readme: ---
  tags:
  - structured-data-classification
  - sklearn
  - joblib
  dataset:
  - wine-quality
  widget:
    structuredData:
      fixed_acidity:
        - 7.4
        - 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 🍷
  
  