Hey Offir - how did you go on the santander data set?
There are a heap of tabular data sets around, look at the data sets available on kaggle, I think there arenâ€™t many competitions though.

Hey, Iâ€™m working on a similar problem, maybe you all could help me figure this out: Iâ€™m trying to use a tabular learner on a wide dataset, and all the values are continuous. I keep getting a â€śdivide by zeroâ€ť error, and I think itâ€™s due to the way that Iâ€™m creating my databunch, but not sure what exactly Iâ€™m doing wrong.

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from fastai.tabular import *
import os
print(os.listdir("../input"))
path = Path('../input')
```df = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')

/opt/conda/lib/python3.6/site-packages/torch/nn/init.py in kaiming_uniform_(tensor, a, mode, nonlinearity)
288 fan = _calculate_correct_fan(tensor, mode)
289 gain = calculate_gain(nonlinearity, a)
--> 290 std = gain / math.sqrt(fan)
291 bound = math.sqrt(3.0) * std # Calculate uniform bounds from standard deviation
292 with torch.no_grad():
ZeroDivisionError: float division by zero```

Hey @Stephen_F, the error might be because you are not telling the model which columns contain constant values and which ones contain categorical values. You might want to add a line like cont_names = ['age', 'gender' and so on] or since all your columns are constant you can write cont_names = list(df.columns). Then in your learner pass that as an argument like this cont_names=cont_names
Regards

@dipam7 Iâ€™ll be darned, I think that worked! I think when I tried it before I accidentally left the target variable in the list of columns and that threw me off the trail. Thanks!