def prediction(data): df = DataFrame(data, index=[0]) df.CNDTN = df.apply(applyCNDTN, axis=1) df.GRADE = df.apply(applyGRADE, axis=1) import category_encoders as ce df_temp = encoder_features.transform(df.drop( ['GRADE', 'CNDTN'], axis=1)).drop([ 'HEAT_0', 'AC_0', 'STYLE_0', 'STRUCT_0', 'EXTWALL_0', 'QUALIFIED_0', 'ROOF_0', 'INTWALL_0', 'ASSESSMENT_NBHD_0', 'WARD_0', 'QUADRANT_0' ], axis=1) df = pd.concat([df_temp, df[['GRADE', 'CNDTN']]], axis=1) df_scaled = scaller_features.transform(df[['YR_RMDL', 'GBA', 'LANDAREA']]) df_scaled = pd.DataFrame(df_scaled, columns=['YR_RMDL', 'GBA', 'LANDAREA']) df = pd.concat( [df.drop(['YR_RMDL', 'GBA', 'LANDAREA'], axis=1), df_scaled], axis=1) df = df[[ 'BATHRM', 'HF_BATHRM', 'HEAT_1', 'HEAT_2', 'HEAT_3', 'HEAT_4', 'AC_1', 'NUM_UNITS', 'ROOMS', 'BEDRM', 'QUALIFIED_1', 'BLDG_NUM', 'STYLE_1', 'STYLE_2', 'STYLE_3', 'STYLE_4', 'STYLE_5', 'STRUCT_1', 'STRUCT_2', 'STRUCT_3', 'EXTWALL_1', 'EXTWALL_2', 'EXTWALL_3', 'EXTWALL_4', 'EXTWALL_5', 'ROOF_1', 'ROOF_2', 'ROOF_3', 'ROOF_4', 'INTWALL_1', 'INTWALL_2', 'INTWALL_3', 'INTWALL_4', 'KITCHENS', 'FIREPLACES', 'ASSESSMENT_NBHD_1', 'ASSESSMENT_NBHD_2', 'ASSESSMENT_NBHD_3', 'ASSESSMENT_NBHD_4', 'ASSESSMENT_NBHD_5', 'ASSESSMENT_NBHD_6', 'WARD_1', 'WARD_2', 'WARD_3', 'QUADRANT_1', 'QUADRANT_2', 'GRADE', 'CNDTN', 'YR_RMDL', 'GBA', 'LANDAREA' ]] hasil = model.predict(df) hasil_fix = int(hasil[0]) return hasil_fix