Titanic Dataset · Ace Data Cloud

Titanic Passenger Dataset：
Machine Learning Classic Challenge

Based on the real passenger data from the 1912 Titanic sinking. 887 samples, 12 features—covering numerical, categorical, and text features, making it an ideal dataset for learning binary classification, feature engineering, and data preprocessing.

Titanic Passenger Dataset
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CSV Format · 44 KB Public Domain License Derived from Real Historical Data of 1912
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887
Number of Samples
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12
Number of Features
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2
Number of Categories
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44KB
File Size

Dataset Highlights

The Titanic dataset has good reasons to be the most popular introductory dataset in the global ML community.

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Classic Binary Classification

A classic survival prediction binary classification problem with a clear goal—predicting whether passengers survived the sinking, making it an excellent starting point for learning classification algorithms.

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Rich Features

Includes numerical, categorical, and text features, covering multidimensional information such as age, fare, gender, and cabin class, suitable for various feature processing methods.

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Real Data

Based on real historical data from the Titanic in 1912, each record corresponds to a real passenger, giving deeper meaning to data analysis.

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Data Cleaning

Contains missing values (such as age, cabin number), suitable for data preprocessing practice, learning practical skills like filling missing values and handling outliers.

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Widely Used

One of the most commonly used introductory datasets in the global ML community, a classic Kaggle competition topic, with a wealth of tutorials and reference solutions.

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Compact and Portable

A 44KB CSV file contains all the data, supports quick offline loading, no worries about storage or bandwidth issues, start analyzing anytime, anywhere.

Applicable Scenarios

From classroom exercises to Kaggle competitions - common uses of the Titanic dataset

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Survival Prediction

Use algorithms like logistic regression, random forests, and XGBoost to predict passenger survival probabilities, a classic binary classification introductory task

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Feature Engineering

Extract titles from names, combine family size, bin ages and fares, practice feature creation and transformation techniques

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Data Visualization

Plot charts showing the relationship between survival rates and gender, class, age, intuitively understand data distribution and feature correlation

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Machine Learning Teaching

Cover the complete process of data cleaning, feature engineering, model training, and evaluation, suitable for teaching and self-study

Data Preview

Sample examples of the Titanic dataset (CSV format)

CSV
Survived,Pclass,Name,Sex,Age,SiblingsSpouses,ParentsChildren,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
Survived Whether survived Pclass Class level Name Name Sex Gender Age Age SiblingsSpouses Number of siblings/spouses ParentsChildren Number of parents/children Fare Fare

3 Steps to Get Started Quickly

From browsing to using, it only takes a few minutes

01

Browse the Dataset

View detailed descriptions, field definitions, and data previews of the Titanic dataset on the Ace Data Cloud platform.

02

Download the CSV File

One-click download of a 44 KB CSV file to your local machine, no registration, no payment, get it immediately.

03

Load and Use

Load the data using Python, R, or any data analysis tool, and start training models or creating visualizations.

Python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
# Load data
df = pd.read_csv("titanic.csv")
# Feature engineering: extract titles, calculate family size
df["Title"] = df["Name"].str.extract(r" ([A-Za-z]+)\.")
df["FamilySize"] = df["SiblingsSpouses"] + df["ParentsChildren"] + 1
# Select features and process
features = ["Pclass", "Sex", "Age", "Fare", "FamilySize"]
df["Sex"] = df["Sex"].map({"male": 0, "female": 1})
df["Age"].fillna(df["Age"].median(), inplace=True)
# Split into training and testing sets
X = df[features]
y = df["Survived"]
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)
# Train the random forest classifier
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)
# Evaluate the model
y_pred = clf.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred):.2%}")
print(classification_report(y_test, y_pred, target_names=["Did not survive", "Survived"]))
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The Titanic dataset is a classic challenge for millions of developers worldwide to learn machine learning. Download for free and start exploring immediately.

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