
## 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 🍷


{"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]}}}
sklearn joblib structured-data-classification has_space region:us
TAGS
#sklearn #joblib #structured-data-classification #has_space #region-us 

Wine Quality classification
---------------------------


### A Simple Example of Scikit-learn Pipeline



> 
> Inspired by URL by Saptashwa Bhattacharyya
> 
> 
> 


### How to use


#### Get sample data from this repo



#### Get your prediction


#### Eval


### Disclaimer


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



### A Simple Example of Scikit-learn Pipeline



> 
> Inspired by URL by Saptashwa Bhattacharyya
> 
> 
> ### How to use #### Get sample data from this repo #### Get your prediction #### Eval ### Disclaimer


No red wine was drunk (unfortunately) while training this model
TAGS
#sklearn #joblib #structured-data-classification #has_space #region-us 
 ### A Simple Example of Scikit-learn Pipeline



> 
> Inspired by URL by Saptashwa Bhattacharyya
> 
> 
> ### How to use #### Get sample data from this repo #### Get your prediction #### Eval ### Disclaimer


No red wine was drunk (unfortunately) while training this model
passage: TAGS
#sklearn #joblib #structured-data-classification #has_space #region-us 
### A Simple Example of Scikit-learn Pipeline



> 
> Inspired by URL by Saptashwa Bhattacharyya
> 
> 
>### How to use#### Get sample data from this repo#### Get your prediction#### Eval### Disclaimer


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