The dataset containing information about passengers aboard the Titanic is one of the most famous datasets used in data science and machine learning. It was created to analyze and understand the factors that influenced survival rates among passengers during the tragic sinking of the RMS Titanic on April 15, 1912.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19517213%2Fd4016c159f1ad17cb30d8905192fe9d7%2Ftitanic-ship_1027017-11.avif?generation=1711562371875068&alt=media" alt="">

Data Description :-

The dataset is often used for predictive modeling and statistical analysis to determine which factors (such as socio-economic status, age, gender, etc.) were associated with a higher likelihood of survival. It contains 1309 rows and 14 columns.

Columns : -

• Pclass: Ticket class indicating the socio-economic status of the passenger. It is categorized into three classes: 1 = Upper, 2 = Middle, 3 = Lower.

• Survived: A binary indicator that shows whether the passenger survived (1) or not (0) during the Titanic disaster. This is the target variable for analysis.

• Name: The full name of the passenger, including title (e.g., Mr., Mrs., etc.).

• Sex: The gender of the passenger, denoted as either male or female.

• Age: The age of the passenger in years.

• SibSp: The number of siblings or spouses aboard the Titanic for the respective passenger.

• Parch: The number of parents or children aboard the Titanic for the respective passenger.

• Ticket: The ticket number assigned to the passenger.

• Fare: The fare paid by the passenger for the ticket.

• Cabin: The cabin number assigned to the passenger, if available.

• Embarked: The port of embarkation for the passenger. It can take one of three values: C = Cherbourg, Q = Queenstown, S = Southampton.

• Boat: If the passenger survived, this column contains the identifier of the lifeboat they were rescued in.

• Body: If the passenger did not survive, this column contains the identification number of their recovered body, if applicable.

• Home.dest: The destination or place of residence of the passenger.

These descriptions provide a detailed understanding of each column in the Titanic dataset subset, offering insights into the demographic, travel, and survival-related information recorded for each passenger.

Detail Description:

The Titanic dataset offers a comprehensive glimpse into the passengers aboard the ill-fated RMS Titanic, which famously sank on its maiden voyage in April 1912 after colliding with an iceberg. This dataset contains a wealth of information about individual passengers, including demographics, ticket class, cabin information, family relationships, fare details, and most notably, survival outcomes.

Key attributes within the dataset include:

1. Passenger Class (Pclass): This categorical variable indicates the ticket class of each passenger, ranging from 1st class (wealthiest) to 3rd class (lower socioeconomic status).

2. Name: The names of passengers, providing insight into their identities.

3. Sex: Gender of passengers, categorized as male or female.

4. Age: Age of passengers, providing information about the demographic composition of the Titanic's passengers.

5. SibSp: Number of siblings/spouses aboard the Titanic, offering insight into family relationships.

6. Parch: Number of parents/children aboard the Titanic, indicating family size and composition.

7. Ticket: Ticket number, providing additional information about passenger accommodations and fare details.

8. Fare: Fare paid by each passenger, which can be indicative of their ticket class and economic status.

9. Cabin: Cabin number or location, offering insights into passenger accommodations.

10. Embarked: Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton), providing information about passengers' embarkation points.

11. Survived: This binary variable indicates whether a passenger survived the disaster (1) or not (0), serving as the primary outcome variable for analyses.

Researchers and data analysts frequently utilize the Titanic dataset for various purposes, including:

• Exploratory data analysis to understand the demographic composition of passengers and their survival outcomes.
• Predictive modeling to develop algorithms that predict the likelihood of survival based on passenger characteristics.
• Feature engineering to derive new variables that may enhance predictive accuracy.
• Hypothesis testing to investigate factors associated with survival rates, such as passenger class, gender, age, and family size.

Overall, the Titanic dataset serves as a valuable resource for understanding historical events, exploring data analysis techniques, and teaching machine learning concepts. Its accessibility and rich contextual information make it a popular choice for both educational and research purposes within the data science community.

