Update README.md

README.md

@@ -29,9 +29,23 @@ 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([
+                ("loading_missing_value_imputer", SimpleImputer(strategy="mean"), ["loading"]),
+                ("numerical_missing_value_imputer", SimpleImputer(strategy="mean"), list(df.columns[df.dtypes == 'float64'])),
+                ("attribute_0_encoder", OneHotEncoder(categories = "auto"), ["attribute_0"]),
+                ("attribute_1_encoder", OneHotEncoder(categories = "auto"), ["attribute_1"]),
+                ("product_code_encoder", OneHotEncoder(categories = "auto"), ["product_code"])])
 import json
+pipeline = Pipeline([
+    ('transformation', column_transformer_pipeline),
+    ('model', model)
+])
 
 with open("config.json", "r") as f:
     config = json.load(f)
@@ -53,7 +67,7 @@ input_data = np.array([
 if input_data.shape[1] != len(features):
     raise ValueError(f"Input data must have {len(features)} features.")
 
-predicted_classes = model.predict(input_data)
+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)