Titanic survivor classification challenge

Titanic classification challenge on Kaggle. Given a dataset of a subset of the Titanic's passengers predict whether they will survive or not.

Credits

• Claudia Chianella (@clauchian)
• Yannick Giovanakis (@yangvnks)
• Flavio Primo (@flaprimo)
• Francesco Zinnari (@FrancescoZinnari)

Method

Below are provided the steps that were followed for this project. Each step and classifiers have their own document.

1. Data visualization: data analysis to understand missing values, data relations and usefulness of features
2. Preprocessing: with the knowledge acquired with the preceding step, apply preprocessing of data including dealing with missing values, drop unuseful features and build new features
3. Classifier: build classifiers based on the preprocessed data using a variety of techniques

Classification techniques

Classification techniques together with the relative scores.

Classifier	Test set score	CV score	Kaggle score
KNN	-	-	-
Logistic Regression	-	0.82	0.78947
Neural Networks	-	-	-
Random Forest	0.82	0.84	0.79425
Support Vector Machines	0.85	0.84	0.80861
Perceptron	0.78	-	0.62679
Naive Bayes	0.78	0.80	0.76076

Folder structures

• \ contains all of the jupyter's notebooks including classifiers, preprocessing and data visualization
• \Data contains the project dataset given in the Kaggle challenge
• \Data\outputs contains the outputs given by the classifiers that were submitted to Kaggle

Installation instructions

1. Install Python and clone this repository
2. Install required Python modules with pip install -r requirements.txt

to run the jupyter's notebooks just go with jupyter notebook