This dataset provides information on passengers aboard the RMS Titanic, including features that can be used for predicting survival. It contains various attributes related to passengers such as age, sex, ticket fare, and passenger class, which are crucial for understanding patterns and building predictive models.

Content:

PassengerID: Unique identifier for each passenger. Pclass: Passenger class (1st, 2nd, 3rd). Name: Full name of the passenger. Sex: Gender of the passenger. Age: Age of the passenger. SibSp: Number of siblings/spouses aboard. Parch: Number of parents/children aboard. Ticket: Ticket number. Fare: Ticket fare. Cabin: Cabin number. Embarked: Port of embarkation (C = Cherbourg; Q = Queenstown; S = Southampton). Survived: Survival status (0 = No; 1 = Yes).

Usage:

This dataset is ideal for practice in classification tasks, particularly for predicting binary outcomes such as survival status. It is commonly used for various machine learning challenges, including exploratory data analysis and feature engineering.

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… See the full description on the dataset page: https://huggingface.co/datasets/hdhnhdhnhdhn/titanic.

🛳️ Titanic Dataset (JSON Format) 📌 Overview

This is the classic Titanic: Machine Learning from Disaster dataset, converted into JSON format for easier use in APIs, data pipelines, and Python projects. It contains the same passenger details as the original CSV version, but stored as JSON for convenience.

📂 Dataset Contents

File: titanic.json

Columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Use Cases: Exploratory Data Analysis (EDA), feature engineering, machine learning model training, web app backends, JSON parsing practice.

🛠️ How to Use 🔹 1. Load with kagglehub import kagglehub

Download the latest version of the dataset

path = kagglehub.dataset_download("engrbasit62/titanic-json-format") print("Path to dataset files:", path)

🔹 2. Load into Pandas import pandas as pd

Read the JSON file into a DataFrame

df = pd.read_json(f"{path}/titanic.json")

print(df.head())

💡 Notes

Preview truncation: Kaggle may show only part of the JSON in the preview panel because of its size. ✅ Don’t worry — the full dataset is available when loaded via code.

Benefits of JSON format: Ideal for web apps, APIs, or projects that work with structured data. Easily convertible back to CSV if needed.

skillshikshyalearning/titanic-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11299784%2F6530245ff6b6d097af8cb56c86b79943%2Fpxfuel.jpg?generation=1682007437079315&alt=media" alt="">The Titanic dataset is a widely used dataset that contains information on the passengers who were aboard the Titanic when it sank on its maiden voyage in 1912. The dataset includes features such as age, sex, passenger class, and fare paid, as well as whether or not the passenger survived the sinking. The dataset is often used for machine learning and data analysis tasks, such as predicting survival based on passenger characteristics or exploring patterns in the data. The Titanic dataset is a classic example of data analysis and is a great starting point for those new to data science.

The Titanic dataset is available in CSV format and contains two files, one for training and one for testing. The training file is used to build the machine learning model, while the testing file is used to test the performance of the model.

Column Description

PassengerId: unique identifier for each passenger Survived: whether the passenger survived (1) or not (0) Pclass: passenger class (1 = 1st class, 2 = 2nd class, 3 = 3rd class) Name: name of the passenger Sex: gender of the passenger Age: age of the passenger (in years) 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)

MIT License

Copyright (c) [2023] [Md Kazi Sajiduddin]

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Predict passenger survival on the Titanic based on age, sex, passenger class, ticket fare, and family size.

Muttanna/Titanic dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset is very similar to what was offered by the Titanic competition. Unfortunately, many wrong data spoiled the original Titanic competition dataset. I discussed this topic in more detail here.

It is provided by an R package stablelearner. For ease of use, I converted it from RDA to CSV format. I tried to make it error-free. Moreover, there are far fewer missing data.

It lacks the Cabin feature, but it has Country of nationality. Moreover, it has far fewer missing values.

This is a classic dataset used in many data mining tutorials and demos -- perfect for getting started with exploratory analysis and building binary classification models to predict survival.

