Update README.md

README.md

@@ -24,50 +24,4 @@ widget:
     - 0
     - 2
 pipeline_tag: tabular-classification
----
-
-## Usage
-```python
-import joblib
-from sklearn.impute import SimpleImputer
-from sklearn.compose import ColumnTransformer
-from sklearn.pipeline import Pipeline
-
-model = joblib.load("iris_svm.joblib")
-
-column_transformer_pipeline = ColumnTransformer([
-                ("SepalLengthCm", SimpleImputer(strategy="mean"), ["SepalLengthCm"]),
-                ("SepalWidthCm", SimpleImputer(strategy="mean"), ["SepalWidthCm"]),
-                ("PetalLengthCm", SimpleImputer(strategy="mean"), ["PetalLengthCm"]),
-                ("PetalWidthCm", SimpleImputer(strategy="mean"), ["PetalWidthCm"])])
-import json
-pipeline = Pipeline([
-    ('transformation', column_transformer_pipeline),
-    ('model', model)
-])
-
-with open("config.json", "r") as f:
-    config = json.load(f)
-
-features = config["features"]
-target = config["targets"][0]
-target_mapping = config["target_mapping"]
-
-import numpy as np
-
-# example input data
-input_data = np.array([
-    [5.1, 3.5, 1.4, 0.2],
-    [4.9, 3.0, 1.4, 0.2],
-    [6.2, 3.4, 5.4, 2.3]
-])
-
-# make sure the input data has the correct shape
-if input_data.shape[1] != len(features):
-    raise ValueError(f"Input data must have {len(features)} features.")
-
-predicted_classes = pipeline.predict(input_data)
-predicted_class_names = [list(target_mapping.keys())[list(target_mapping.values()).index(predicted_class)] for predicted_class in predicted_classes]
-
-print("Predicted classes:", predicted_class_names)
-```
+---