View project page here: https://kacox.github.io/titanicml/
This project is an analysis of and deployment of a machine learning algorithm on the Titanic Dataset from Kaggle.com.
Part 1: Investigate a Dataset
The dataset ("titanic_data.csv") contains information for 891 passengers with 12 variables. The goal of this investigation was to determine if any variables correlate with passenger survival.
Tools/technologies: Python, NumPy, Pandas, Seaborn
Part 2: Machine Learning Addendum
For this part of the project, a machine learning algorithm is developed and deployed to predict which passengers survived the sinking of the Titanic. Part 1 (finding which passenger characteristics correlate with survival) serves as a basis for feature selection in this addendum.
Two sets of data are used: "train.csv" and "test.csv". "train.csv" has 891 data points and 12 variables while "test.csv" has 418 data points and 11 variables.
Tools/technologies: Python, NumPy, Pandas, scikit-learn
Information on dataset
PassengerId: unique identifier for passenger
Survived: Survival; 0 = No, 1 = Yes
Pclass: Ticket class, a proxy for socio-economic status (SES); 1 = 1st (Upper), 2 = 2nd (Middle), 3 = 3rd (Lower)
Name: Name of passenger
Sex: Sex of passenger
Age: Age in years
Sibsp: # of siblings / spouses aboard the Titanic
Parch: # of parents / children aboard the Titanic
Ticket: Ticket number
Fare: Passenger fare
Cabin: Cabin number
Embarked: Port of Embarkation; C = Cherbourg, Q = Queenstown, S = Southampton
Variable Notes
age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5
sibsp: The dataset defines family relations in this way...
Sibling = brother, sister, stepbrother, stepsister
Spouse = husband, wife (mistresses and fiancés were ignored)
parch: The dataset defines family relations in this way...
Parent = mother, father
Child = daughter, son, stepdaughter, stepson
Some children travelled only with a nanny, therefore parch=0 for them.
index.html - Main file of finished project (HTML file)
Titanic_investigate.html - Part 1 report (HTML file)
Titanic_ML.html - Part 2 report (HTML file)
Titanic.ipynb - Investigation code for Part 1 (iPython notebook)
Titanic_ML.ipynb - Investigation code for Part 2 (iPython notebook)
titanic_data.csv - Data source for Part I (CSV)
train.csv - Training data for Part II (CSV)
test.csv - Test data for Part II (CSV)
submission.csv - Predictions from Part II (CSV)
README.md - Information on data set and variables (Markdown file)
resource_list.txt - References used in analysis (Plain text file)