Upload 8 files

app.py

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+import pandas as pd 
+import streamlit as st
+import eda
+import prediction
+
+page = st.sidebar.selectbox("choose page: ", ("Home page","Data exploration","Data Prediction"))
+
+if page == "Home page":
+    st.title("Default Payment Prediction")
+    st.write("Name      :Dicky Gabriel")
+    st.write("Batch     :SBY-002")
+    st.write("Objective : Predict Default Payment")
+elif page == "Data exploration":
+    eda.run()
+else:
+    prediction.run()

best_pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d9acb821bd4b7e6e41b2e2acb705449faadef9229d54078f5ac2a49ecd9fe0b2
+size 72927667

eda.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from matplotlib.ticker import MultipleLocator
+
+def run():
+    st.title("Flight Passenger Satisfaction Prediction")
+
+    st.subheader("Analysis Data for Flight Passenger")
+
+    st.write("This page made by Dicky Gabriel")
+    st.markdown("---")
+
+    df = pd.read_csv("Fligh_satification.csv")
+    st.dataframe(df)
+    df.columns = df.columns.str.lower()
+
+    st.write("## Passenger Satisfaction")
+    sns.set_style("whitegrid")
+    fig_pie, ax_pie = plt.subplots(figsize=(6, 6))
+    ax_pie.pie(df.satisfaction.value_counts(), labels=["Neutral or dissatisfied", "Satisfied"], autopct='%1.1f%%')
+    ax_pie.set_title('Perbandingan kolom satisfied dan neutral or disastified')
+    st.pyplot(fig_pie)
+
+    st.write("## Age Histogram")
+    fig_hist, ax_hist = plt.subplots(figsize=(20, 20))
+    ax_hist.minorticks_on()
+    ax_hist.xaxis.set_minor_locator(MultipleLocator(5))
+    ax_hist.yaxis.set_minor_locator(MultipleLocator(100))
+    ax_hist.set_title('Ages Histogram', size=20, fontweight='bold', y=1.04)
+
+    sns.histplot(x='age', data=df, edgecolor='black', kde=True, line_kws={'lw': 1, 'linestyle': '--'}, ax=ax_hist)
+
+    ax_hist.set_xlabel('Age', size=15)
+    ax_hist.set_ylabel('Count', size=15)
+    st.pyplot(fig_hist)
+
+    st.write("## Scatterplot of age and satisfaction")
+    fig_scatter, ax_scatter = plt.subplots(figsize=(20, 20))
+    ax_scatter.minorticks_on()
+    ax_scatter.xaxis.set_minor_locator(MultipleLocator(5))
+    ax_scatter.yaxis.set_minor_locator(MultipleLocator(100))
+    ax_scatter.set_title('Ages Histogram with Satisfaction', size=20, fontweight='bold', y=1.04)
+
+    sns.histplot(x='age', data=df, edgecolor='black', hue="satisfaction", kde=True,
+                 line_kws={'lw': 1, 'linestyle': '--'}, ax=ax_scatter)
+
+    ax_scatter.set_xlabel('Age', size=15)
+    ax_scatter.set_ylabel('Count', size=15)
+    st.pyplot(fig_scatter)
+
+    st.write("## plane type")
+    fig_plane_type = plt.figure(figsize=(10, 5), dpi=200)
+
+    ax_plane_type = sns.countplot(x='type of travel', hue='satisfaction', data=df)
+    ax_plane_type.set_title('type of travel', size=15)
+    ax_plane_type.legend(fontsize='10')
+    plt.xlabel('')
+    plt.ylabel('Count', size=15)
+
+    st.write("## plane type")
+    fig_plane_type = plt.figure(figsize=(10, 5), dpi=200)
+
+    ax_plane_type = sns.countplot(x='type of travel', hue='satisfaction', data=df)
+    ax_plane_type.set_title('type of travel', size=15)
+    ax_plane_type.legend(fontsize='10')
+    plt.xlabel('')
+    plt.ylabel('Count', size=15)
+
+
+    for p in ax_plane_type.patches:
+        ax_plane_type.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),
+                                ha='center', va='center', fontsize=8, color='black', xytext=(0, 5),
+                                textcoords='offset points')
+
+    st.pyplot(fig_plane_type)
+
+    st.write("## Class type")
+    fig_class_type = plt.figure(figsize=(10,5),dpi=200)
+
+    ax=sns.countplot(x='class',hue='satisfaction',data=df)
+    ax.set_title('class',size=15)
+    ax.legend(fontsize='10')
+    plt.xlabel('')
+    plt.ylabel('Count',size=15)
+
+    for p in ax.patches:
+        ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),
+                    ha='center', va='center', fontsize=8, color='black', xytext=(0, 5),
+                    textcoords='offset points')
+        
+    plt.show()
+
+    st.pyplot(fig_class_type)
+
+if __name__ == "__main__":
+    run()

list_cat_nominal_columns.txt

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+["type of travel", "generation"]

list_cat_ordinal_columns.txt

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+["class", "inflight wifi service", "ease of online booking", "food and drink", "online boarding", "seat comfort", "inflight entertainment", "on-board service", "leg room service", "baggage handling", "checkin service", "inflight service", "cleanliness"]

list_num_columns.txt

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+["flight distance"]

