wlaminack/testingmodel

Wine Quality classification clone for testing

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 = "wlaminack/testingmodel"
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 🍷