benhawkins18/titanic_survival_manual

Getting over 80% accuracy using a manually created decision tree

Project 0: Introduction and Fundamentals

Titanic Survival Exploration

Install

This project requires Python 2.7 and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• scikit-learn

You will also need to have software installed to run and execute an iPython Notebook

Udacity recommends our students install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.

Code

Template code is provided in the notebook titanic_survival_exploration.ipynb notebook file. Additional supporting code can be found in titanic_visualizations.py. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project.

Run

In a terminal or command window, navigate to the top-level project directory titanic_survival_exploration/ (that contains this README) and run one of the following commands:

jupyter notebook titanic_survival_exploration.ipynb

or

ipython notebook titanic_survival_exploration.ipynb

This will open the iPython Notebook software and project file in your web browser.

Data

The dataset used in this project is included as titanic_data.csv. This dataset is provided by Udacity and contains the following attributes:

• survival : Survival (0 = No; 1 = Yes)
• pclass : Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)
• name : Name
• sex : Sex
• age : Age
• sibsp : Number of Siblings/Spouses Aboard
• parch : Number of Parents/Children Aboard
• ticket : Ticket Number
• fare : Passenger Fare
• cabin : Cabin
• embarked : Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)