Analyzing Titanic Passenger Data: Insights and Challenges

Analyze Titanic dataset structure and address missing values to prepare data for modeling.

Analyze the Titanic dataset to understand passenger details, survival factors, and data quality considerations. Learn effective approaches for handling missing values and preparing data for predictive modeling.

Key Insights

  • The dataset includes information about 891 Titanic passengers, with survival status (1 for survived, 0 for died) serving as the target variable.
  • Cabin data is missing in approximately 75% of cases, thus it will be excluded from the predictive modeling.
  • Age, missing in nearly 20% of records, is considered valuable for prediction, while embarkation port data has minimal missing values that can be managed during preprocessing.

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Let's take a look at this data and see what is happening with it. First off, there are 891 people for whom we have information on the Titanic. We have quite a few columns to dive into here.

Passenger ID, survive (0 or 1). This is our target variable, Y. Did they survive or not? 1 means they survived, and 0 means they died.

Pclass is the passenger class for that particular passenger. First class, second class, third class. Their name, sex, and age.

This refers to siblings and spouses, people in the same generation. This refers to parents and children, people from different generations. We have their ticket, fare, cabin, and port of embarkation.

There are three ports where they could have boarded the Titanic. Let's take a look at which of this data is valid. We will examine the Titanic dataset's `isNA` to identify missing values.

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And we have a few that we need to address. We will ignore the cabin data, as it is missing in almost all instances. It is missing in about three-quarters of the data. We won't use it in our final dataset when we create X to train and test our model. Age is missing in approximately 20% of the data. We will address that issue. Age could be a valuable predictor, so we will retain it.

Embarked is missing only a few data points. We will address these missing values as we proceed. But that's where we are starting with our data.

Next, we'll explore how to use and clean this data.

Colin Jaffe

Colin Jaffe is a programmer, writer, and teacher with a passion for creative code, customizable computing environments, and simple puns. He loves teaching code, from the fundamentals of algorithmic thinking to the business logic and user flow of application building—he particularly enjoys teaching JavaScript, Python, API design, and front-end frameworks.

Colin has taught code to a diverse group of students since learning to code himself, including young men of color at All-Star Code, elementary school kids at The Coding Space, and marginalized groups at Pursuit. He also works as an instructor for Noble Desktop, where he teaches classes in the Full-Stack Web Development Certificate and the Data Science & AI Certificate.

Colin lives in Brooklyn with his wife, two kids, and many intricate board games.

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