update readme

README.md

@@ -1,3 +1,64 @@
 ---
 license: mit
 ---
+
+---
+tags:
+- tabular-classification
+- sklearn
+datasets:
+- wine-quality
+- imodels/compas-recidivism
+---
+
+
+### Load the data
+
+```python
+from datasets import load_dataset
+import imodels
+import numpy as np
+from sklearn.model_selection import GridSearchCV
+import joblib
+
+dataset = load_dataset("imodels/compas-recidivism")
+df = pd.DataFrame(dataset['train'])
+X_train = df.drop(columns=['is_recid'])
+y_train = df['is_recid'].values
+
+df_test = pd.DataFrame(dataset['test'])
+X_test = df.drop(columns=['is_recid'])
+y_test = df['is_recid'].values
+```
+
+### Load the model
+## 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
+
+
+### Load the model
+
+```python
+from huggingface_hub import hf_hub_url, cached_download
+import joblib
+import pandas as pd
+
+REPO_ID = "imodels/figs-compas-recidivism"
+FILENAME = "figs_model.joblib"
+
+model = joblib.load(cached_download(
+    hf_hub_url(REPO_ID, FILENAME)
+))
+
+# model is a `imodels.FIGSClassifier`
+```
+
+### Make prediction
+
+```
+preds = model.predict(X_test)
+print('accuracy', np.mean(preds==y_test))
+```