What is the Titanic Dataset dataset?

Titanic passenger dataset with 887 records featuring survival outcomes, demographics, and fare data. 38.6% survival rate reveals stark gender disparities (74% female vs 19% male) and class-based inequalities in the 1912 maritime disaster.

How can I download the Titanic Dataset dataset?

You can download the Titanic Dataset dataset in CSV or Parquet format directly from this page. Each table has its own download buttons.

What format is the Titanic Dataset dataset available in?

This dataset is available in CSV and Parquet formats. CSV is great for spreadsheet applications, while Parquet is optimized for data analysis tools like Pandas and DuckDB.

What is the license for the Titanic Dataset dataset?

This dataset is available under the Public Domain license. See the full license at https://creativecommons.org/publicdomain/mark/1.0/

How many tables are in the Titanic Dataset dataset?

The Titanic Dataset dataset contains 1 table: titanic.

Titanic Dataset

Last updated 2 months ago•December 27, 2025

Source:Encyclopedia Titanica / British Board of Trade

License:Public Domain

Version:1.0

Time:April 1912

Location:North Atlantic Ocean (Southampton to New York route)

Created by Dataset Agent

Overview

The Titanic dataset is the most widely-used introductory dataset for binary classification in machine learning. It contains passenger information from the RMS Titanic, which sank on April 15, 1912, after colliding with an iceberg during its maiden voyage from Southampton to New York City. Of the estimated 2,224 passengers and crew aboard, approximately 1,502 perished—making it one of the deadliest peacetime maritime disasters in history.

This dataset answers the question: What sorts of people were more likely to survive? The data reveals that survival was not random—factors like gender, passenger class, and age significantly influenced who lived and who died.

The dataset contains 887 passenger records with an overall survival rate of 38.56% (342 survivors out of 887 passengers).

View Source

SQL

SELECT COUNT( CASE WHEN Survived = 1 THEN 1 END ) AS survivors, COUNT(*) AS total, ROUND( COUNT( CASE WHEN Survived = 1 THEN 1 END ) * 100.0 / COUNT(*), 2 ) AS survival_rate FROM titanic.csv

Data

Survivors	Total	Survival Rate
342	887	38.56
1 row

Dataset at a Glance

Passenger Class Distribution

The Pclass column represents ticket class, which served as a proxy for socioeconomic status in 1912:

Passenger Class Breakdown

Pclass Value	Class Name	Count	Percentage	Description
1	First Class	216	24.4%	Upper class, luxury cabins on upper decks
2	Second Class	184	20.7%	Middle class, comfortable accommodations
3	Third Class	487	54.9%	Lower class, basic quarters on lower decks
3 rows

View Source

SQL

SELECT Pclass, COUNT(*) AS count, ROUND( COUNT(*) * 100.0 / ( SELECT COUNT(*) FROM titanic.csv ), 1 ) AS percentage FROM titanic.csv GROUP BY Pclass ORDER BY Pclass

Data

Pclass Value	Class Name	Count	Percentage	Description
1	First Class	216	24.4%	Upper class, luxury cabins on upper decks
2	Second Class	184	20.7%	Middle class, comfortable accommodations
3	Third Class	487	54.9%	Lower class, basic quarters on lower decks
3 rows

Note: The dataset shows significant class imbalance—over half the passengers (54.9%) traveled in third class. This reflects the ship's design to maximize steerage passenger capacity for immigrant transport to America.

Key Survival Patterns

Gender: The Strongest Predictor

74.2% of women survived compared to only 18.9% of men—a 55 percentage point difference that reflects the "women and children first" evacuation protocol.

View Source

SQL

SELECT Sex, COUNT(*) AS total, COUNT( CASE WHEN Survived = 1 THEN 1 END ) AS survivors, ROUND( COUNT( CASE WHEN Survived = 1 THEN 1 END ) * 100.0 / COUNT(*), 1 ) AS survival_rate FROM titanic.csv GROUP BY Sex

Data

Sex	Total	Survivors	Survival Rate
male	573	109	18.9
female	314	233	74.2
2 rows

View Source

SQL

Data

Gender	Total Passengers	Survivors	Survival Rate (%)
Male	573	109	18.9
Female	314	233	74.2
2 rows

Class: Wealth Determined Access

First-class passengers had a 62.96% survival rate versus only 24.44% for third-class—a gap of nearly 40 percentage points revealing how cabin location affected lifeboat access.

View Source

SQL

SELECT Pclass, COUNT(*) AS total, COUNT( CASE WHEN Survived = 1 THEN 1 END ) AS survivors, ROUND( COUNT( CASE WHEN Survived = 1 THEN 1 END ) * 100.0 / COUNT(*), 2 ) AS survival_rate FROM titanic.csv GROUP BY Pclass ORDER BY Pclass

Data

Pclass	Total	Survivors	Survival Rate
1	216	136	62.96
2	184	87	47.28
3	487	119	24.44
3 rows

View Source

SQL

Data

Class	Total Passengers	Survivors	Survival Rate (%)
1st Class	216	136	62.96
2nd Class	184	87	47.28
3rd Class	487	119	24.44
3 rows

The Compounding Effect: Gender × Class

First-class women had an extraordinary 96.81% survival rate, while third-class men survived at only 13.7%—a 7:1 ratio demonstrating how gender and class compounded to determine fate.

