The Titanic dataset is a popular dataset used for data analysis and machine learning tasks. It contains various information about passengers aboard the Titanic, including whether they survived or not. Here's a brief description of each of the columns:

PassengerId: A unique identifier for each passenger. Survived: Indicates whether the passenger survived or not. (0 = No, 1 = Yes) Pclass: Ticket class (1 = 1st, 2 = 2nd, 3 = 3rd) Name: Name of the passenger. Sex: Gender of the passenger. Age: Age of the passenger in years. (Fractional if less than 1) SibSp: Number of siblings or spouses aboard the Titanic. Parch: Number of parents or children aboard the Titanic. Ticket: Ticket number. Fare: Fare paid for the ticket. Cabin: Cabin number. Embarked: Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton) This dataset is often used for tasks such as predicting survival based on various factors or analyzing demographics of passengers aboard the Titanic.

Titanic Dataset (for Machine Learning)The Titanic dataset is a classic and widely used dataset for machine learning and data analysis. It contains information about the passengers of the RMS Titanic, which tragically sank on its maiden voyage on April 15, 1912. The dataset provides details about each passenger, including their demographics, ticket information, and survival status. This dataset is often used to demonstrate and practice various machine learning techniques, particularly classification.This dataset is divided into two: training set & testing set.Dataset Variables:PassengerId: count for each passengerSurvived: 0 = No; 1 = YesName: name of passengerSex: passenger's sexAge: passenger's ageSibSp: number of siblings/spouses abroad the TitanicParch: number of parents/children abroad the TitanicTicket: ticket numberFare: passenger fareCabin: cabin numberEmbarked: port where passenger embarked (C = Cherbourg; Q = Queenstown; S = Southampton)

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.

Dataset

This dataset was created by ManishaPrajapati

Released under MIT

Contents

Analysis of ‘Titanic: Machine Learning from Disaster’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/shuofxz/titanic-machine-learning-from-disaster on 28 January 2022.

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

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

Titanic dataset for classification training.

Analysis of ‘Titanic: cleaned data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jamesleslie/titanic-cleaned-data on 30 September 2021.

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

Introduction

This dataset was created in this notebook as part of a three-part series. The data is in machine-learning-ready format, with all missing values for the Age, Fare and Embarked columns having been imputed.

Data imputation

• Age: this column was imputed by using the median age for the passenger's title (Mr, Mrs, Dr etc).
• Fare: the single missing value in this column was imputed using the median value for that passenger's class.
• Embarked: the two missing values here were imputed using the Pandas backfill method.

Usage

This data is used in both the second and third parts of the series.

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

Analysis of ‘Titanic Solution for Beginner's Guide’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/harunshimanto/titanic-solution-for-beginners-guide on 14 February 2022.

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

Overview

The data has been split into two groups:

training set (train.csv)
test set (test.csv)

The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features.

The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic.

We also include gender_submission.csv, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like.

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: A proxy for socio-economic status (SES) 1st = Upper 2nd = Middle 3rd = Lower

age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5

sibsp: The dataset defines family relations in this way... Sibling = brother, sister, stepbrother, stepsister Spouse = husband, wife (mistresses and fiancés were ignored)

parch: The dataset defines family relations in this way... Parent = mother, father Child = daughter, son, stepdaughter, stepson Some children travelled only with a nanny, therefore parch=0 for them.

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

This dataset contains cleaned Titanic passenger data for EDA and machine learning tasks. Includes features like age, sex, class, fare, and family details. Ideal for survival prediction and beginner ML projects.

🚀 Great for:

Feature engineering

Data visualization

Classification modeling

🔄 Both train and test sets included.

💬 If you find this dataset helpful, please upvote and share your notebook!

Dataset used in my 🛳️ Titanic - Top 1% with KNN [0.81818] notebook. It contains all the leaderboard's entries from the Titanic - Machine Learning from Disaster competition in March 2023.

Explora nuestro proyecto de aprendizaje automático para predecir la supervivencia en el Titanic. Con un puntaje perfecto de 1.0 y una matriz de confusión impecable, revelamos patrones asombrosos en los datos históricos.

This is the classic Titanic Dataset provided in the Kaggle Competition K Kernel and then cleaned in one of the most popular Kernels there. Please see the Kernel titled, "A Data Science Framework: To Achieve 99% Accuracy" for a great lesson in data science. This Kernel gives a great explanaton of the thinking behind the of this data cleaning as well as a very professional demonstration of the technologies and skills to do so. It then continues to provide an overview of many ML techniques and it is copiously and meticulously documented with many useful citations.

Of course, data cleaning is an essential skill in data science but I wanted to use this data for a study of other machine learning techniques. So, I found and used this set of data that is well known and cleaned to a benchmark accepted by many.

Author: H. Altay Guvenir, Burak Acar, Haldun Muderrisoglu
Source: UCI
Please cite: UCI

Cardiac Arrhythmia Database
The aim is to determine the type of arrhythmia from the ECG recordings. This database contains 279 attributes, 206 of which are linear valued and the rest are nominal.

Concerning the study of H. Altay Guvenir: "The aim is to distinguish between the presence and absence of cardiac arrhythmia and to classify it in one of the 16 groups. Class 01 refers to 'normal' ECG classes, 02 to 15 refers to different classes of arrhythmia and class 16 refers to the rest of unclassified ones. For the time being, there exists a computer program that makes such a classification. However, there are differences between the cardiologist's and the program's classification. Taking the cardiologist's as a gold standard we aim to minimize this difference by means of machine learning tools.

