Datasets:

paulopontesm
/

titanic

language:
  - en
license: other
license_name: titanic-competition-license
license_link: https://www.kaggle.com/competitions/titanic/data
license_details: >-
  This dataset is provided for use under the Titanic Machine Learning
  competition rules.
tags:
  - tabular
  - classification
  - survival-analysis
  - competition
annotations_creators:
  - found
language_creators:
  - found
language_details: en-US
pretty_name: 'Copy of the original Kaggle Titanic dataset '
size_categories:
  - 1K<n<10K
source_datasets:
  - found
task_categories:
  - tabular-classification
task_ids:
  - tabular-multi-class-classification
paperswithcode_id: titanic-survival
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.csv
      - split: test
        path: test.csv
      - split: gender_submission
        path: gender_submission.csv
dataset_info:
  features:
    - name: PassengerId
      dtype: int32
    - name: Survived
      dtype: int32
    - name: Pclass
      dtype: int32
    - name: Name
      dtype: string
    - name: Sex
      dtype: string
    - name: Age
      dtype: float32
    - name: SibSp
      dtype: int32
    - name: Parch
      dtype: int32
    - name: Ticket
      dtype: string
    - name: Fare
      dtype: float32
    - name: Cabin
      dtype: string
    - name: Embarked
      dtype: string
  config_name: default
  splits:
    - name: train
      num_bytes: 61194
      num_examples: 891
    - name: test
      num_bytes: 28629
      num_examples: 418
  download_size: 93080
  dataset_size: 89823
extra_gated_fields:
  Competition Agreement: checkbox
extra_gated_prompt: >-
  By accessing this dataset, you agree to abide by the competition rules and not
  use external datasets for training.
train-eval-index:
  - config: default
    task: tabular-classification
    task_id: tabular-multi-class-classification
    splits:
      train_split: train
      eval_split: test
    col_mapping:
      features:
        - Pclass
        - Sex
        - Age
        - SibSp
        - Parch
        - Fare
        - Embarked
      label: Survived
    metrics:
      - type: accuracy
        name: Accuracy
dataset_details:
  original_url: https://www.kaggle.com/competitions/titanic/data
  description: >
    This dataset is a **copy of the original Kaggle Titanic dataset** uploaded
    for exploring the **Hugging Face Datasets feature**.

    The Titanic dataset is a classic dataset used in machine learning and
    statistics.

    It consists of passenger information and survival status from the Titanic
    disaster.

    The dataset is provided as part of the Kaggle Titanic competition.

Dataset Card for Titanic Survival Prediction

Dataset Details

Dataset Description

This dataset is a copy of the original Kaggle Titanic dataset made to explore the Hugging Face Datasets feature.

The Titanic Survival Prediction dataset is widely used in machine learning and statistics. It originates from the Titanic: Machine Learning from Disaster competition on Kaggle. The dataset consists of passenger details from the RMS Titanic disaster, including demographic and ticket-related attributes, with the goal of predicting whether a passenger survived.

Curated by: Kaggle
Funded by: Kaggle
Shared by: Kaggle
Language(s) (NLP, if applicable): English
License: Subject to Competition Rules

Dataset Sources

Repository: Kaggle Titanic Competition
Paper [optional]: None
Demo [optional]: Not applicable

Uses

Direct Use

The dataset is primarily used for:

Supervised learning: Predicting survival outcomes based on passenger characteristics.
Feature engineering: Extracting new insights from existing features.
Data preprocessing techniques: Handling missing values, encoding categorical variables, and normalizing data.
Benchmarking machine learning models: Logistic regression, decision trees, random forests, neural networks, etc.

Out-of-Scope Use

This dataset is not meant for:

Real-world survival predictions: It is based on a historical dataset and should not be used for real-world survival predictions.
Sensitive or personally identifiable information analysis: The dataset does not contain modern personal data but should still be used responsibly.

Dataset Structure

The dataset consists of three CSV files:

train.csv (891 entries) – Includes the "Survived" column as labels for training.
test.csv (418 entries) – Used for evaluation, with missing "Survived" labels.
gender_submission.csv – A sample submission file assuming all female passengers survived.

Data Dictionary

Column	Description
PassengerId	Unique ID for each passenger
Survived	Survival status (0 = No, 1 = Yes)
Pclass	Ticket class (1st, 2nd, 3rd)
Name	Passenger name
Sex	Gender (male/female)
Age	Passenger age
SibSp	Number of siblings/spouses aboard
Parch	Number of parents/children aboard
Ticket	Ticket number
Fare	Ticket fare
Cabin	Cabin number (if known)
Embarked	Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton)

Dataset Creation

Curation Rationale

The dataset was created to help users develop predictive models for classification tasks and serves as an entry-level machine learning dataset.

Source Data

Data Collection and Processing

The dataset originates from historical records of the RMS Titanic disaster and has been structured for machine learning purposes. Some entries contain missing values, particularly in Age and Cabin, requiring imputation or removal.

Who are the source data producers?

The dataset is derived from Titanic passenger records.

Annotations

Annotation process

The dataset is not annotated beyond the Survived label.

Who are the annotators?

The survival labels come from historical records.

Personal and Sensitive Information

The dataset does not contain sensitive or personally identifiable information.

Bias, Risks, and Limitations

The dataset represents historical biases in survival rates:

Women and children had a higher chance of survival due to evacuation priorities.
First-class passengers had a higher survival rate compared to lower-class passengers.
Some data is missing or estimated, particularly age and cabin numbers.

Recommendations

Use fairness metrics when training models to assess potential biases.
Avoid real-world applications for decision-making, as this is a historical dataset.

Citation

Since this dataset originates from Kaggle, it does not have an official citation. However, you can reference it as follows:

APA: Kaggle. (n.d.). Titanic - Machine Learning from Disaster. Retrieved from https://www.kaggle.com/competitions/titanic/data

BibTeX:

@misc{kaggle_titanic,
  title = {Titanic - Machine Learning from Disaster},
  author = {Kaggle},
  year = {n.d.},
  url = {https://www.kaggle.com/competitions/titanic/data}
}