The test set from "Titanic - Machine Learning from Disaster" doesn't include labels.

This is an augmented version of the test set with the correct labels, retrieved from the original Titanic dataset at: https://www.openml.org/search?type=data&sort=runs&id=40945&status=active

The accuracy of the labels was validated by getting a 1.0 score in the competition with them.

This dataset is provided for educational purposes, and is not intended to help people cheat in the competition. If the only reason you want to download this is so you can get a shiny 1.0 on the leaderboards, don't do it.

The Titanic dataset on Kaggle is a well-known dataset used for machine learning and data science projects, especially for binary classification tasks. It includes data on the passengers of the Titanic, which sank on its maiden voyage in 1912. This dataset is often used to predict the likelihood of a passenger's survival based on various features. Here is a detailed description of the dataset:

Overview The Titanic dataset includes information about the passengers on the Titanic, such as their demographic information, class, fare, and whether they survived the disaster. The goal is to predict the survival of the passengers.

Files The dataset typically includes three files:

train.csv: The training set, which includes the features and the target variable (Survived). test.csv: The test set, which includes the features but not the target variable. You use this file to make predictions that can be submitted to Kaggle. gender_submission.csv: An example of a submission file in the correct format. Features The dataset contains the following columns:

PassengerId: Unique ID for each passenger. Survived: Target variable (0 = No, 1 = Yes) indicating if the passenger survived. Pclass: Ticket class (1 = 1st, 2 = 2nd, 3 = 3rd). Name: Name of the passenger. Sex: Gender of the passenger (male or female). Age: Age of the passenger in years. Fractional values indicate age in months for infants. SibSp: Number of siblings or spouses aboard the Titanic. Parch: Number of parents or children aboard the Titanic. Ticket: Ticket number. Fare: Passenger fare. Cabin: Cabin number. Embarked: Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton).

This dataset is a modified version of the original train.csv file provided for the Kaggle Titanic Competition. Missing passenger ages have been imputed using randomly generated values within a reasonable range. While these values may not reflect the actual ages, they facilitate a more structured classification of passengers into categories such as children and adults.

The imputation process was based on the following assumptions:

1. Passengers with 0 Parch (no parents or children aboard) were classified as adults, with their ages randomly assigned between 21 and 80, reflecting the Age of Majority at the time (21 years).
2. Passengers with 4 or more siblings/spouses (SibSp ≥ 4) were likely children (under 21).
3. Passengers with "Miss." in their name were assumed to be children. While this may not always be accurate, it provided a simple classification method.
4. Passengers with "Mr." in their name were assumed to be adults.
5. Passengers with "Master." in their name were assumed to be children.
6. Passengers with "Mrs." in their name were assumed to be married and, therefore, likely adults. The imputation process was carried out in the following order: 1 → 2 → (3, 4, 5, 6 applied together).

It is important to note that while honorifics such as Mr., Miss., Mrs., and Master. were historically used with some flexibility, this dataset (Version 1) assumes a strict age classification based on a legal age of adulthood set at 21.

Version 2 Modifications:

The title "Master." was assumed to refer to males aged 0 to 16. The title "Mr." was assumed to refer to males aged 17 and above. These modifications aim to provide a structured approach to handling missing age data while maintaining reasonable historical assumptions.

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Overview

The data has been split into two groups:

• training set (train.csv)
• test set (test.csv)

The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features.

The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic.

We also include gender_submission.csv, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like.

Data Dictionary:

| Variable | Definition | Key | | --- | --- | | survival | Survival | 0 = No, 1 = Yes | | pclass | Ticket class | 1 = 1st, 2 = 2nd, 3 = 3rd | | sex | Sex | | | 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

pclass: A proxy for socio-economic status (SES) 1st = Upper 2nd = Middle 3rd = Lower

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.

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