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.gitattributes

@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+airline_passenger_satisfaction.csv filter=lfs diff=lfs merge=lfs -text

airline_passenger_satisfaction.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b92fa53b0aa2f6ebb7e7785e7122585794dfb8f6178565f4f03255ddb5367831
+size 14339544

app.py

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+import eda as eda
+import prediction
+import streamlit as st
+PAGES = {
+    "Exploratory Data Analysis": eda,
+    "Model Prediction": prediction
+}
+st.sidebar.title('Navigation')
+selection = st.sidebar.radio("Go to", list(PAGES.keys()))
+page = PAGES[selection]
+page.app()

best_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:acde1554f9bcde5bdb1ade2e3a39575ffee006b0ca72ac1da42ee5d8baf6f368
+size 346280

eda.py

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+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+def app():
+    st.header('Model Deployment')
+    st.write("""
+    Created by Maria Melisa Gunawan""")
+    st.subheader("""Airline Passenger Satisfaction Exploratory Data Analysis
+    """)
+
+    @st.cache_data
+    def fetch_data():
+        df = pd.read_csv('airline_passenger_satisfaction.csv')
+        return df
+
+    df = fetch_data()
+    df.drop_duplicates(inplace=True)
+    st.write(df)
+
+    fig, ax = plt.subplots()
+    df['Gender'].value_counts().plot(kind='bar', ax=ax)
+    plt.tight_layout()
+    st.header('Distribusi Jenis Kelamin Pelanggan')
+    st.pyplot(fig)
+    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%")
+
+    for feature in ['inflight_entertainment', 'seat_comfort', 'onboard_service', 
+                'leg_room_service', 'inflight_wifi_service', 'baggage_handling', 
+                'inflight_service', 'checkin_service', 'online_boarding', 
+                'cleanliness']:
+        fig, ax = plt.subplots()
+        df[feature].value_counts().plot(kind='bar', ax=ax)
+        plt.tight_layout()
+        st.header(f'{feature.capitalize()} Counts')
+        st.pyplot(fig)
+        st.write(""" Berdasarkan data diatas, dapat disimpulkan bahwa rata-rata service yang diberikan memiliki tingkat kepuasan yang cukup tinggi
+        """)
+
+    fig, ax = plt.subplots()
+    df['customer_type'].value_counts().plot(kind='bar', ax=ax)
+    plt.tight_layout()
+    st.header('Jumlah Pelanggan Berdasarkan Tipe Pelanggan')
+    st.pyplot(fig)
+    st.write("Loyal customer jauh lebih tinggi dibandingkan dengan disloyal customer, yang berarti banyak pelanggan yang loyal.")
+
+    fig, ax = plt.subplots()
+    df['age'].plot(kind='hist', ax=ax, bins=20, edgecolor='black', alpha=0.7)
+    plt.tight_layout()
+    st.header('Distribusi Usia Pelanggan')
+    st.pyplot(fig)
+    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.")
+
+    fig, ax = plt.subplots()
+    df['type_of_travel'].value_counts().plot(kind='bar', ax=ax)
+    plt.tight_layout()
+    st.header('Jumlah Pelanggan Berdasarkan Jenis Perjalanan')
+    st.pyplot(fig)
+    st.write("Mayoritas pelanggan melakukan perjalanan business travel dibandingkan dengan personal travel.")
+
+    fig, ax = plt.subplots()
+    df['customer_class'].value_counts().plot(kind='bar', ax=ax)
+    plt.tight_layout()
+    st.header('Jumlah Pelanggan Berdasarkan Kelas Perjalanan')
+    st.pyplot(fig)
+    st.write("Mayoritas pelanggan berada di kelas business diikuti dengan kelas eco dan paling sedikit pelanggan yang menaiki kelas eco plus.")
+
+
+    fig, ax = plt.subplots()
+    df['flight_distance'].plot(kind='hist', ax=ax, bins=20, edgecolor='black', alpha=0.7)
+    plt.tight_layout()
+    st.header('Distribusi Jarak Penerbangan')
+    st.pyplot(fig)
+    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.")
+
+
+    st.header('KDE of Flight Distance by Satisfaction')
+    fig = sns.displot(data=df, x="flight_distance", kind='kde', hue='satisfaction')
+    st.pyplot(fig)
+    st.write("Lebih banyak orang yang merasa puas pada perjalanan yang jauh daripada perjalanan yang pendek.")
+
+
+    fig, ax = plt.subplots()
+    df['satisfaction'].value_counts().plot(kind='bar', ax=ax)
+    plt.tight_layout()
+    st.header('Jumlah Pelanggan Berdasarkan Kepuasan')
+    st.pyplot(fig)
+    st.write("Lebih banyak pelanggan yang merasa netral/tidak puas daripada yang merasa puas.")
+
+
+    st.header('KDE of Age by Satisfaction')
+    fig = sns.displot(data=df, x="age", kind='kde', hue='satisfaction')
+    st.pyplot(fig)
+    st.write("Orang dengan umur 40 keatas atau yang lebih tua cenderung lebih puas dari umur yang masih muda.")
+
+    st.header('Skewness of Flight Distance')
+    skew = round(df["flight_distance"].skew(), 2)
+    st.write("Skewness:", skew)
+    fig, ax = plt.subplots()
+    sns.histplot(df["flight_distance"], bins=60, ax=ax)
+    plt.tight_layout()
+    st.pyplot(fig)
+    st.write(" Dari gambar diatas menunjukkan flight distance memiliki skewness positif.")
+
+
+    st.header('Box Plot of Flight Distance')
+    plt.figure(figsize=(8, 6))
+    sns.boxplot(x=df["flight_distance"])
+    plt.title('Box Plot Jarak Penerbangan')
+    plt.xlabel('Jarak Penerbangan')
+    plt.grid(True)
+    st.pyplot(plt)
+    st.write("Tidak ada outliers yang besar pada flight distance.")
+
+if __name__ == '__main__':
+    app()

prediction.py

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+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()

requirements.txt

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+streamlit
+pandas
+seaborn
+matplotlib
+numpy
+scikit-learn == 1.2.2