Titanic Survival Exploration and Data Analysis

Description

This project creates decision functions using data analytic approaches to predict survival outcomes from the 1912 Titanic disaster based on each passenger’s features, such as sex and age.

Install

This project ran tested on Python 2.7 and Python 3.7 and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• scikit-learn

You will also need to have software installed to run and execute a Jupyter Notebook

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included.

Code

The code is provided in the notebook titanic_survival_exploration.ipynb notebook file.

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 Jupyter Notebook software and project file in your web browser.

Data

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

Features

• 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)

Target Variable

• survival : Survival (0 = No; 1 = Yes)