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# import library yang dibutuhkan
import streamlit as st
import pandas as pd
import numpy as np
import joblib
# load best model
with open('catb_randcv.pkl','rb') as file_1:
catb_pipe = joblib.load(file_1)
# Construct Data Infer
# define semua fitur/kolom
features = ['Gender','Customer Type','Age','Type of Travel','Class','Flight Distance',\
'Inflight wifi service','Departure/Arrival time convenient','Ease of Online booking',\
'Gate location','Food and drink','Online boarding','Seat comfort','Inflight entertainment',\
'On-board service','Leg room service','Baggage handling','Checkin service','Inflight service',\
'Cleanliness','Departure Delay in Minutes','Arrival Delay in Minutes']
def infer(data_infer):
# predict result with pre-trained model
pred = catb_pipe.predict(data_infer)
return pred
# header deployment
st.header("Predicting Passenger Flight Satisfaction")
# artificial data infer
gender_options = ["Male", "Female"]
gender = st.selectbox("Please input your gender: ", gender_options)
customer_type_options = ['Loyal Customer', 'disloyal Customer']
customer_type = st.selectbox("Which type of customer are you? ", customer_type_options)
type_of_travel_options = ['Personal Travel', 'Business travel']
type_of_travel = st.selectbox("Please input your type of travel: ", type_of_travel_options)
class_options = ['Eco', 'Eco Plus', 'Business']
class_ = st.selectbox("Please input your flight class: ", class_options)
age = st.slider("Please input your age: ",0,100)
flight_distance = st.slider("Please input your flight distance (in Miles): ",0,5000)
wifi_service = st.slider("Please input your wifi experience (0\:lowest 5\:highest) ",0,5)
departure_arrival_conv = st.slider("Please input your departure or arrival time convenience experience (0\:lowest 5\:highest)",0,5)
online_booking_exp = st.slider("Please input your online booking experience (0\:lowest 5\:highest)",0,5)
gate_loc_exp = st.slider("Please input your gate location experience (0\:lowest 5\:highest)",0,5)
food_drinks_exp = st.slider("Please input your food & drinks experience (0\:lowest 5\:highest)",0,5)
online_boarding_exp = st.slider("Please input your online boarding experience (0\:lowest 5\:highest)",0,5)
seat_comfort_exp = st.slider("Please input your seat comfort experience (0\:lowest 5\:highest)",0,5)
inflight_entertainment_exp = st.slider("Please input your inflight entertainment experience (0\:lowest 5\:highest)",0,5)
on_board_svc_exp = st.slider("Please input your on-board service experience (0\:lowest 5\:highest)",0,5)
leg_room_svc_exp = st.slider("Please input your leg room service experience (0\:lowest 5\:highest)",0,5)
baggage_handling_exp = st.slider("Please input your baggage handling experience (0\:lowest 5\:highest)",0,5)
checkin_svc_exp = st.slider("Please input your check-in service experience (0\:lowest 5\:highest)",0,5)
inflight_svc_exp = st.slider("Please input your inflight service experience (0\:lowest 5\:highest)",0,5)
cleanliness = st.slider("How do you rate our cleanliness? (0\:lowest 5\:highest)\: ",0,5)
depart_delay = st.slider("Did you experience delay in your departure? if so please specify (in minutes): ",0,1500)
arriv_delay = st.slider("Did you experience delay in your arrival? if so please specify (in minutes): ",0,1500)
if st.button("Submit"):
D = {
'Gender':gender,
'Customer Type':customer_type,
'Age':age,
'Type of Travel':type_of_travel,
'Class':class_,
'Flight Distance':flight_distance,
'Inflight wifi service':wifi_service,
'Departure/Arrival time convenient':departure_arrival_conv,
'Ease of Online booking':online_booking_exp,
'Gate location':gate_loc_exp,
'Food and drink':food_drinks_exp,
'Online boarding':online_boarding_exp,
'Seat comfort':seat_comfort_exp,
'Inflight entertainment':inflight_entertainment_exp,
'On-board service':on_board_svc_exp,
'Leg room service':leg_room_svc_exp,
'Baggage handling':baggage_handling_exp,
'Checkin service':checkin_svc_exp,
'Inflight service':inflight_svc_exp,
'Cleanliness':cleanliness,
'Departure Delay in Minutes':depart_delay,
'Arrival Delay in Minutes':arriv_delay,
}
# construct data inference dalam dataframe
data_infer = pd.DataFrame(data=D,columns=features,index=[0])
#panggil fungsi inference
pred = infer(data_infer)
st.header(f"Prediction Result: ")
st.write("You are most likely " + pred[0] + " with your flight experience")
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