타이타닉호 그것이 알고 싶다-Titanic Data Insights

Example

Identifying key statistics and distributions within the Titanic dataset, such as the average age of passengers or the proportion of survivors by ticket class.

Scenario

A user uploads the Titanic dataset seeking to understand the basic demographics of the passengers. The gizmo provides a detailed exploration, revealing insights such as the average age, gender distribution, and class-wise survival rates.

Example

Examining the relationship between passenger class and survival rates, or between fare paid and survival rate.

Scenario

In a scenario where a researcher is investigating the factors influencing survival rates on the Titanic, the gizmo analyzes the dataset to find significant correlations, offering insights into how different variables interact and affect survival chances.

Example

Using machine learning models to predict the survival chances of passengers based on input variables such as age, sex, class, and number of companions aboard.

Scenario

A user interested in machine learning provides specific passenger data to predict survival outcomes. The gizmo applies predictive modeling techniques to estimate the likelihood of survival for these individuals under similar circumstances.