Commit
•
6bce4ff
1
Parent(s):
b6fcc85
Upload 6 files
Browse files- .gitattributes +1 -0
- airline_passenger_satisfaction.csv +3 -0
- app.py +11 -0
- best_model.pkl +3 -0
- eda.py +117 -0
- prediction.py +77 -0
- requirements.txt +6 -0
.gitattributes
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@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpeg filter=lfs diff=lfs merge=lfs -text
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*.webp filter=lfs diff=lfs merge=lfs -text
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airline_passenger_satisfaction.csv filter=lfs diff=lfs merge=lfs -text
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airline_passenger_satisfaction.csv
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version https://git-lfs.github.com/spec/v1
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oid sha256:b92fa53b0aa2f6ebb7e7785e7122585794dfb8f6178565f4f03255ddb5367831
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size 14339544
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app.py
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import eda as eda
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import prediction
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import streamlit as st
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PAGES = {
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"Exploratory Data Analysis": eda,
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"Model Prediction": prediction
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}
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st.sidebar.title('Navigation')
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selection = st.sidebar.radio("Go to", list(PAGES.keys()))
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page = PAGES[selection]
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page.app()
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best_model.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:acde1554f9bcde5bdb1ade2e3a39575ffee006b0ca72ac1da42ee5d8baf6f368
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size 346280
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eda.py
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import streamlit as st
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import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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def app():
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st.header('Model Deployment')
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st.write("""
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Created by Maria Melisa Gunawan""")
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st.subheader("""Airline Passenger Satisfaction Exploratory Data Analysis
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""")
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@st.cache_data
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def fetch_data():
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df = pd.read_csv('airline_passenger_satisfaction.csv')
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return df
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df = fetch_data()
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df.drop_duplicates(inplace=True)
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st.write(df)
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fig, ax = plt.subplots()
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df['Gender'].value_counts().plot(kind='bar', ax=ax)
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plt.tight_layout()
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st.header('Distribusi Jenis Kelamin Pelanggan')
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st.pyplot(fig)
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st.write("Berdasarkan jenis kelamin, dengan angka 0 menunjukkan Female dan angka 1 menunjukkan Male, didapatkan bahwa gender Female dan Male memiliki angka yang hampir sama yaitu Female 50.7% dan Male 49.3%")
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for feature in ['inflight_entertainment', 'seat_comfort', 'onboard_service',
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'leg_room_service', 'inflight_wifi_service', 'baggage_handling',
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'inflight_service', 'checkin_service', 'online_boarding',
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'cleanliness']:
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fig, ax = plt.subplots()
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df[feature].value_counts().plot(kind='bar', ax=ax)
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plt.tight_layout()
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st.header(f'{feature.capitalize()} Counts')
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st.pyplot(fig)
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st.write(""" Berdasarkan data diatas, dapat disimpulkan bahwa rata-rata service yang diberikan memiliki tingkat kepuasan yang cukup tinggi
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""")
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fig, ax = plt.subplots()
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df['customer_type'].value_counts().plot(kind='bar', ax=ax)
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plt.tight_layout()
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st.header('Jumlah Pelanggan Berdasarkan Tipe Pelanggan')
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st.pyplot(fig)
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st.write("Loyal customer jauh lebih tinggi dibandingkan dengan disloyal customer, yang berarti banyak pelanggan yang loyal.")
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fig, ax = plt.subplots()
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df['age'].plot(kind='hist', ax=ax, bins=20, edgecolor='black', alpha=0.7)
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plt.tight_layout()
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st.header('Distribusi Usia Pelanggan')
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st.pyplot(fig)
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st.write("Mayoritas pelanggan berumur dari 10 hingga 85 tahun dan pelanggan terbanyak berada di usia kisaran 20 hingga 60 tahun dengan pelanggan yang paling banyak pada usia 40 tahun.")
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fig, ax = plt.subplots()
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df['type_of_travel'].value_counts().plot(kind='bar', ax=ax)
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plt.tight_layout()
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st.header('Jumlah Pelanggan Berdasarkan Jenis Perjalanan')
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st.pyplot(fig)
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st.write("Mayoritas pelanggan melakukan perjalanan business travel dibandingkan dengan personal travel.")
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fig, ax = plt.subplots()
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df['customer_class'].value_counts().plot(kind='bar', ax=ax)
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plt.tight_layout()
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st.header('Jumlah Pelanggan Berdasarkan Kelas Perjalanan')
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st.pyplot(fig)
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st.write("Mayoritas pelanggan berada di kelas business diikuti dengan kelas eco dan paling sedikit pelanggan yang menaiki kelas eco plus.")
