This project requires Python 3.X and the following Python libraries installed:
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 3.X installer and not the Python 2.7 installer.
Template code is provided in the notebook titanic_survival_exploration.ipynb notebook file. Additional supporting code can be found in visuals.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. Note that the code included in visuals.py is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.
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:
git clone https://github.com/cakmakaf/titanic_survival_exploration.git
This will open the Jupyter Notebook software and project file in your web browser.
The dataset used in this project is included as titanic_data.csv. This dataset is provided by Udacity and contains the features of the passengers, such as name, sex, age, etc.
Target Variable
survival: Survival (0 = No; 1 = Yes)
In this optional project, we will create a decision functions to predict survival outcomes from the 1912 Titanic disaster based on each passenger’s features. We start with a simple classification and increase the algorithm's copmlexity until the accuracy hits to 80% at least.
The project provides us an idea about one of the fundamental concepts of machine learning such as decision tree and we apply decision trees to implement results out of the data.