I need to utilize TensorFlow for a project to classify items based on their attributes to a certain class (either 1, 2, or 3).

Only problem is almost every TF tutorial or example I find online is about image recognition or text classification. I can't find anything about classification based on numbers. I guess what I'm asking for is where to get started. If anyone knows of a relevant example, or if I'm just thinking about this completely wrong.

We are given the 13 attributes for each item, and need to use the TF neural network to classify each item correctly (or mark the margin of error). But nothing online is showing me even how to start with this kind of dataset.

Example of dataset: (first value is class, other values are attributes)

2, 11.84, 2.89, 2.23, 18,   112, 1.72, 1.32, 0.43, 0.95, 2.65, 0.96, 2.52, 500
3, 13.69, 3.26, 2.54, 20,   107, 1.83, 0.56, 0.5,  0.8,  5.88, 0.96, 1.82, 680
3, 13.84, 4.12, 2.38, 19.5, 89,  1.8,  0.83, 0.48, 1.56, 9.01, 0.57, 1.64, 480
2, 11.56, 2.05, 3.23, 28.5, 119, 3.18, 5.08, 0.47, 1.87, 6,    0.93, 3.69, 465
1, 14.06, 1.63, 2.28, 16,   126, 3,    3.17, 0.24, 2.1,  5.65, 1.09, 3.71, 780

for this kind problem TensorFlow have an in depth tutorial here or in toward data science here

if your looking for videos to start i think sentdex's tutorials on the titanic data-set is what your looking for although he is using k means to do the classification (actually I think his entire deep learning/machine learning playlist is great to start with)
you can find it here

otherwise if your looking for basic how to start

first prepossessing:

try first separating the data into class labels and inputs (pandas lib should be able to help you with this)

make your class labels into a one-hot array

than normalize the data:

it looks like your different data attributes have wildly different ranges, make sure to get them all in the same range between 0 and 1

build your model:

a simple fully connected net should do the trick remember to make the output layer the same size as the number of classes you have

use an argmax function on the output of the finale layer to decide which class the model thinks is the proper classification