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[kaggle]Analytics Vidhya Loan Prediction
{'X_num': {'ApplicantIncome': {0: -1.5474634170532227, 1: -1.1698240041732788, 2: -0.9614736437797546, 3: -1.3783310651779175, 4: -0.4060468375682831, 5: -1.4718408584594727, 6: 0.8235251307487488, 7: -0.9806960821151733, 8: -1.3203742504119873, 9: 0.7123724222183228, 10: 0.051976729184389114, 11: 0.199598029255867, 12...
binclass
[kaggle]Audit Data
"{'X_num': {'Sector_score': {0: 0.9620829224586487, 1: -0.3839802145957947, 2: -1.1107414960861206, (...TRUNCATED)
binclass
[kaggle]Automobiles
"{'X_num': {'normalized_losses': {0: 1.07398521900177, 1: 0.4124636948108673, 2: 0.7290838360786438,(...TRUNCATED)
binclass
[kaggle]Bigg Boss India
"{'X_num': {'AverageTRP': {0: -0.9086897969245911, 1: -1.365738868713379, 2: -0.7736529111862183, 3:(...TRUNCATED)
binclass
[kaggle]Breast Cancer Dataset
"{'X_num': {'radius_mean': {0: -0.14935018122196198, 1: -1.4926971197128296, 2: 0.5436511039733887, (...TRUNCATED)
binclass
[kaggle]Campus Recruitment
"{'X_num': {'sl_no': {0: -0.6383345127105713, 1: -0.436063677072525, 2: -1.2235928773880005, 3: 0.20(...TRUNCATED)
binclass
[kaggle]chronic kidney disease
"{'X_num': {'Sg': {0: 0.12877696752548218, 1: 0.20355501770973206, 2: 0.20355501770973206, 3: 0.1796(...TRUNCATED)
binclass
[kaggle]Collection of Classification & Regression Datasets(House Price)
"{'X_num': {'crime_rate': {0: 0.3026769161224365, 1: 1.3436630964279175, 2: -1.1707563400268555, 3: (...TRUNCATED)
binclass
[kaggle]Compositions of Glass
"{'X_num': {'RI': {0: 0.155638188123703, 1: 0.15518225729465485, 2: 0.14920392632484436, 3: 0.154190(...TRUNCATED)
binclass
[kaggle]Credit Card Approval
"{'X_num': {'Age': {0: -0.9363404512405396, 1: -0.9668024778366089, 2: 1.5633041858673096, 3: 1.5591(...TRUNCATED)
binclass
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ExcelFormer Benchmark

The datasets used in ExcelFormer. The usage example is as follows:

from datasets import load_dataset
import pandas as pd
import numpy as np

# process train split, similar to other splits
data = {}
datasets = load_dataset('jyansir/excelformer') # load 96 small-scale datasets in default
# datasets = load_dataset('jyansir/excelformer', 'large') # load 21 large-scale datasets with specification
dataset = datasets['train'].to_dict()
for table_name, table, task in zip(dataset['dataset_name'], dataset['table'], dataset['task']):
    data[table_name] = {
        'X_num': None if not table['X_num'] else pd.DataFrame.from_dict(table['X_num']),
        'X_cat': None if not table['X_cat'] else pd.DataFrame.from_dict(table['X_cat']),
        'y': np.array(table['y']),
        'y_info': table['y_info'],
        'task': task,
    }
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