A classic abalone dataset from the UCI Machine Learning Repository, containing 4,177 samples and 8 physical measurement features, predicting abalone age through shell ring count, widely used in regression analysis research.
A classic regression dataset suitable for various data analysis scenarios from beginner to advanced.
The data comes from physical measurements of real abalone samples, including length, diameter, height, and weights of various parts.
Predicting shell ring count (age) from physical measurements, making it an ideal dataset for learning linear regression, SVR, XGBoost, and other regression algorithms.
Shell ring count can be used directly for regression or grouped into a multi-classification task (juvenile/adult/senior).
The data quality is excellent, with no missing values, containing one categorical feature (gender: M/F/I) and seven continuous features.
4,177 samples are sufficient to support complex model training while being convenient for quick experiments and teaching demonstrations.
Originating from the UCI Machine Learning Repository, it is a classic benchmark dataset in the field of regression analysis.
From classroom teaching to research experiments, it can provide value
Predict the number of rings on the abalone shell (age), practice linear regression, random forests, XGBoost, and other regression algorithms
Group the number of rings into age ranges, transforming it into a multi-class problem for training
Explore the correlation between physical measurement features, practice feature selection and dimensionality reduction techniques
Visualize the distribution differences of physical features across different genders and age groups
The following are the first few rows of the abalone dataset
sex,length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,rings M,0.455,0.365,0.095,0.514,0.2245,0.101,0.15,15 M,0.35,0.265,0.09,0.2255,0.0995,0.0485,0.07,7 F,0.53,0.42,0.135,0.677,0.2565,0.1415,0.21,9 M,0.44,0.365,0.125,0.516,0.2155,0.114,0.155,10 I,0.33,0.255,0.08,0.205,0.0895,0.0395,0.055,7
From browsing to analysis, you can start your data science project in just a few minutes
View dataset details on the Ace Data Cloud platform, including field descriptions, sample size, and licensing agreements.
Download the CSV file (192 KB), data is ready to use without additional cleaning.
Use pandas.read_csv() to load the data and start exploratory analysis, modeling, and visualization.
A classic regression dataset with open licensing, available for immediate download. Whether you are a beginner in machine learning or an experienced data scientist, this dataset is worth trying.
