titanic_testing

datasets

The main focus is on preprocessing and data visualization of machine learning models performances.
Some functions allow to fill in gaps in time series using linear interpolation on panel data, some functions
permit to draw lift effect and lift curve in order to benchmark machine learning models or you can even find
the optimal number of clusters in agglomerative clustering algorithm.

Simon Corde

helda

Preprocess Data and Get Better Insights from Machine Learning
Models

titanic_testing function

Data frame of 418 observations and 11 features:<dl class="dl-horizontal">
<dt>PassengerId</dt><dd>id of the passenger</dd>
<dt>Pclass</dt><dd>passenger class on the boat</dd>
<dt>Name</dt><dd>name of the passenger</dd>
<dt>Sex</dt><dd>male / female</dd>
<dt>Age</dt><dd>age of the passenger</dd>
<dt>SibSp</dt><dd>number of siblings/spouses aboard</dd>
<dt>Parch</dt><dd>number of parents/children aboard</dd>
<dt>Ticket</dt><dd>ticket no</dd>
<dt>Fare</dt><dd>price of the ticket</dd>
<dt>Cabin</dt><dd>location of the cabin on the boat</dd>
<dt>Embarked</dt><dd>harbor city of boarding</dd>
</dl>

Format

Titanic testing data set — titanic_testing

Data frame of 418 observations and 11 features:<dl class='dl-horizontal'>
<dt>PassengerId</dt><dd>id of the passenger</dd>
<dt>Pclass</dt><dd>passenger class on the boat</dd>
<dt>Name</dt><dd>name of the passenger</dd>
<dt>Sex</dt><dd>male / female</dd>
<dt>Age</dt><dd>age of the passenger</dd>
<dt>SibSp</dt><dd>number of siblings/spouses aboard</dd>
<dt>Parch</dt><dd>number of parents/children aboard</dd>
<dt>Ticket</dt><dd>ticket no</dd>
<dt>Fare</dt><dd>price of the ticket</dd>
<dt>Cabin</dt><dd>location of the cabin on the boat</dd>
<dt>Embarked</dt><dd>harbor city of boarding</dd>
</dl>

titanic_testing: Titanic testing data set

Description

Usage

Arguments

Format

Examples