Project to examine the performance of neural networks in classification tasks using tabular data, with PyTorch.
The dataset can be accessed from https://archive.ics.uci.edu/dataset/109/wine .
The Wine dataset created by Stefan Aeberhard and M. Forina is a popular dataset for DS/ML studies.
| Variable Name | Role | Type |
|---|---|---|
| class | Target | Categorical |
| Alcohol | Feature | Continuous |
| Malicacid | Feature | Continuous |
| Ash | Feature | Continuous |
| Alcalinity_of_ash | Feature | Continuous |
| Magnesium | Feature | Integer |
| Total_phenols | Feature | Continuous |
| Flavanoids | Feature | Continuous |
| Nonflavanoid_phenols | Feature | Continuous |
| Proanthocyanins | Feature | Continuous |
| Color_intensity | Feature | Continuous |
| Hue | Feature | Continuous |
| 0D280_0D315_of_diluted_wines | Feature | Continuous |
| Proline | Feature | Integer |
- Install the ucimlrepo package
pip install ucimlrepo
- Import the dataset into your code
from ucimlrepo import fetch_ucirepo # fetch dataset wine = fetch_ucirepo(id=109) # data (as pandas dataframes) X = wine.data.features y = wine.data.targets # metadata print(wine.metadata) # variable information print(wine.variables)
- Execute the code. Please feel free to modify the hyperparameters to see the performance of the model.
In our experiment we were able to obtain an accuracy of 98.1%.
Aeberhard,Stefan and Forina,M.. (1991). Wine. UCI Machine Learning Repository. https://doi.org/10.24432/C5PC7J.
Tam,Adrian.(2023).Building a Multiclass Classification Model in PyTorch. https://machinelearningmastery.com/building-a-multiclass-classification-model-in-pytorch/