Titanic Machine Learning From Disaster is my first data science project and also my first submission in a Kaggle competition. The goal was to have a first real experience in machine learning, python, sklearn and try many different classifiers.
You can either view my analysis directly in the Jupyter Notebook Titanic.ipynb or install and run the full environment as described hereafter.
The code was tested with Python 3.6.
The packages I used to run the code are listed in requirements.txt (Note that some of these exact version numbers may not be available on your platform: you may have to tweak them for your own use). To install the requirements using conda, run the following at the command-line:
$ conda install --file requirements.txt
To create a stand-alone environment named kaggle-titanic with Python 3.5 and all the required package versions, run the following:
$ conda create -n kaggle-titanic python=3.6 --file requirements.txt
You can read more about using conda environments in the Managing Environments section of the conda documentation.
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.
One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.
In this contest, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.
This Kaggle Getting Started Competition provides an ideal starting place for people who may not have a lot of experience in data science and machine learning."
From the competition homepage.
Show a simple example of an analysis of the Titanic disaster in Python. This is aimed for those looking to get into the field or those who are already in the field and looking to see an example of an analysis done with Python.
- Feature ranking with recursive feature elimination and cross-validated selection of the best number of features with RFECV
- Benchmarks multiple classifiers among XGBClassifier, AdaBoostClassifier, DecisionTreeClassifier, RandomForestClassifier
- Benchmarks multiple hyper parameters with GridSearchCV and cross validation
- Displaying results
- Refining hyper parameters for the best model with GridSearchCV
- K-folds cross validation to valuate results locally
- Output the results from the Notebook to Kaggle
Competition Website: https://www.kaggle.com/c/titanic