A classic mushroom dataset from the UCI Machine Learning Repository, containing 8,124 samples and 22 categorical features, used to determine whether mushrooms are edible or poisonous. It is a standard introductory dataset for classification learning.
A dataset with purely categorical features, ideal for learning decision trees and rule mining
Determine whether a mushroom is edible (edible) or poisonous (poisonous); the results are intuitive and practically meaningful.
All 22 features are categorical variables (cap shape, color, odor, etc.), suitable for practicing one-hot encoding and label encoding.
Categorical features make it an ideal dataset for learning decision trees, random forests, and rule-learning algorithms.
With 8,124 samples, the dataset is sufficiently sized, with a relatively balanced distribution of edible and poisonous classes.
A single feature such as odor can achieve nearly perfect classification, making it suitable for exploring feature importance.
From the UCI Machine Learning Repository, this is a classic binary classification dataset widely cited in academia.
Valuable for everything from introductory learning to advanced feature analysis
Purely categorical features are ideal for learning decision trees, CART, and rule-learning algorithms
Use Naive Bayes, logistic regression, SVM, and other algorithms for edible/poisonous classification
Discover the strong predictive power of key features such as odor, and practice information gain and chi-square tests
Practice one-hot encoding, label encoding, and target encoding techniques for categorical variables
Below are sample rows from the mushroom dataset (all features are encoded as single letters)
class,cap_shape,cap_surface,cap_color,bruises,odor,gill_attachment,...,habitat p,x,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u e,x,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g e,b,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m p,x,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,u e,x,s,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g
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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.
After purchase, download the ZIP delivery package, which includes a 374 KB CSV file, source attribution, and CC BY 4.0 license text; field names have been added to the official headerless CSV data.
Use pandas.read_csv() to load the data, together with pd.get_dummies() to encode categorical features.
A classic classification dataset with previewable licensing information and data samples, available for download after purchase. Its purely categorical feature design makes it an ideal introductory dataset for decision trees and rule learning.
