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Titanic Challenge: Data Analysis to Predict Passenger Survival

Authors: Francisco Alfaro Medina

Full Description:
I've created a tutorial to solve the Titanic challenge using data analysis and machine learning techniques in Python. The tutorial walks through the entire process, from exploratory data analysis (EDA) and feature engineering to building predictive models.

The main goal is to demonstrate how to apply these techniques using Python, with detailed explanations and practical examples. The data used in this tutorial comes from the Titanic dataset, which includes real-world data on the passengers of the RMS Titanic. This comprehensive tutorial has received positive feedback from data science enthusiasts and professionals alike for its practical approach and detailed explanations.

:key: Note: To learn more about this challenge, we invite you to use the following resources:

  • :open_book: Docs: Official documentation of the Titanic Challenge (using MkDocs together with GitHub Actions and GitHub Pages).
  • :crown: Streamlit: An interactive way to learn about the Titanic Challenge (featuring amazing libraries like great_tables, itables, plotly and more!).
  • :globe_with_meridians: Quarto Post: Step-by-step tutorial to solve the Titanic challenge (with Quarto!).

Table Type: interactive-HTML
Submission Type: Tutorial
Table: Personal Blog - Solving the Titanic Challenge
Code: GitHub - fralfaro/Titanic-Challenge: Step-by-Step tutorial to solve the Titanic Challenge with streamlit, jupyter notebooks and more!
Language:
Industries: .
Packages: great_tables, itables, pandas, plotly, sklearn, streamlit

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