Kaggle Titanic Challenge - AI Engineering Master's Project

This repository contains the code for my attempt to solve the Kaggle Titanic Challenge. The complete competition can be viewed here.

Project Description

This approach was developed as part of a course in my AI Engineering Master's program. One significant challenge was the limitation in the provided data: the "Sex" column was missing, making the task more difficult.

Key Points:

• Data: All used data can be found in the CSV folder.
• Notebook: The Jupyter Notebook is thoroughly documented and includes all necessary steps to reproduce the results.
• Result: With the chosen approach, a accuracy score of 81.1818% was achieved, placing this approach in the top 200 out of over 17,200 participants (as of July 2024).
• Usage: To replicate the results, simply run the notebook.