View Source

SQL

SELECT Sex, Pclass, COUNT(*) AS total, COUNT( CASE WHEN Survived = 1 THEN 1 END ) AS survivors, ROUND( COUNT( CASE WHEN Survived = 1 THEN 1 END ) * 100.0 / COUNT(*), 2 ) AS survival_rate FROM titanic.csv GROUP BY Sex, Pclass ORDER BY survival_rate DESC

Data

Sex	Pclass	Total	Survivors	Survival Rate
female	1	94	91	96.81
female	2	76	70	92.11
female	3	144	72	50
male	1	122	45	36.89
male	2	108	17	15.74
male	3	343	47	13.7
6 rows

View Source

SQL

SELECT Sex, Pclass, ROUND( COUNT( CASE WHEN Survived = 1 THEN 1 END ) * 100.0 / COUNT(*), 2 ) AS survival_rate FROM titanic.csv GROUP BY Sex, Pclass ORDER BY survival_rate DESC

Data

Category	Survival Rate (%)
1st Class Women	96.81
2nd Class Women	92.11
3rd Class Women	50
1st Class Men	36.89
2nd Class Men	15.74
3rd Class Men	13.7
6 rows

Age: Children Had Better Odds

Passengers ranged from 0.42 to 80 years old with a mean age of 29.5 years. Children under 18 had a 50% survival rate—the highest among all age groups.

View Source

SQL

SELECT ROUND(AVG(Age), 1) AS avg_age, MIN(Age) AS min_age, MAX(Age) AS max_age FROM titanic.csv WHERE Age IS NOT NULL

Data

Avg Age	Min Age	Max Age
29.5	0.42	80
1 row

View Source

SQL

SELECT CASE WHEN Age < 18 THEN 'Child (0-17)' WHEN Age < 35 THEN 'Young Adult (18-34)' WHEN Age < 55 THEN 'Middle Age (35-54)' ELSE 'Senior (55+)' END AS age_group, COUNT(*) AS total, COUNT( CASE WHEN Survived = 1 THEN 1 END ) AS survivors, ROUND( COUNT( CASE WHEN Survived = 1 THEN 1 END ) * 100.0 / COUNT(*), 2 ) AS survival_rate FROM titanic.csv WHERE Age IS NOT NULL GROUP BY age_group

Data

Age Group	Total	Survivors	Survival Rate (%)
Child (0-17)	130	65	50
Young Adult (18-34)	474	168	35.44
Middle Age (35-54)	234	96	41.03
Senior (55+)	49	13	26.53
4 rows

Fare: Economic Status Mattered

Ticket fares ranged from £0 to £512.33 (average £32.31). Passengers paying premium fares (£100+) had a 73.58% survival rate versus just 6.67% for those with free tickets.

View Source

SQL

SELECT ROUND(AVG(Fare), 2) AS avg_fare, MIN(Fare) AS min_fare, ROUND(MAX(Fare), 2) AS max_fare FROM titanic.csv

Data

Avg Fare	Min Fare	Max Fare
32.31	0	512.33
1 row

View Source

SQL

SELECT CASE WHEN Fare = 0 THEN 'Free (£0)' WHEN Fare < 10 THEN 'Low (<£10)' WHEN Fare < 30 THEN 'Medium (£10-30)' WHEN Fare < 100 THEN 'High (£30-100)' ELSE 'Premium (£100+)' END AS fare_category, COUNT(*) AS total, COUNT( CASE WHEN Survived = 1 THEN 1 END ) AS survivors, ROUND( COUNT( CASE WHEN Survived = 1 THEN 1 END ) * 100.0 / COUNT(*), 2 ) AS survival_rate FROM titanic.csv GROUP BY fare_category ORDER BY MIN(Fare)

Data

Fare Category	Total	Survivors	Survival Rate (%)
Free (£0)	15	1	6.67
Low (<£10)	318	66	20.75
Medium (£10-30)	314	134	42.68
High (£30-100)	187	102	54.55
Premium (£100+)	53	39	73.58
5 rows

Data Quality Notes

Understanding data quality is essential for accurate analysis. This dataset has varying completeness across columns:

Age Missing Values: Approximately 20% of Age values are missing. Common strategies include: (1) median imputation by passenger class, (2) median imputation by title extracted from Name, or (3) creating an 'Age_Missing' binary feature to capture the missingness pattern itself.

