Binary-Class Tabular Model for Kaggle Spaceship Titanic Using Python and Scikit-Learn

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Kaggle Spaceship Titanic dataset is a binary-class modeling situation where we attempt to predict one of two possible outcomes.

INTRODUCTION: In 2912, the Spaceship Titanic, an interstellar passenger liner, launched with almost 13,000 passengers on board. The vessel set out on its maiden voyage transporting emigrants from our solar system to three newly habitable exoplanets orbiting nearby stars.

While rounding Alpha Centauri en route to its first destination, the unwary Spaceship Titanic collided with a spacetime anomaly hidden within a dust cloud. Sadly, it met a similar fate as its namesake from 1000 years before. Though the ship stayed intact, almost half of the passengers were transported to an alternate dimension!

This incident presented a challenge to the data scientists of the future. To help rescue crews and retrieve the lost passengers, the data scientists tried to build a model that predicted which passengers were transported by the anomaly using records recovered from the spaceship’s damaged computer system.

ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 78.30% after training. Furthermore, we selected Random Forest as the final model as it processed the training dataset with an accuracy score of 79.55%. When we processed the test dataset with the final model, the model achieved an accuracy score of 79.58%.

CONCLUSION: In this iteration, the Random Forest model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Spaceship Titanic Dataset

Dataset ML Model: Binary-Class classification with numerical and categorical features

Dataset Reference:

One source of potential performance benchmarks:

The HTML formatted report can be found here on GitHub.