Team solutions

Case Kaufland Team “Ignacio”

A simple approach using neural networks.

I’m learning data science, and this is my first experiment with Neural Networks.
The accuracy is about 2.78%, because we have over 50 categories in the training dataset

Thank you for your support. This is an amazing competition.


Case and members

Case: Kaufland

Members: Ignacio Bengoechea

Providers: No


Kaufland data must be under subdirectoy Kaufland

Train and Test folder must be created empty

Preparation of the data

The train/test folders have been created with the sample core given by the mentor mitzev.

The train/test dataset images have been resized to 320×240 grayscale. This give us 76800 pixels.

This web because i couldn’t process al the images on my computer.

The dimensions of the data have been reduced using PCA to 1000 pixels. To show fast results.


I have used a simple neural network to classify the object.

This NN has 1000 inputs, the same number as the PCA output

This NN has 53 outputs, thats the number of clases of the trainf/folder

The value of epochs, learning rate and neurons have been selected using a hyperparameter tuning, not included in the final code.

Evaluation and results

Sorry, the accuracy is poor, a 2.78%.

That’s because this is a simple model, and we have over 53 categories in training dataset. The accuracy is almost random.

Jupiter Notebook

You can download the jupyter notebook here:

Kaufland Jupyter Notebook

Thank you for your support. This is an amazing competition.

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5 thoughts on “Case Kaufland Team “Ignacio”

  1. 1

    If you kept the colors you should get better results. It is excellent that you shared all you code in the notebook. You should also try to import additional datasets and enrich the provided data.

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