Upload README.md

README.md

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+---
+tags:
+- structured-data-classification
+- sklearn
+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 🍷
+