow installation?

• tensorflow upgrade

• pip install upgrade tensorflow

• pip install tensorflow

• install tensorflow

What should you do if your Python interpreter doesn't complain after importing TensorFlow?

• Reinstall TensorFlow

• Start using TensorFlow

• Check for errors

• Install additional packages