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Update app.py
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app.py
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@@ -28,8 +28,10 @@ def cleaning_features(data):
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# with open('race_onehot_encoder.pkl', 'rb') as enc_file:
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# encoder = pickle.load(enc_file)
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with open('
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with open('scaler.pkl', 'rb') as scaler_file:
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scaler = pickle.load(scaler_file)
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@@ -59,8 +61,8 @@ def cleaning_features(data):
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numeric_cols = ['age', 'educational-num', 'hours-per-week']
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columns_to_encode = ['race','marital-status','relationship']
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data['workclass'] =
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data['occupation'] =
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data['gender'] = data['gender'].map(gender_mapping)
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data['native-country'] = data['native-country'].map(country_mapping)
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data['educational-num'] = data['educational-num'].map(education_num_mapping)
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# with open('race_onehot_encoder.pkl', 'rb') as enc_file:
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# encoder = pickle.load(enc_file)
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with open('label_encoder_work.pkl', 'rb') as le_file:
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le_work = pickle.load(le_file)
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with open('label_encoder_occ.pkl', 'rb') as le_file:
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le_occ = pickle.load(le_file)
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with open('scaler.pkl', 'rb') as scaler_file:
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scaler = pickle.load(scaler_file)
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numeric_cols = ['age', 'educational-num', 'hours-per-week']
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columns_to_encode = ['race','marital-status','relationship']
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data['workclass'] = le_work.transform(data['workclass'])
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data['occupation'] = le_occ.transform(data['occupation'])
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data['gender'] = data['gender'].map(gender_mapping)
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data['native-country'] = data['native-country'].map(country_mapping)
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data['educational-num'] = data['educational-num'].map(education_num_mapping)
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