From a46d5eed29b7c194d5a6ac9d8c204e8cb610cbd3 Mon Sep 17 00:00:00 2001
From: "github-classroom[bot]"
 <66690702+github-classroom[bot]@users.noreply.github.com>
Date: Mon, 18 Oct 2021 04:53:39 +0000
Subject: [PATCH 1/2] Setting up GitHub Classroom Feedback


From c0b3e503429ddb14bbcc6cba72c39ffb627bc8e2 Mon Sep 17 00:00:00 2001
From: Jovan-Li <86348354+Jovan-Li@users.noreply.github.com>
Date: Tue, 19 Oct 2021 23:04:00 -0400
Subject: [PATCH 2/2] Add files via upload

---
 Cleaned_Satisfaction.csv | 50001 +++++++++++++++++++++++++++++++++++++
 app.py                   |   174 +
 classifier.pkl           |   Bin 0 -> 1339576 bytes
 zhouyanl - Hw2Report.pdf |   Bin 0 -> 1066115 bytes
 4 files changed, 50175 insertions(+)
 create mode 100644 Cleaned_Satisfaction.csv
 create mode 100644 app.py
 create mode 100644 classifier.pkl
 create mode 100644 zhouyanl - Hw2Report.pdf

diff --git a/Cleaned_Satisfaction.csv b/Cleaned_Satisfaction.csv
new file mode 100644
index 0000000..1ff167c
--- /dev/null
+++ b/Cleaned_Satisfaction.csv
@@ -0,0 +1,50001 @@
+,Customer Type,Class,Flight Distance,Seat Comfort,Inflight Wifi Service,Inflight Entertainment,Cleanliness,Departure Delay in Minutes,Arrival Delay in Minutes,Satisfaction
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diff --git a/app.py b/app.py
new file mode 100644
index 0000000..323c087
--- /dev/null
+++ b/app.py
@@ -0,0 +1,174 @@
+ 
+import pickle
+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import numpy as np
+import matplotlib.pyplot as plt
+ 
+# loading the trained model
+pickle_in = open('classifier.pkl', 'rb') 
+classifier = pickle.load(pickle_in)
+clean_data = pd.read_csv("Cleaned_Satisfaction.csv")
+ 
+@st.cache()
+  
+# defining the function that will make the prediction using the data the user inputs 
+def prediction(Cus_Type, Class, Flight_Dist, Seat_Comfort, In_Wifi_Ser, 
+            In_Enter, Cleanliness, Depart_Delay, Arrival_Delay):   
+ 
+    # Pre-processing user input    
+    if Cus_Type == "Loyal Customer":
+        Cus_Type = 1
+    else:
+        Cus_Type = 0
+ 
+    if Class == "Business":
+        Class = 2
+    elif Class == "Eco Plus":
+        Class = 1
+    else:
+        Class = 0
+    
+    if Seat_Comfort == "Very Comfortable":
+        Seat_Comfort = 5
+    elif Seat_Comfort == "Comfortable":
+        Seat_Comfort = 4
+    elif Seat_Comfort == "Ordinary":
+        Seat_Comfort = 3
+    elif Seat_Comfort == "Uncomfortable":
+        Seat_Comfort = 2
+    elif Seat_Comfort == "Very Uncomfortable":
+        Seat_Comfort = 1
+    else:
+        Seat_Comfort = 0
+    
+    if In_Wifi_Ser == "Excellent":
+        In_Wifi_Ser = 5
+    elif In_Wifi_Ser == "Good":
+        In_Wifi_Ser = 4
+    elif In_Wifi_Ser == "Ordinary":
+        In_Wifi_Ser = 3
+    elif In_Wifi_Ser == "Unstable":
+        In_Wifi_Ser = 2
+    elif In_Wifi_Ser == "Poor":
+        In_Wifi_Ser = 1
+    else:
+        In_Wifi_Ser = 0
+    
+    if In_Enter == "Excellent":
+        In_Enter = 5
+    elif In_Enter == "Good":
+        In_Enter = 4
+    elif In_Enter == "Ordinary":
+        In_Enter = 3
+    elif In_Enter == "Poor":
+        In_Enter = 2
+    elif In_Enter == "Disappointing":
+        In_Enter = 1
+    else:
+        In_Enter = 0
+        
+    if Cleanliness == "Excellent":
+        Cleanliness = 5
+    elif Cleanliness == "Good":
+        Cleanliness = 4
+    elif Cleanliness == "Ordinary":
+        Cleanliness = 3
+    elif Cleanliness == "Poor":
+        Cleanliness = 2
+    elif Cleanliness == "Messy":
+        Cleanliness = 1
+    else:
+        Cleanliness = 0
+    
+ 
+    # Making predictions 
+    prediction = classifier.predict( 
+        [[Cus_Type, Class, Flight_Dist, Seat_Comfort, In_Wifi_Ser, 
+            In_Enter, Cleanliness, Depart_Delay, Arrival_Delay]])
+     
