Titanic Dataset Description Overview The data is divided into two groups: - Training set (train.csv): Used to build machine learning models. It includes the outcome (also called the "ground truth") for each passenger, allowing models to predict survival based on “features” like gender and class. Feature engineering can also be applied to create new features. - Test set (test.csv): Used to evaluate model performance on unseen data. The ground truth is not provided; the task is to predict survival for each passenger in the test set using the trained model.

Additionally, gender_submission.csv is provided as an example submission file, containing predictions based on the assumption that all and only female passengers survive.

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: Proxy for socio-economic status (SES): 1st = Upper 2nd = Middle 3rd = Lower age:
Fractional if less than 1 year.
Estimated ages are represented in the form xx.5. sibsp: Defines family relations as: Sibling: Brother, sister, stepbrother, stepsister. Spouse: Husband, wife (excluding mistresses and fiancés). parch: Defines family relations as: Parent: Mother, father. Child: Daughter, son, stepdaughter, stepson. Some children traveled only with a nanny, so parch = 0 for them.

1) Data Introduction • Based on passenger information from the Titanic, which sank in 1912, the Titanic Dataset is a representative binary classification data that includes various demographics and boarding information such as Survived, Passengers Class, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, and Embarked.

2) Data Utilization (1) Titanic Dataset has characteristics that: • It consists of a total of 891 training samples and 12 to 15 columns (numerical and categorical mix) and also includes variables such as Age, Cabin, and Embarked with some missing values, making it suitable for preprocessing and feature engineering practice. (2) Titanic Dataset can be used to: • Development of survival prediction models: Key characteristics such as passenger rating, gender, age, and fare can be used to predict survival with different machine learning classification models such as logistic regression, random forest, and SVM. • Analysis of survival influencing factors: By analyzing the correlation between variables such as gender, age, socioeconomic status, and survival rates, you can statistically and visually explore which groups have a higher survival probability.

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

Dataset describing the survival status of individual passengers on the Titanic. Missing values in the original dataset are represented using ?. Float and int missing values are replaced with -1, string missing values are replaced with 'Unknown'.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('titanic', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://raw.githubusercontent.com/Masterx-AI/Project_Titanic_Survival_Prediction_/main/titanic.jpg" alt="">

Description:

The sinking of the Titanic is one of the most infamous shipwrecks in history.

On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone on board, resulting in the death of 1502 out of 2224 passengers and crew.

While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.

In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).

Acknowledgements:

This dataset has been referred from Kaggle: https://www.kaggle.com/c/titanic/data.

Objective:

• Understand the Dataset & cleanup (if required).
• Build a strong classification model to predict whether the passenger survives or not.
• Also fine-tune the hyperparameters & compare the evaluation metrics of various classification algorithms.

nik20004/titanic dataset hosted on Hugging Face and contributed by the HF Datasets community

The Titanic dataset is a well-known dataset that provides information on the passengers who were onboard the fateful voyage of the RMS Titanic. The data includes details such as the passenger's name, age, gender, ticket class, fare paid, and information on their family members. The dataset also includes a column called "Survived" which indicates whether a passenger survived the disaster or not.

There are a total of 891 rows in the dataset, with 12 columns. Some of the key columns in the dataset include:

• PassengerId: a unique identifier for each passenger • Survived: a binary variable that indicates whether the passenger survived (1) or did not survive (0) the disaster • Pclass: the ticket class of the passenger (1 = first class, 2 = second class, 3 = third class) • Name: the name of the passenger • Sex: the gender of the passenger (male or female) • Age: the age of the passenger (some values are missing) • SibSp: the number of siblings or spouses the passenger had on board • Parch: the number of parents or children the passenger had on board • Ticket: the ticket number of the passenger • Fare: the fare paid by the passenger • Cabin: the cabin number of the passenger (some values are missing) • Embarked: the port at which the passenger embarked (C = Cherbourg, Q = Queenstown, S = Southampton)

Overall, the key challenges I encountered when working on the Titanic dataset were: how to handle missing values and imbalanced classes, encode categorical variables, reduce the dimensionality of the dataset, and identify and handle noise in the data.

Here are a few tips and resources that I found helpful when getting started in the Titanic dataset competition: 1. Get familiar with the dataset 2. Pre-process the data 3. Split the data into training and test sets 4. Try out a few different algorithms 5. Tune the hyper parameters 6. Evaluate the model

Here are a few resources that I found helpful as I started Working on the competition: • Kaggle's Titanic tutorial • scikit-learn documentation. • Pandas documentation

Analysis of ‘Titanic Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yasserh/titanic-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

https://raw.githubusercontent.com/Masterx-AI/Project_Titanic_Survival_Prediction_/main/titanic.jpg" alt="">

Description:

The sinking of the Titanic is one of the most infamous shipwrecks in history.

On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone on board, resulting in the death of 1502 out of 2224 passengers and crew.

While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.

In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).

Acknowledgements:

This dataset has been referred from Kaggle: https://www.kaggle.com/c/titanic/data.

Objective:

• Understand the Dataset & cleanup (if required).
• Build a strong classification model to predict whether the passenger survives or not.
• Also fine-tune the hyperparameters & compare the evaluation metrics of various classification algorithms.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Titanic dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ibrahimelsayed182/titanic-dataset on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Overview

This is Titanic dataset

Data Dictionary

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Titanic-Dataset (train.csv)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/hesh97/titanicdataset-traincsv on 28 January 2022.

--- No further description of dataset provided by original source ---

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Titanic Dataset Analysis’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/cities/titanic123 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

--- Original source retains full ownership of the source dataset ---

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

Titanic dataset for classification training.

TITANIC-FGS is the first domain knowledge-enhanced, instruction-following dataset specifically designed for the Remote Sensing Fine-Grained Ship Classification (RS-FGSC) task. It simulates human-like step-by-step decision-making to train vision-language models (VLMs) for interpretable and accurate ship classification.

Dataset

This dataset was created by VVignesh Kumar

Contents

Who travelled on the Titanic? When she reached the open Atlantic on 11 April 1912, the Titanic carried 2,208 people however many more travelled on her: on the delivery trip from Belfast to Southampton, and on the short journeys to Cherbourg and Queenstown. This dataset includes everyone that travelled on the maiden voyage but also the delivery and passengers who were fortunate enough to disembark.

titanic dataset

Dataset

This dataset was created by Vijesh

Contents

Analysis of ‘Titanic survival’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/dushyantkhinchi/titanic-survival on 28 January 2022.

--- No further description of dataset provided by original source ---

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Titanic csv’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/fossouodonald/titaniccsv on 28 January 2022.

--- Dataset description provided by original source is as follows ---

this dataset is the result of titanic csv

--- Original source retains full ownership of the source dataset ---