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Titanic Survival Prediction Using Machine Learning

Ryan Ahmed

In this 1-hour long project-based course, we will predict titanic survivors’ using logistic regression and naïve bayes classifiers. The sinking of the Titanic is one of the key sad tragedies in history and it took place on April 15th, 1912. The numbers of survivors were low due to lack of lifeboats for all passengers. This practical guided project, we will analyze what sorts of people were likely to survive this tragedy with the power of machine learning.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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What's inside

Syllabus

Project Overview

In this project-based course, we will build, train and test a machine learning model to predict titanic survivors based on their features such as age, number of siblings, and ticket class. We will explore two machine learning algorithms, namely: (1) logistic regression classifier model and (2) Naive Bayes classifier model.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces learners to the theory behind logistic regression and Naïve Bayes, a common foundational element in many machine learning specialties

Applies machine learning to a real-world dataset, which translates concepts into useful models and improves comprehension

Helps learners develop strong problem-solving skills by using machine learning to address real-world problems

Teaches machine learning theory by walking the learner through every step of building and training a model, reinforcing these concepts

Instructs learners in the application of model building across varying data sets, a key skill for professionals in every industry where data is available

Provides hands-on practice with supervised machine learning techniques, such as logistic regression and Naïve Bayes, a standard foundational element in many machine learning specialties

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Titanic Survival Prediction Using Machine Learning with these activities:

Organize and review course materials

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Keep organized and optimize your learning by reviewing and consolidating course materials

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Organize notes, assignments, and quizzes into a central location
Review materials regularly to reinforce concepts and identify areas for improvement

Review statistical concepts

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Refresh your understanding of statistical concepts to build a strong foundation for the course

Browse courses on Statistical Concepts

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Review notes from previous statistics courses or textbooks
Work through practice problems to reinforce your understanding

Read 'An Introduction to Statistical Learning'

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Expand your understanding of statistical concepts and machine learning techniques for Titanic survivor prediction

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Read the book's chapters on logistic regression and naive Bayes classifiers
Work through the practice problems provided in the book

Two other activities

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Solve Titanic survivor prediction practice problems

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Practice solving Titanic survivor prediction problems to improve your understanding of logistic regression and naive Bayes classifiers

Browse courses on Machine Learning

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Find practice problems online or in textbooks
Solve the problems using the techniques learned in class
Check your solutions against provided answer keys

Build a Titanic survivor prediction model

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Apply your knowledge of logistic regression and naive Bayes classifiers to build a model that predicts Titanic survivors

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Gather and clean the Titanic dataset
Choose and apply appropriate machine learning algorithms
Evaluate the performance of your model

Career center

Learners who complete Titanic Survival Prediction Using Machine Learning will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist analyzes and interprets data to extract meaningful insights. By gaining the ability to utilize logistic regression and naïve Bayes classifiers to predict outcomes, you can significantly enhance your ability to make data-driven decisions. This course provides a solid foundation in machine learning techniques that can prove invaluable in this role.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers develop and implement machine learning models to solve real-world problems. This course offers a practical approach to building and evaluating machine learning models using logistic regression and naïve Bayes classifiers, providing you with a valuable skill set for this in-demand role.

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Business Analyst

Business Analysts analyze business processes and develop solutions to improve efficiency. By mastering logistic regression and naïve Bayes classifiers, you can enhance your ability to identify patterns and make data-driven recommendations, becoming a more effective Business Analyst.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. By gaining proficiency in logistic regression and naïve Bayes classifiers, you can develop optimization models and make data-driven decisions, enhancing your effectiveness in this role.

See salaries and explore the career path for Operations Research Analyst

Statistician

Statisticians apply statistical methods to analyze and interpret data. This course provides hands-on experience with logistic regression and naïve Bayes classifiers, equipping you with advanced techniques for data analysis and modeling, which are essential in this field.

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Risk Manager

Risk Managers identify and mitigate potential risks to an organization. This course provides a practical understanding of logistic regression and naïve Bayes classifiers, equipping you with the skills to assess and manage risks effectively, making you a valuable asset in this role.

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Market Researcher

Market Researchers collect and analyze data to understand consumer behavior and market trends. This course offers hands-on experience with logistic regression and naïve Bayes classifiers, providing you with valuable techniques for predicting customer preferences and making informed marketing decisions.

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Quantitative Analyst (Quant)

Quantitative Analysts (Quants) develop and implement mathematical and statistical models to analyze financial data. By learning logistic regression and naïve Bayes classifiers, you can enhance your ability to build predictive models and make informed decisions in the financial industry.

See salaries and explore the career path for Quantitative Analyst (Quant)

Data Analyst

Data Analysts collect, analyze, and interpret data to uncover trends and patterns. By mastering logistic regression and naïve Bayes classifiers, you can strengthen your ability to extract meaningful insights from data, making you a more effective Data Analyst.

See salaries and explore the career path for Data Analyst

Financial Analyst

Financial Analysts evaluate financial data and make investment recommendations. By gaining proficiency in logistic regression and naïve Bayes classifiers, you can enhance your ability to build financial models and make informed investment decisions, increasing your effectiveness in this role.

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Data Engineer

Data Engineers design and build systems to store, process, and analyze data. This course provides a solid foundation in logistic regression and naïve Bayes classifiers, equipping you with the skills to develop and maintain machine learning pipelines, making you a more effective Data Engineer.

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Actuary

Actuaries assess and manage financial risks. This course provides a practical introduction to logistic regression and naïve Bayes classifiers, enabling you to develop a solid understanding of risk assessment and prediction techniques commonly used in the actuarial field.

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Software Engineer

Software Engineers design, develop, and maintain software systems. While not directly related to machine learning, this course may be helpful in providing a foundation for developing software solutions that incorporate machine learning capabilities.

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Web Developer

Web Developers design and develop websites and web applications. While not directly related to machine learning, this course may be helpful in providing a foundation for developing web applications that incorporate machine learning capabilities.

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Database Administrator

Database Administrators manage and maintain databases. While not directly related to machine learning, this course may be helpful in providing a foundation for managing and maintaining databases used in machine learning applications.

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Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Titanic Survival Prediction Using Machine Learning.

The Eternal Darkness

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Describes the discovery and exploration of the Titanic wreck by Robert Ballard and his team. It provides a firsthand account of the expedition and the challenges involved in finding and documenting the wreck.

Titanic Survival Prediction Using Machine Learning

What's inside

Syllabus

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Activities

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Reading list

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