All the metrics used are available in the sklearn package, see the documentation at https://scikit-learn.org/stable/api/sklearn.metrics.html.

The analysed data and complete scripts for the permutation tests and mixed linear regression models (MLRMs) used in the paper 'Identifying Key Drivers of Product Formation in Microbial Electrosynthesis with a Mixed Linear Regression Analysis'.

Python version 3.10.13 with packages numpy, pandas, os, scipy.optimize, scipy.stats, sklearn.metrics, matplotlib.pyplot, statsmodels.formula.api, seaborn are required to run the .py files. Ensure all packages are installed before running the scripts. Data files required to run the code (.xlsx and .csv format) are included in the relevant folders.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6372737%2F2d0c8c299f63bb8a5823683346ba1ba8%2FImage2.jpg?generation=1659570752665846&alt=media">

The dataset contains 2 folders: one with the test data and the other one with train data. The test-train-split ratio is 0.14, with the test dataset containing 114 images and the train dataset containing 711. The images have a resolution of 240x240 pixels in RGB color model. Both the folders contain 3 classes:

• Adidas
• Converse
• Nike ** ** ### Inspiration

This dataset is ideal for performing multiclass classification with deep neural networks like CNNs or simpler machine learning classification models. You can use Tensorflow, his high-level API keras, Sklearn, PyTorch or other deep/machine learning libraries to building the model from scratch or, as an alternative, fetching pretrained models as well as fine-tuning them. It is also possible to modify the size of the images or preprocessing them using OpenCV , and check if the accuracy of the model improves.
Remember to upvote if you found the dataset useful :).

Collection methodology

The dataset was obtained downloading images from Google images.

The images with a .webp format were transformed into .jpg images. The obtained images were randomly shuffled and resized so that all the images had a resolution of 240x240 pixels. Then, they were split into train and test datasets and saved.

The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.

It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.

The columns in this dataset are:

• Id
• SepalLengthCm
• SepalWidthCm
• PetalLengthCm
• PetalWidthCm
• Species