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

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

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

labels = model.predict(X[:3])
# [5, 5, 5]

Eval

model.score(X, Y)
# 0.6616635397123202

🍷 Disclaimer

No red wine was drunk (unfortunately) while training this model 🍷

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