README.md

---

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](https://www.kaggle.com/competitions/titanic/data). 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](https://www.kaggle.com/competitions/titanic/data)
- **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:
1. **train.csv** (891 entries) – Includes the "Survived" column as labels for training.
2. **test.csv** (418 entries) – Used for evaluation, with missing "Survived" labels.
3. **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](https://www.kaggle.com/competitions/titanic/data)

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