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Create README.md

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+ ---
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+ tags:
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+ - structured-data-classification
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+ dataset:
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+ - wine-quality
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+ library_name: scikit-learn
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+ ---
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+
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+ ## Wine Quality classification
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+
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+ ### A Simple Example of Scikit-learn Pipeline
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+
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+ > Inspired by https://towardsdatascience.com/a-simple-example-of-pipeline-in-machine-learning-with-scikit-learn-e726ffbb6976
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+
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+
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+ ### How to use
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+
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+ ```python
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+ from huggingface_hub import hf_hub_url, cached_download
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+ import joblib
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+ import pandas as pd
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+
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+ REPO_ID = "julien-c/wine-quality"
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+ FILENAME = "sklearn_model.joblib"
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+
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+
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+ model = joblib.load(cached_download(
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+ hf_hub_url(REPO_ID, FILENAME)
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+ ))
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+
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+ # model is a `sklearn.pipeline.Pipeline`
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+
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+ data_file = cached_download(
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+ hf_hub_url(REPO_ID, "winequality-red.csv")
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+ )
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+ winedf = pd.read_csv(data_file, sep=";")
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+
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+
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+ X = winedf.drop(["quality"], axis=1)
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+ Y = winedf["quality"]
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+
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+
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+ labels = model.predict(X[:3])
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+ ```
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+
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+ ^^ get your prediction
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+
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+ #### Eval
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+
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+ ```python
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+ model.score(X, Y)
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+ # 0.6616635397123202
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+ ```
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+
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+ ### 🍷 Disclaimer
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+
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+ No red wine was drunk (unfortunately) while training this model 🍷
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+