.

app.py

@@ -5,7 +5,12 @@ import pandas as pd
 import category_encoders as ce
 healthcare_stroke_data = pd.read_csv("healthcare-dataset-stroke-data.csv")
 
-s = setup(healthcare_stroke_data, target = 'stroke', normalize = True, normalize_method = 'zscore', transformation=True, fix_imbalance = True, session_id=123, fold = 60, remove_outliers= True, outliers_threshold = 0.05, remove_multicollinearity=True, multicollinearity_threshold = 0.9) 
+encoder= ce.OrdinalEncoder(cols=['gender'],return_df=True, mapping=[{'col':'gender', 'mapping':{0: 1, 1: 2,'Other': 3}}])
+healthcare_stroke_data['gender'] = encoder.fit_transform(healthcare_stroke_data['gender'])
+encoder= ce.OrdinalEncoder(cols=['work_type'],return_df=True, mapping=[{'col':'work_type', 'mapping':{0: 1, 1: 2, 'children': 3, '2': 4, 'Never_worked': 5}}])
+healthcare_stroke_data['work_type'] = encoder.fit_transform(healthcare_stroke_data['work_type'])
+
+exp_clf101 = setup(data = healthcare_stroke_data, target = 'stroke', session_id=123)
 
 best = compare_models(sort = 'AUC', n_select = 15)
 compare_model_results = pull()