---
datasets:
- scikit-learn/iris
widget:
  structuredData:
    SepalLengthCm:
    - 5.1
    - 4.9
    - 6.2
    SepalWidthCm:
    - 3.5
    - 3
    - 3.4
    PetalLengthCm:
    - 1.4
    - 1.4
    - 5.4
    PetalWidthCm:
    - 0.2
    - 0.2
    - 2.3
    target:
    - 0
    - 0
    - 2
tags:
- tabular-classification
---
### How to use

```python
from huggingface_hub import hf_hub_url, cached_download
import joblib
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

REPO_ID = "d2i-pti-iu/test_svc_model"
FILENAME = "iris_svm.joblib"
model = joblib.load(cached_download(hf_hub_url(REPO_ID, FILENAME)))
iris = load_iris()

X = iris.data[:3]
# model is a `sklearn.pipeline.Pipeline`
labels = model.predict(X)
```