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.

• Data visualization: data analysis to understand missing values, data relations and usefulness of features
• 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
• 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

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

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