|
--- |
|
tags: |
|
- tabular-classification |
|
- sklearn |
|
dataset: |
|
- wine-quality |
|
widget: |
|
- structuredData: |
|
fixed_acidity: |
|
- 7.3 |
|
- 7.8 |
|
- 10.3 |
|
volatile_acidity: |
|
- 0.7 |
|
- 0.88 |
|
- 0.32 |
|
citric_acid: |
|
- 0 |
|
- 0 |
|
- 0.45 |
|
residual_sugar: |
|
- 1.9 |
|
- 2.6 |
|
- 6.4 |
|
chlorides: |
|
- 0.076 |
|
- 0.098 |
|
- 0.073 |
|
free_sulfur_dioxide: |
|
- 11 |
|
- 25 |
|
- 5 |
|
total_sulfur_dioxide: |
|
- 34 |
|
- 67 |
|
- 13 |
|
density: |
|
- 0.9978 |
|
- 0.9968 |
|
- 0.9976 |
|
pH: |
|
- 3.51 |
|
- 3.2 |
|
- 3.23 |
|
sulphates: |
|
- 0.56 |
|
- 0.68 |
|
- 0.82 |
|
alcohol: |
|
- 9.4 |
|
- 9.8 |
|
- 12.6 |
|
--- |
|
|
|
## Wine Quality classification |
|
|
|
### A Simple Example of Scikit-learn Pipeline |
|
|
|
> Inspired by https://towardsdatascience.com/a-simple-example-of-pipeline-in-machine-learning-with-scikit-learn-e726ffbb6976 by Saptashwa Bhattacharyya |
|
|
|
|
|
### How to use |
|
|
|
```python |
|
from huggingface_hub import hf_hub_url, cached_download |
|
import joblib |
|
import pandas as pd |
|
|
|
REPO_ID = "julien-c/wine-quality" |
|
FILENAME = "sklearn_model.joblib" |
|
|
|
|
|
model = joblib.load(cached_download( |
|
hf_hub_url(REPO_ID, FILENAME) |
|
)) |
|
|
|
# model is a `sklearn.pipeline.Pipeline` |
|
``` |
|
|
|
#### Get sample data from this repo |
|
|
|
```python |
|
data_file = cached_download( |
|
hf_hub_url(REPO_ID, "winequality-red.csv") |
|
) |
|
winedf = pd.read_csv(data_file, sep=";") |
|
|
|
|
|
X = winedf.drop(["quality"], axis=1) |
|
Y = winedf["quality"] |
|
|
|
print(X[:3]) |
|
``` |
|
|
|
| | fixed acidity | volatile acidity | citric acid | residual sugar | chlorides | free sulfur dioxide | total sulfur dioxide | density | pH | sulphates | alcohol | |
|
|---:|----------------:|-------------------:|--------------:|-----------------:|------------:|----------------------:|-----------------------:|----------:|-----:|------------:|----------:| |
|
| 0 | 7.4 | 0.7 | 0 | 1.9 | 0.076 | 11 | 34 | 0.9978 | 3.51 | 0.56 | 9.4 | |
|
| 1 | 7.8 | 0.88 | 0 | 2.6 | 0.098 | 25 | 67 | 0.9968 | 3.2 | 0.68 | 9.8 | |
|
| 2 | 7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15 | 54 | 0.997 | 3.26 | 0.65 | 9.8 | |
|
|
|
|
|
#### Get your prediction |
|
|
|
```python |
|
labels = model.predict(X[:3]) |
|
# [5, 5, 5] |
|
``` |
|
|
|
#### Eval |
|
|
|
```python |
|
model.score(X, Y) |
|
# 0.6616635397123202 |
|
``` |
|
|
|
### 🍷 Disclaimer |
|
|
|
No red wine was drunk (unfortunately) while training this model 🍷 |
|
|
|
|
