Simple Classification of Titanic Dataset, Take 2

Methodology Credit: Adapted from a tutorial made available by Trevor Stephens, Titanic: Getting Started With R.

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

Data Set Description: https://www.kaggle.com/c/titanic/data

Benchmark References: https://www.kaggle.com/c/titanic/data

For the take #2 version of the project, we will add the fourth and fifth iterations of experimenting with several machine learning algorithms. We will see whether the machine learning algorithms can improve our predictions.

  1. Label all passengers dead or the attribute $Survived = 0 (The worst-case scenario)
  2. Label all female passengers survived or the attribute $Survived = 1
  3. Label all female passengers with Pclass=3 and Fare > 20 dead or the attribute $Survived = 0
  4. Leverage machine learning algorithms to generate predictions
  5. Tune the best-performing algorithm by experimenting with various parameters

The HTML formatted report can be found here on GitHub.