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Content: Introduction and Foundations

Project: Titanic Survival Exploration

Project Overview

Welcome to the Machine Learning Engineer Nanodegree!

In this optional project, you will create decision functions that attempt to predict survival outcomes from the 1912 Titanic disaster based on each passenger's features, such as sex and age. You will start with a simple algorithm and increase its complexity until you are able to accurately predict the outcomes for at least 80% of the passengers in the provided data. This project will introduce you to some of the concepts of machine learning as you start the Nanodegree program.

In addition, you'll make sure Python is installed with the necessary packages to complete this project. There are two Python libraries, numpy and pandas, that we'll use a bit here in this project. Don't worry about how these libraries work for now -- we'll get to them in more detail in later projects. This project will also familiarize you with the submission process for the projects that you will be completing as part of the Nanodegree program.

Software Requirements

This project uses the following software and Python libraries:

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. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.

If you already have Python 2.7 installed on your computer, then you can install numpy, pandas, matplotlib and Jupyter Notebook (formerly known as "iPython") by using pip on the command line. This page may also be of use for some packages for Windows users, if pip has trouble performing the installation. After installing pip, you can install all the packages with the following command:

sudo pip install numpy pandas matplotlib jupyter

Starting the Project

For this assignment, you can find the titanic_survival_exploration folder containing the necessary project files on the Machine Learning projects GitHub, under the projects folder. You may download all of the files for projects we'll use in this Nanodegree program directly from this repo. Please make sure that you use the most recent version of project files when completing a project!

This project contains three files:

  • titanic_survival_exploration.ipynb: This is the main file where you will be performing your work on the project.
  • titanic_data.csv: The project dataset. You?ll load this data in the notebook.
  • visuals.py: This Python script provides supplementary visualizations for the project. Do not modify.

In the Terminal or Command Prompt, navigate to the folder containing the project files, and then use the command jupyter notebook titanic_survival_exploration.ipynb to open up a browser window or tab to work with your notebook. Alternatively, you can use the command jupyter notebook or ipython notebook and navigate to the notebook file in the browser window that opens. Follow the instructions in the notebook and answer each question presented to successfully complete the project. A README file has also been provided with the project files which may contain additional necessary information or instruction for the project.

Submitting the Project

Evaluation

Your project will be reviewed by a Udacity reviewer against the Titanic Survival Exploration project rubric. Be sure to review this rubric thoroughly and self-evaluate your project before submission. All criteria found in the rubric must be meeting specifications for you to pass.

Submission Files

When you are ready to submit your project, collect the following files and compress them into a single archive for upload. Alternatively, you may supply the following files on your GitHub Repo in a folder named titanic_survival_exploration for ease of access:

  • The titanic_survival_exploration.ipynb notebook file with all questions answered and all code cells executed and displaying output.
  • An HTML export of the project notebook with the name report.html. Instructions for exporting to HTML are at the bottom of the notebook; you may need to install the mistune package first, e.g. via pip install mistune in the terminal.

Once you have collected these files and reviewed the project rubric, proceed to the project submission page.

I'm Ready!

When you're ready to submit your project, click on the Submit Project button at the bottom of the page.

If you are having any problems submitting your project or wish to check on the status of your submission, please email us at [email protected] or visit us in the discussion forums.

What's Next?

You will get an email as soon as your reviewer has feedback for you. In the meantime, review your next project and feel free to get started on it or the courses supporting it!