SMOTE Data Generator

This tool generates synthetic data for imbalanced datasets using the SMOTE technique. Upload your data file, specify the target class column, and define the number of synthetic samples to generate. The result table will display the synthetic data below. This function page was created for the following paper: "Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea"

Step 1: Upload Your File (Excel or CSV):

Upload your dataset containing numeric features and a target class column. The tool supports Excel (.xlsx) and CSV (.csv) files.

Step 2: Specify Target Class Column Name:

Enter the exact name of the column representing the target class (e.g., "Class" or "Target").

Step 3: Enter Number of Synthetic Samples:

Enter the number of synthetic samples to generate for the minority class.

Generated Synthetic Data

The table below shows the synthetic data generated using the SMOTE technique:

Numeric features are interpolated between randomly selected samples.
Binary features (e.g., 0/1 or 1/2) are rounded to maintain valid values.
The target column retains the minority class value.