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fig, ax = plt.subplots()
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df['flight_distance'].plot(kind='hist', ax=ax, bins=20, edgecolor='black', alpha=0.7)
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plt.tight_layout()
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st.header('Distribusi Jarak Penerbangan')
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st.pyplot(fig)
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st.write("Mayoritas pelanggan berumur dari 10 hingga 85 tahun dan pelanggan terbanyak berada di usia kisaran 20 hingga 60 tahun dengan pelanggan yang paling banyak pada usia 40 tahun.")
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st.header('KDE of Flight Distance by Satisfaction')
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fig = sns.displot(data=df, x="flight_distance", kind='kde', hue='satisfaction')
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st.pyplot(fig)
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st.write("Lebih banyak orang yang merasa puas pada perjalanan yang jauh daripada perjalanan yang pendek.")
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fig, ax = plt.subplots()
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df['satisfaction'].value_counts().plot(kind='bar', ax=ax)
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plt.tight_layout()
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st.header('Jumlah Pelanggan Berdasarkan Kepuasan')
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st.pyplot(fig)
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st.write("Lebih banyak pelanggan yang merasa netral/tidak puas daripada yang merasa puas.")
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st.header('KDE of Age by Satisfaction')
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fig = sns.displot(data=df, x="age", kind='kde', hue='satisfaction')
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st.pyplot(fig)
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st.write("Orang dengan umur 40 keatas atau yang lebih tua cenderung lebih puas dari umur yang masih muda.")
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st.header('Skewness of Flight Distance')
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skew = round(df["flight_distance"].skew(), 2)
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st.write("Skewness:", skew)
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fig, ax = plt.subplots()
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sns.histplot(df["flight_distance"], bins=60, ax=ax)
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plt.tight_layout()
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st.pyplot(fig)
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st.write(" Dari gambar diatas menunjukkan flight distance memiliki skewness positif.")
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st.header('Box Plot of Flight Distance')
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plt.figure(figsize=(8, 6))
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sns.boxplot(x=df["flight_distance"])
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plt.title('Box Plot Jarak Penerbangan')
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plt.xlabel('Jarak Penerbangan')
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plt.grid(True)
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st.pyplot(plt)
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st.write("Tidak ada outliers yang besar pada flight distance.")
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if __name__ == '__main__':
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app()
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prediction.py
ADDED
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import streamlit as st
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import pandas as pd
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import pickle
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def app():
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st.header('Model Prediction')
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st.write("""
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Created by Maria Melisa Gunawan
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Airline Passenger Satisfaction Prediction
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""")
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@st.cache_data
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def fetch_data():
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# Ganti path file CSV sesuai dengan lokasi file Anda
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df = pd.read_csv('airline_passenger_satisfaction.csv')
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return df
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df = fetch_data()
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st.header('User Input Features')
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def user_input():
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online_boarding = st.slider("Online Boarding", 0, 5, 3)
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type_of_travel = st.slider("Type of Travel", 0, 1)
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inflight_entertainment = st.slider("Inflight Entertainment", 0, 5, 3)
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customer_class = st.slider("Customer Class", 0, 1, 2)
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seat_comfort = st.slider("Seat Comfort", 0, 5, 3)
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onboard_service = st.slider("Onboard Service", 0, 5, 3)
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leg_room_service = st.slider("Leg Room Service", 0, 5, 3)
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cleanliness = st.slider("Cleanliness", 0, 5, 3)
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inflight_wifi_service = st.slider("Inflight Wifi Service", 0, 5, 3)
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baggage_handling = st.slider("Baggage Handling", 0, 5, 3)
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inflight_service = st.slider("Inflight Service", 0, 5, 3)
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flight_distance = st.number_input("Flight Distance", min_value=0)
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checkin_service = st.slider("Checkin Service", 0, 5, 3)
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data = {
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'online_boarding': [online_boarding],
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'type_of_travel': [type_of_travel],
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'inflight_entertainment': [inflight_entertainment],
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'customer_class' : [customer_class],
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'seat_comfort': [seat_comfort],
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'onboard_service': [onboard_service],
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'leg_room_service': [leg_room_service],
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'cleanliness': [cleanliness],
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'inflight_wifi_service': [inflight_wifi_service],
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'baggage_handling': [baggage_handling],
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'inflight_service': [inflight_service],
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'flight_distance': [flight_distance],
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'checkin_service': [checkin_service]
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}
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features = pd.DataFrame(data)
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return features
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input_df = user_input()
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st.subheader('User Input')
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st.write(input_df)
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# Load trained model
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filename = 'best_model.pkl'
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loaded_model = pickle.load(open(filename, 'rb'))
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# Predict
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prediction = loaded_model.predict(input_df)
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if prediction == 1:
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result = 'Satisfied'
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else:
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result = 'Dissatisfied'
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st.write('Predicted Passenger Satisfaction:')
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st.write(result)
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if __name__ == '__main__':
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app()
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requirements.txt
ADDED
@@ -0,0 +1,6 @@
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streamlit
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pandas
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seaborn
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matplotlib
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numpy
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scikit-learn == 1.2.2
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