README.md

metadata

tags:
  - structured-data-classification
dataset:
  - wine-quality
library_name: scikit-learn

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

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`

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"]


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

^^ get your prediction

Eval

model.score(X, Y)
# 0.6616635397123202

🍷 Disclaimer

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