prediction.py

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+import streamlit as st
+import pickle
+import json
+import pandas as pd
+import numpy as np
+
+with open("list_num_columns.txt", 'r') as file_1:
+    list_num_skew_columns = json.load(file_1)
+
+with open("list_cat_nominal_columns.txt", "r") as file_2:
+    nom_col_skew = json.load(file_2)
+
+with open("list_cat_ordinal_columns.txt", "r") as file_3:
+    ord_col_skew = json.load(file_3)
+
+with open("best_pipeline.pkl", "rb") as file_4:
+    best_pipeline = pickle.load(file_4)
+
+def run():
+    # create form 
+    with st.form("form"):
+        age = st.number_input("age",
+                            min_value= 5,
+                            max_value= 90,
+                            value=30,
+                            step=2) 
+        flight_distance = st.number_input("flight distance",
+                            min_value= 31,
+                            max_value= 5000,
+                            value=850,
+                            step=10) 
+        departure_delay_in_minutes = st.number_input("departure delay in minutes",
+                            min_value= 0,
+                            max_value= 1600,
+                            value=200,
+                            step=10) 
+        arrival_delay_in_minutes = st.number_input("arrival delay in minutes",
+                            min_value= 0,
+                            max_value= 1600,
+                            value=200,
+                            step=10) 
+
+        st.markdown("---")
+        gender = st.radio("gender",("Male","Female"),index= 0)
+        customer_type = st.radio("customer type",("Loyal customer","disloyal customer"),index= 0)
+        type_of_travel = st.radio("type of travel",('Personal Travel', 'Business travel'),index= 0)
+        class_flight = st.radio("class flight",('Eco Plus', 'Business', 'Eco'),index= 0)
+        
+        st.markdown("---")
+        inflight_wifi_service = st.radio("inflight_wifi_service",(0,1,2,3,4,5),index= 0)
+        departure_arrival_time_convenient = st.radio("departure/arrival_time_convenient",(0,1,2,3,4,5),index= 0)
+        ease_of_online_booking = st.radio("ease_of_online_booking",(0,1,2,3,4,5),index= 0)
+        gate_location = st.radio("gate_location",(0,1,2,3,4,5),index= 0)
+        food_and_drink = st.radio("food_and_drink",(0,1,2,3,4,5),index= 0)
+        online_boarding = st.radio("online_boarding",(0,1,2,3,4,5),index= 0)
+        seat_comfort = st.radio("seat_comfort",(0,1,2,3,4,5),index= 0)
+        inflight_entertainment = st.radio("inflight_entertainment",(0,1,2,3,4,5),index= 0)
+        on_board_service = st.radio("on_board_service",(0,1,2,3,4,5),index= 0)
+        leg_room_service = st.radio("leg_room_service",(0,1,2,3,4,5),index= 0)
+        baggage_handling = st.radio("baggage_handling",(0,1,2,3,4,5),index= 0)
+        checkin_service = st.radio("checkin_service",(0,1,2,3,4,5),index= 0)
+        inflight_service = st.radio("inflight_service",(0,1,2,3,4,5),index= 0)
+        cleanliness = st.radio("cleanliness",(0,1,2,3,4,5),index= 0)
+        st.markdown("---")
+
+        submitted = st.form_submit_button("predict")
+
+    data_inf = {
+        "gender" : gender,
+        "customer type" : customer_type,
+        "age" : age,
+        "type of travel" : type_of_travel,
+        "class" : class_flight,
+        "inflight wifi service" : inflight_wifi_service, 
+        "departure/arrival time convenient" : departure_arrival_time_convenient, 
+        "ease of online booking" : ease_of_online_booking, 
+        "gate location" : gate_location, 
+        "food and drink" : food_and_drink, 
+        "online boarding" : online_boarding,
+        "seat comfort" : seat_comfort, 
+        "inflight entertainment" : inflight_entertainment,
+        "on-board service" : on_board_service, 
+        "leg room service" : leg_room_service, 
+        "baggage handling" : baggage_handling, 
+        "checkin service" : checkin_service, 
+        "inflight service" : inflight_service, 
+        "cleanliness" : cleanliness,
+        "flight distance" : flight_distance,
+        "departure delay in minutes" : departure_delay_in_minutes,
+        "arrival delay in minutes" : arrival_delay_in_minutes
+    }
+
+    data_inf = pd.DataFrame([data_inf])
+    st.dataframe(data_inf)
+
+
+    age_category = []
+    for x in data_inf["age"]:
+        if 6 <= x <= 21:
+            age_category.append('Generation z') 
+        elif 22 <= x <= 36:
+            age_category.append('Millennials')
+        elif 37 <= x <= 52:
+            age_category.append('Generation X')
+        elif 53 <= x <= 73:
+            age_category.append('Baby Boomers')
+        else:
+            age_category.append('Silent Generation')
+
+    data_inf["generation"] = age_category
+
+    if submitted:
+        data_inf_num_skew = data_inf[list_num_skew_columns]
+        data_inf_cat_nom = data_inf[nom_col_skew]
+        data_inf_cat_ord = data_inf[ord_col_skew]
+        y_predict_inf = best_pipeline.predict(data_inf)
+
+        st.write("# Satisfaction: ", str(y_predict_inf[0]))
+
+if __name__=="__main__":
+    run()

requirements.txt

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+streamlit
+pandas
+numpy
+matplotlib
+seaborn
+plotly
+scikit-learn