This dataset contains Apple stock prices from 2014 to 2023, along with various technical indicators. It can be used for multi-class classification to predict the price trend for the next day based on…
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2516 instances - 19 features - 3 classes - 0 missing values
This dataset is designed to evaluate companies based on quality and valuation metrics. It uses a two-stage scoring system to classify companies into categories like 'High Quality, Fair Valuation'. The…
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719 instances - 11 features - 6 classes - 0 missing values
This dataset is derived from the Credit Risk Analytics book by Harald, Daniel, and Bart, as described in the Medium article by Roi Polanitzer. It focuses on predicting financial difficulties and…
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5000 instances - 8 features - 10 classes - 0 missing values
An updated version of the Customer Churn dataset for binary classification. The target variable 'exited' is now treated as nominal with categories: 'not_churned' and 'churned'.
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35000 instances - 25 features - 2 classes - 0 missing values
A fraud detection dataset for binary classification. The target variable is 'is_fraud', indicating whether a transaction is fraudulent.
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5227 instances - 22 features - 2 classes - 0 missing values
A dataset for binary classification of credit approval status. Features include customer demographics, financial attributes, and credit history. The target variable `class` indicates whether the…
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1000 instances - 21 features - 2 classes - 0 missing values
This dataset contains customer data for a classification task to predict churn based on behavioral and demographic features. The target variable 'exited' indicates whether a customer has churned (1)…
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175028 instances - 25 features - 2 classes - 0 missing values
This dataset contains customer data for a classification task to predict churn based on behavioral and demographic features. The target variable 'exited' indicates whether a customer has churned (1)…
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175028 instances - 25 features - 2 classes - 0 missing values
An updated version of the dataset for classifying risk levels in transactions. The target variable 'anomaly' is now treated as nominal with categories: low risk, moderate risk, and high risk.
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78600 instances - 19 features - 3 classes - 0 missing values
Updated Fraud Detection dataset with nominal target for binary classification.
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4156 instances - 29 features - 2 classes - 25715 missing values
Updated Fraud Detection dataset with nominal target for binary classification.
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4156 instances - 29 features - 2 classes - 25715 missing values
A fraud detection dataset with date components and categorical encodings for OpenML upload.
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4156 instances - 29 features - 0 classes - 25715 missing values