Feature Engineering Opportunities

The raw features can be transformed to improve predictive models. Here are proven feature engineering techniques used by top Kaggle competitors:

Title Extraction Tip: The Name column contains honorific titles (Mr., Mrs., Miss., Master., Dr., Rev., etc.) that strongly correlate with survival. Extract using regex: df['Title'] = df['Name'].str.extract(' ([A-Za-z]+)\.') — "Master" indicates male children with 57.5% survival rate.

Benchmark Model Performance

When building survival prediction models, here are typical accuracy benchmarks to compare against:

Historical Context

The RMS Titanic was the largest ship afloat at the time of its maiden voyage in April 1912. Operated by the White Star Line, it was marketed as "practically unsinkable" due to its advanced safety features including 16 watertight compartments. The ship struck an iceberg at 11:40 PM on April 14, 1912, and sank in under three hours.

The disaster exposed critical failures: the ship carried only 20 lifeboats (capacity for 1,178 people) despite holding 2,224 passengers and crew. Third-class passengers faced locked gates and confusing routes to the boat deck. The tragedy led to the International Convention for the Safety of Life at Sea (SOLAS), still the primary maritime safety treaty today.

Sample Records

Example Passenger Records

Survived	Pclass	Name	Sex	Age	Sib Sp	Parch	Fare
0	3	Mr. Owen Harris Braund	male	22	1	0	£7.25
1	1	Mrs. John Bradley Cumings	female	38	1	0	£71.28
1	3	Miss. Laina Heikkinen	female	26	0	0	£7.93
1	1	Mrs. Jacques Heath Futrelle	female	35	1	0	£53.10
0	3	Mr. William Henry Allen	male	35	0	0	£8.05
5 rows

View Source

SQL

SELECT Survived, Pclass, Name, Sex, Age, "Siblings/Spouses Aboard" AS SibSp, "Parents/Children Aboard" AS Parch, Fare FROM titanic.csv LIMIT 5

Data

Survived	Pclass	Name	Sex	Age	Sib Sp	Parch	Fare
0	3	Mr. Owen Harris Braund	male	22	1	0	£7.25
1	1	Mrs. John Bradley Cumings	female	38	1	0	£71.28
1	3	Miss. Laina Heikkinen	female	26	0	0	£7.93
1	1	Mrs. Jacques Heath Futrelle	female	35	1	0	£53.10
0	3	Mr. William Henry Allen	male	35	0	0	£8.05
5 rows

Important Considerations

Dataset Scope: This dataset contains 887 of the ~1,309 passengers aboard (excluding crew). Records with incomplete information were excluded from this version. The 38.56% survival rate here is slightly higher than the historical 32% overall survival rate due to this sampling.

Column Encoding: The 'Survived' column uses binary encoding (1 = survived, 0 = did not survive). The 'Sex' column contains string values ('male', 'female') that must be encoded for most ML algorithms. SibSp counts siblings AND spouses; Parch counts parents AND children.

Dataset Variants Comparison

Multiple versions of the Titanic dataset exist across different platforms. This table helps identify which version you're working with:

Table Overview

titanic

Contains 887 rows and 8 columns. Column types: 6 numeric, 2 text.

887 rows8 columns

titanic

887

rows

columns

Data Preview

Scroll to see more

Survived	Pclass	Name	Sex	Age	Siblings/Spouses Aboard	Fare
0	3	Mr. Owen Harris Braund	male	22	1	7.25
1	1	Mrs. John Bradley (Florence Briggs Th...	female	38	1	71.28
1	3	Miss. Laina Heikkinen	female	26	0	7.93
1	1	Mrs. Jacques Heath (Lily May Peel) Fu...	female	35	1	53.1
0	3	Mr. William Henry Allen	male	35	0	8.05

Row 1

Survived0

Pclass3

NameMr. Owen Harris Braund

Sexmale

Age22

+3 more columns

Row 2

Survived1

Pclass1

NameMrs. John Bradley (Florence...

Sexfemale

Age38

+3 more columns

Row 3

Survived1

Pclass3

NameMiss. Laina Heikkinen

Sexfemale

Age26

+3 more columns

Showing 5 of 887 rows

Data Profile

887

rows

columns

100%

complete

346.5 KB

estimated size

Column Types

6 Numeric2 Text

High-Cardinality Columns

Columns with many unique values (suitable for identifiers or categorical features)

Name(887 unique values)

Data Dictionary

titanic

Column	Type	Description	Example
`Survived`	numeric	Survival status (0=No, 1=Yes)	0, 1
`Pclass`	numeric	Class category	3, 1
`Name`	string	Name or title	"Mr. Owen Harris Brau...", "Mrs. John Bradley (F..."
`Sex`	string	Gender	"male", "female"
`Age`	numeric	Age in years (22 to 38)	22, 38
`Siblings/Spouses Aboard`	numeric	Siblings/spouses aboard	1, 1
`Parents/Children Aboard`	numeric	Parents/children aboard	0, 0
`Fare`	numeric	Passenger fare in pounds	7.25, 71.2833