+    if prediction == 0:
+        pred = 'Neutral or Dissatisfied'
+    else:
+        pred = 'Satisfied'
+        
+    return pred
+
+def short_score(value):
+    return '{0:.3f}'.format(value)
+   
+def main():       
+    # front end elements of the web page 
+    html_temp = """ 
+    <div style ="background-color:blue;padding:13px"> 
+    <h1 style ="color:black;text-align:center;">Airflight Satisfaction Prediction</h1> 
+    </div> 
+    """
+      
+    # display the front end aspect
+    st.markdown(html_temp, unsafe_allow_html = True) 
+    
+    #first let's find out every user's average ratings for the flight
+    clean_data['Rating Average'] = clean_data[['Seat Comfort','Inflight Wifi Service',
+                                               'Inflight Entertainment','Cleanliness']].mean(axis = 1)
+    
+    st.header("1. Let's first explore the relationship between some features and satisfaction")
+    
+    st.subheader('The relationship between Flight Distance and Class')
+    fig, ax = plt.subplots(figsize=(6,6))
+    ax = sns.histplot(x='Flight Distance', hue='Class', data=clean_data, kde=True, palette='viridis')
+    average = np.average(clean_data['Flight Distance'])
+    plt.axvline(average, color='red')
+    plt.legend(labels=[*['Business','Eco Plus', 'Eco'], f'Average ({short_score(average)})'], loc='center left', bbox_to_anchor=(1, 0.5))
+    st.pyplot(fig)
+    
+    
+    st.subheader('Passenger Average Rating Distribution')
+    clean_data['Rating Average'] = clean_data['Rating Average'].astype(int)
+    fig, ax = plt.subplots(figsize=(6,6))
+    ax = sns.countplot(x='Rating Average', data=clean_data, palette='viridis')
+    st.pyplot(fig)
+    
+    st.subheader('Correlation between Features')
+    fig, ax = plt.subplots(figsize=(6,6))
+    ax = sns.heatmap(clean_data.corr(), annot = True, vmin=-1, vmax=1, center= 0, 
+                     fmt = '.1f', cmap=plt.cm.viridis, linecolor='white', linewidths=0.9)
+    st.pyplot(fig)
+    
+    
+    st.header("2. Let's use these features to make a prediction!")
+    # following lines create boxes in which user can enter data required to make prediction 
+    Cus_Type = st.selectbox('Customer Type',("Loyal Customer", "Others"))
+    
+    Class = st.selectbox('Travel Class', ("Business", "Eco Plus", "Eco")) 
+    
+    Flight_Dist = st.slider('Flight Distance', 100, 3500)
+    
+    Depart_Delay = st.slider('Departure Delay in Minutes', 0, 150)
+    
+    Arrival_Delay = st.slider('Arrival Delay in Minutes', 0, 150)
+    
+    Seat_Comfort = st.selectbox('Seat Comfortness', ("Very Comfortable", "Comfortable", 
+                                                     "Ordinary", "Uncomfortable", "Very Uncomfortable",
+                                                    "Painful"))
+    
+    In_Wifi_Ser = st.selectbox('Inflight Wifi Service',("Excellent", "Good", "Ordinary",
+                                                "Unstable", "Poor", "No Service"))
+    
+    In_Enter = st.selectbox('Inflight Entertainment',("Excellent", "Good", "Ordinary",
+                                                "Poor", "Disappointing", "No Entertainment"))
+    
+    Cleanliness = st.selectbox('Cleanliness',("Excellent", "Good", "Ordinary",
+                                                "Poor", "Messy", "Disgusting"))
+    
+    result =""
+      
+    # when 'Predict' is clicked, make the prediction and store it 
+    if st.button("Predict"): 
+        result = prediction(Cus_Type, Class, Flight_Dist, Seat_Comfort, In_Wifi_Ser, 
+                            In_Enter, Cleanliness, Depart_Delay, Arrival_Delay) 
+        st.success('The passenger is {} with this air travel'.format(result))
+     
+if __name__=='__main__': 
+    main()
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