A classic abalone dataset from the UCI Machine Learning Repository, containing 4,177 samples and 8 physical measurement features. It predicts abalone age through shell ring counts and is widely used in regression analysis research.
A classic regression dataset suitable for a wide range of data analysis scenarios, from beginner to advanced
The data comes from physical measurements of real abalone samples, including length, diameter, height, and the weight of each part.
Predict the number of shell rings (age) through physical measurements; an ideal dataset for learning regression algorithms such as linear regression, SVR, and XGBoost.
The number of shell rings can be used directly for regression or grouped into a multi-class classification task (juvenile/adult/elderly).
The data is of high quality, with no missing values, and includes one categorical feature (sex: M/F/I) and seven continuous features.
The 4,177 samples are sufficient to support complex model training while also being convenient for rapid experimentation and teaching demonstrations.
Derived 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 deliver value
Predict abalone shell ring counts (age), and practice regression algorithms such as linear regression, random forest, and XGBoost
Group shell ring counts into age groups and transform them into a multiclass classification problem for training
Explore correlations among physical measurement features and practice feature selection and dimensionality reduction techniques
Visualize differences in the distribution of physical features across different sexes and age groups
Below are examples of 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
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View dataset details on the Ace Data Cloud platform to learn metadata such as field descriptions, sample size, and license agreement.
Download the ZIP delivery package after purchase, containing the original CSV (192 KB), data source attribution, and CC BY 4.0 license text; simply extract it to load.
Use pandas.read_csv() to load the data and begin exploratory analysis, modeling, and visualization.
A classic regression dataset with previewable license information and data samples, available for download after purchase. Whether you are a machine learning beginner or an experienced data scientist, this dataset is worth trying.