Data covers passengers only, not crew.

1.Features 2.survival - Survival (0 = No; 1 = Yes) 3.class - Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd) 4.name - Name 5.sex - Sex 6.age - Age 7.sibsp - Number of Siblings/Spouses Aboard 8.parch - Number of Parents/Children Aboard 9.ticket - Ticket Number 10.fare - Passenger Fare 11.cabin - Cabin 12.embarked - Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton) 13.boat - Lifeboat (if survived) 14.body - Body number (if did not survive and body was recovered)

Dataset

This dataset was created by Nesiha Mohammed

Released under Apache 2.0

Contents

This dataset contains passenger information from the Titanic disaster. It is commonly used for binary classification tasks (survival prediction).

This dataset provides passenger details from the historic Titanic voyage, typically utilized as a test subset for the classic Titanic machine learning challenge. It serves as a foundational benchmark for practicing binary classification, data cleaning, feature engineering, and handling missing values.

Context

This Data was originally taken from Titanic: Machine Learning from Disaster .But its better refined and cleaned & some features have been self engineered typically for logistic regression . If you use this data for other models and benefit from it , I would be happy to receive your comments and improvements.

Content

There are two files namely:- train_data.csv :- Typically a data set of 792x16 . The survived column is your target variable (The output you want to predict).The parch & sibsb columns from the original data set has been replaced with a single column called Family size.

All Categorical data like Embarked , pclass have been re-encoded using the one hot encoding method .

Additionally, 4 more columns have been added , re-engineered from the Name column to Title_1 to Title_4 signifying males & females depending on whether they were married or not .(Mr , Mrs ,Master,Miss). An additional analysis to see if Married or in other words people with social responsibilities had more survival instincts/or not & is the trend similar for both genders.

All missing values have been filled with a median of the column values . All real valued data columns have been normalized.

test_data.csv :- A data of 100x16 , for testing your model , The arrangement of test_data exactly matches the train_data

I am open to feedbacks & suggesstions

Titanic Dataset ( Large-Scale Passenger Survival )

Author: MobeenFatima @mobeenfatimah

Overview

Titanic Dataset is a modernized, large-scale synthetic benchmark dataset inspired by the classic historical Titanic problem. While the original dataset consists of only 891 rows, Titanic dataset scales the problem to 100,000 passenger records and 31 features, making it an ideal testing ground for modern tabular machine learning algorithms, scalable feature engineering, and explainable AI (XAI).

The target variable (survived) is modeled with continuous non-linear interactions, realistic stochastic noise, and domain-specific edge cases to prevent models from achieving trivial ~100% accuracy while maintaining authentic risk patterns.

Dataset Specifications

• Records: 100,000 rows
• Features: 30 predictor variables + 1 binary target (survived)
• Target Distribution: Imbalanced (~28.5% Survived, ~71.5% Non-Survived)
• File Format: Single CSV (titanic_2_0_passenger_survival_dataset.csv)

Feature Breakdown

1. Passenger Demographics

• passenger_id: Unique identifier (T2-000001 through T2-100000).
• full_name: Synthesized passenger first and last name.
• age: Continuous passenger age (1.0 to 85.0 years).
• gender: Biological/identified gender (Male, Female, Non-Binary).
• nationality: Primary citizenship (10 representative countries).
• marital_status: Marital standing (Single, Married, Divorced, Widowed).
• occupation: Primary employment category.

2. Travel & Booking

• class: Ticket class (1 = First, 2 = Second, 3 = Third).
• ticket_number: 6-digit ticket reference.
• ticket_price: Total fare paid in USD.
• cabin_deck: Assigned deck level (A through G, with realistic missingness).
• embarkation_port: Departure location.
• travel_purpose: Primary reason for voyage (Leisure, Business, Relocation, etc.).
• journey_duration_days: Planned travel time.