The names and id numbers of the patients were recently removed from the database.

Attribute Information

  1 Age: Age in years , linear
  2 Sex: Sex (0 = male; 1 = female) , nominal
  3 Height: Height in centimeters , linear
  4 Weight: Weight in kilograms , linear
  5 QRS duration: Average of QRS duration in msec., linear
  6 P-R interval: Average duration between onset of P and Q waves
   in msec., linear
  7 Q-T interval: Average duration between onset of Q and offset
   of T waves in msec., linear
  8 T interval: Average duration of T wave in msec., linear
  9 P interval: Average duration of P wave in msec., linear
 Vector angles in degrees on front plane of:, linear
 10 QRS
 11 T
 12 P
 13 QRST
 14 J
 15 Heart rate: Number of heart beats per minute ,linear
 Of channel DI:
  Average width, in msec., of: linear
  16 Q wave
  17 R wave
  18 S wave
  19 R' wave, small peak just after R
  20 S' wave
  21 Number of intrinsic deflections, linear
  22 Existence of ragged R wave, nominal
  23 Existence of diphasic derivation of R wave, nominal
  24 Existence of ragged P wave, nominal
  25 Existence of diphasic derivation of P wave, nominal
  26 Existence of ragged T wave, nominal
  27 Existence of diphasic derivation of T wave, nominal
 Of channel DII: 
  28 .. 39 (similar to 16 .. 27 of channel DI)
 Of channels DIII:
  40 .. 51
 Of channel AVR:
  52 .. 63
 Of channel AVL:
  64 .. 75
 Of channel AVF:
  76 .. 87
 Of channel V1:
  88 .. 99
 Of channel V2:
  100 .. 111
 Of channel V3:
  112 .. 123
 Of channel V4:
  124 .. 135
 Of channel V5:
  136 .. 147
 Of channel V6:
  148 .. 159
 Of channel DI:
  Amplitude , * 0.1 milivolt, of
  160 JJ wave, linear
  161 Q wave, linear
  162 R wave, linear
  163 S wave, linear
  164 R' wave, linear
  165 S' wave, linear
  166 P wave, linear
  167 T wave, linear
  168 QRSA , Sum of areas of all segments divided by 10,
    ( Area= width * height / 2 ), linear
  169 QRSTA = QRSA + 0.5 * width of T wave * 0.1 * height of T
    wave. (If T is diphasic then the bigger segment is
    considered), linear
 Of channel DII:
  170 .. 179
 Of channel DIII:
  180 .. 189
 Of channel AVR:
  190 .. 199
 Of channel AVL:
  200 .. 209
 Of channel AVF:
  210 .. 219
 Of channel V1:
  220 .. 229
 Of channel V2:
  230 .. 239
 Of channel V3:
  240 .. 249
 Of channel V4:
  250 .. 259
 Of channel V5:
  260 .. 269
 Of channel V6:
  270 .. 279

Class code - class - number of instances:

  01       Normal        245
  02       Ischemic changes (Coronary Artery Disease)  44
  03       Old Anterior Myocardial Infarction      15
  04       Old Inferior Myocardial Infarction      15
  05       Sinus tachycardy    13
  06       Sinus bradycardy    25
  07       Ventricular Premature Contraction (PVC)    3
  08       Supraventricular Premature Contraction    2
  09       Left bundle branch block     9 
  10       Right bundle branch block    50
  11       1. degree AtrioVentricular block    0 
  12       2. degree AV block        0
  13       3. degree AV block        0
  14       Left ventricule hypertrophy        4
  15       Atrial Fibrillation or Flutter        5
  16       Others         22

Titanic Passenger Data

This dataset contains information about the passengers aboard the RMS Titanic, which tragically sank during its maiden voyage in April 1912. It provides detailed data points for survival analysis and predictive modeling, including demographic details, ticket class, fare, and survival outcomes.

Dataset Highlights:

• Passenger Details: Information such as Name, Age, Gender, and Embarked Port.
• Socioeconomic Status: Passenger Class (1st, 2nd, or 3rd) and Fare Price.
• Survival Information: Whether the passenger survived or perished.
• Family Relationships: Number of Siblings/Spouses and Parents/Children aboard.

This dataset is ideal for exploring patterns of survival, understanding social dynamics aboard the Titanic, and testing machine learning models for classification problems.

Suggested Use Cases:

• Survival rate analysis by age, gender, or class.
• Building classification models for survival prediction.
• Statistical tests and hypothesis exploration on historical data.

Dive in to analyze one of the most famous shipwrecks in history!

Context

Data set was created by preprocessing (filling lost data, extracting new features) of Titanic - Machine Learning Disaster data set.

Using this processed data set, the machine learning models can be applied directly.

You can see preprocessing step in notebook: https://www.kaggle.com/fethiye/titanic-predict-survival-prediction

Overview

The data has been split into two groups:

training set (train.csv)
test set (test.csv)

The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features.

The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic.

We also include gender_submission.csv, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like.

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: A proxy for socio-economic status (SES) 1st = Upper 2nd = Middle 3rd = Lower

age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5

sibsp: The dataset defines family relations in this way... Sibling = brother, sister, stepbrother, stepsister Spouse = husband, wife (mistresses and fiancés were ignored)

parch: The dataset defines family relations in this way... Parent = mother, father Child = daughter, son, stepdaughter, stepson Some children travelled only with a nanny, therefore parch=0 for them.

Context

泰塔尼克号数据集的子集

Content

1313个数据，11列特征

Acknowledgements

Kaggle platform

Inspiration

For spreading machine learning basic knowledge