PRESC/datasets

Classification datasets

File	Description	Source link (with details)	Preprocessing applied	Label column
generated.csv	Automatically-generated dataset containing data samples separated into very well-delineated categories. This can be considered a "best-case scenario" test case.			label
defaults.csv	Defaults on credit card payments	UCI	Minor (column name reformatting)	defaulted
winequality.csv	Quality ratings of Portuguese white wines	UCI	Added binarized label column recommend indicating quality >= 7	recommend
vehicles.csv	Recognizing vehicle type from its silhouette	OpenML	None	Class
eeg.csv	EEG eye state measurements	OpenML	Dropped a few outlier rows	Class
kick_starter.csv	Kick stater project state	Kaggle	Dropped unnamed columns; Minor column name reformatting; Calculated duration of the project and dropped start and end dates; Dropped some rows with wrong input type; Dropped main category column and kept category column; randomply sampled 30% of the data; Filled NA with 0 for numeric values	state
mushrooms.csv	Classification mushrooms edibility based on physical features	UCI	Renamed the column class to edibility for descriptiveness	edibility
Surgical-deepnet.csv	Surgical cases related to complication	Kaggle	None	complication
gender_classification.csv	use hobbies to guess gender	Kaggle	None	Gender

These can all be loaded using Pandas:

import pandas as pd
dataset = pd.read_csv("file.csv")