maralb83/titanic_survival_exploration_mlen

Titanic Survival Exploration: manual implementation of a decision tree.

Titanic_Survival_Exploration_MLEN

Titanic Survival Exploration: manual implementation of a decision tree. Select the file "titanic_survival_exploration.ipynb" to see the analysis.

Disclaimer

This project was done as part of Udacity's Machine Learning Engineer Nanodegree. It started as a template developed by Udacity which I completed with code of my own in order to uncover insights in the data and to answer the questions. I've also edited the notebook so it reads in a more linear fashion.

Data dependencies:

titanic_data.csv

This csv file contains a sample of the RMS Titanic data, composed of 891 rows and 11 columns:

• Survived: Outcome of survival (0 = No; 1 = Yes)
• Pclass: Socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)
• Name: Name of passenger
• Sex: Sex of the passenger
• Age: Age of the passenger (Some entries contain NaN)
• SibSp: Number of siblings and spouses of the passenger aboard
• Parch: Number of parents and children of the passenger aboard
• Ticket: Ticket number of the passenger
• Fare: Fare paid by the passenger
• Cabin Cabin number of the passenger (Some entries contain NaN)
• Embarked: Port of embarkation of the passenger (C = Cherbourg; Q = Queenstown; S = Southampton)

Other dependencies:

Python 3 version was used to run the Notebook.

visuals.py

This is a Python module that was made available by Udacity. It provides visualizations based on the columns that the user is interested in analyzing. I made minor changes so it could be run in Python 3.