File size: 2,551 Bytes
6bce4ff |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
import streamlit as st
import pandas as pd
import pickle
def app():
st.header('Model Prediction')
st.write("""
Created by Maria Melisa Gunawan
Airline Passenger Satisfaction Prediction
""")
@st.cache_data
def fetch_data():
# Ganti path file CSV sesuai dengan lokasi file Anda
df = pd.read_csv('airline_passenger_satisfaction.csv')
return df
df = fetch_data()
st.header('User Input Features')
def user_input():
online_boarding = st.slider("Online Boarding", 0, 5, 3)
type_of_travel = st.slider("Type of Travel", 0, 1)
inflight_entertainment = st.slider("Inflight Entertainment", 0, 5, 3)
customer_class = st.slider("Customer Class", 0, 1, 2)
seat_comfort = st.slider("Seat Comfort", 0, 5, 3)
onboard_service = st.slider("Onboard Service", 0, 5, 3)
leg_room_service = st.slider("Leg Room Service", 0, 5, 3)
cleanliness = st.slider("Cleanliness", 0, 5, 3)
inflight_wifi_service = st.slider("Inflight Wifi Service", 0, 5, 3)
baggage_handling = st.slider("Baggage Handling", 0, 5, 3)
inflight_service = st.slider("Inflight Service", 0, 5, 3)
flight_distance = st.number_input("Flight Distance", min_value=0)
checkin_service = st.slider("Checkin Service", 0, 5, 3)
data = {
'online_boarding': [online_boarding],
'type_of_travel': [type_of_travel],
'inflight_entertainment': [inflight_entertainment],
'customer_class' : [customer_class],
'seat_comfort': [seat_comfort],
'onboard_service': [onboard_service],
'leg_room_service': [leg_room_service],
'cleanliness': [cleanliness],
'inflight_wifi_service': [inflight_wifi_service],
'baggage_handling': [baggage_handling],
'inflight_service': [inflight_service],
'flight_distance': [flight_distance],
'checkin_service': [checkin_service]
}
features = pd.DataFrame(data)
return features
input_df = user_input()
st.subheader('User Input')
st.write(input_df)
# Load trained model
filename = 'best_model.pkl'
loaded_model = pickle.load(open(filename, 'rb'))
# Predict
prediction = loaded_model.predict(input_df)
if prediction == 1:
result = 'Satisfied'
else:
result = 'Dissatisfied'
st.write('Predicted Passenger Satisfaction:')
st.write(result)
if __name__ == '__main__':
app()
|