3. Family & Social Structures

• siblings_spouses: Number of siblings/spouses aboard.
• parents_children: Number of parents/children aboard.
• family_size: Calculated group size (siblings_spouses + parents_children + 1).
• is_alone: Binary indicator (1 if traveling alone, 0 otherwise).
• family_survival_rate: Correlated baseline group survival metric.

4. Emergency, Health & Behavioral

• boarding_position: Sequential boarding queue number (1–1000).
• lifeboat_access: Calculated proximity/accessibility score to lifeboats (0.00 to 1.00).
• medical_condition: Pre-existing conditions impacting mobility.
• mobility_score: Functional score (1–100) combining age and health penalties.
• emergency_response_time: Evacuation response latency in minutes.
• evacuation_priority: Triaged priority band (High, Medium, Low).

5. Derived Features & Target

• fare_per_person: Individual share of ticket price.
• wealth_index: Composite economic metric derived from class and fare.
• age_group: Categorical age binning (Child, Teen, Adult, Middle_Aged, Senior).
• family_risk_score: Aggregated vulnerability score based on group dynamics.
• survival_probability: Underlying non-linear probability calculation (for evaluation reference).
• survived: Target Column (1 = Survived, 0 = Did Not Survive).

Ideal Use Cases

1. Binary Classification: Test Gradient Boosting frameworks (XGBoost, LightGBM, CatBoost) and Tabular Neural Networks.
2. Missing Value Imputation: Practice advanced imputation on cabin_deck (~18% missing) and emergency_response_time (~5% missing).
3. Feature Engineering: Extract non-linear signals from interacting family, wealth, and mobility features.
4. Explainable AI (SHAP / LIME): Interpret complex risk interactions across demographic and behavioral factors.

This is a copy of the Titanic: Machine Learning from Disaster Dataset used for the Pandas Handbook Project

The Titanic dataset is a popular dataset in the field of data science and machine learning. It contains information about the passengers aboard the RMS Titanic, which sank on its maiden voyage in 1912 after hitting an iceberg. The dataset is often used for predictive modeling and classification tasks.

Here are the key features or columns in the Titanic dataset:

- PassengerId: A unique identifier assigned to each passenger. - Survived: A binary variable indicating whether the passenger survived (1) or did not survive (0). - Pclass (Passenger Class): The ticket class of the passenger, which can be 1st (1), 2nd (2), or 3rd (3) class. - Name: The name of the passenger. - Sex: The gender of the passenger (male or female). - Age: The age of the passenger in years. It may contain missing values. - SibSp: The number of siblings or spouses the passenger had aboard the Titanic. - Parch: The number of parents or children the passenger had aboard the Titanic. - Ticket: The ticket number. - Fare: The amount of money the passenger paid for the ticket. - Cabin: The cabin number where the passenger stayed. It may contain missing values. - Embarked: The port at which the passenger boarded the Titanic (C for Cherbourg, Q for Queenstown, S for Southampton).

Dataset

This dataset was created by Waqas Ali Khan

Released under Apache 2.0

Contents

Titanic - Machine Learning from Disaster

Overview

This dataset contains passenger details from the infamous Titanic disaster of 1912. It is widely used for machine learning classification problems, particularly in predictive modeling. The goal is to use passenger features to determine whether a person survived or not.

Dataset Details

Target Variable: Survived (0 = No, 1 = Yes) Features: Pclass: Passenger class (1st, 2nd, 3rd) Name: Name of the passenger Sex: Gender Age: Age in years SibSp: Number of siblings/spouses aboard Parch: Number of parents/children aboard Ticket: Ticket number Fare: Fare paid for the ticket Cabin: Cabin number (if available) Embarked: Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton)

Use Cases

Binary classification (Survived/Not Survived) Exploratory Data Analysis (EDA) Feature Engineering Model training and evaluation using machine learning algorithms

Applications

Training beginners in machine learning Feature selection and model optimization Understanding imbalanced datasets Acknowledgments

This dataset originates from the famous Titanic Machine Learning Challenge on Kaggle and is an excellent starting point for those new to data science and AI.