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app.py

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+import streamlit as st
+import eda
+import prediction
+
+navigation = st.sidebar.selectbox('pilih halaman : ', ('EDA', 'Predict Cancelation'))
+
+if navigation == 'EDA':
+    eda.run()
+
+else:
+    prediction.run()

eda.py

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+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import plotly.express as px
+from PIL import Image
+
+# untuk lebarkan layout setelah import
+st.set_page_config(
+    page_title = 'Hotel Reservation',
+    layout = 'wide',
+    initial_sidebar_state='expanded'
+)
+
+def run():
+
+    # Membuat file
+    st.title( 'Hotel Reservation ')
+
+    # Membuat sub header
+    st.subheader('Cancel or No Cancel Reservation')
+
+    # Menambahkan gambar
+    image = Image.open('hotel.jpg')
+    st.image(image, caption='Creepy Hotel')
+
+    # Menambahkan deskripsi
+    st.write('Exploratory Data dari dataset Hotel Reservation')
+
+    # show data frame
+    st.write('Menampilkan 10 Data dari dataset')
+    df = pd.read_csv('https://raw.githubusercontent.com/mukhlishr/rasyidi/main/h8dsft_P1G3_mukhlish_rasyidi.csv')
+    st.dataframe(df.head(10))
+
+    # Barplot booking status
+    st.write('###### Status Cancel Reservation')
+    fig=plt.figure(figsize=(15,5))
+    sns.barplot(x=df['booking_status'].value_counts().index, y=df['booking_status'].value_counts())
+    st.pyplot(fig)
+
+    # Barplot segmented market 
+    st.write('###### Source of reservation')
+    fig=plt.figure(figsize=(15,5))
+    sns.barplot(x=df['market_segment_type'].value_counts().index, y=df['market_segment_type'].value_counts())
+    st.pyplot(fig)
+
+    # Barplot price room 
+    st.write('###### Price room categories (1 = low, 2 = medium, 3 = high)')
+    bins = [-1, 100,200,1000]
+    labels =[1,2,3]
+    df['binned_price'] = pd.cut(df['avg_price_per_room'], bins,labels=labels).astype(int)
+    fig=plt.figure(figsize=(15,5))
+    sns.barplot(x=df['binned_price'].value_counts().index, y=df['binned_price'].value_counts())
+    st.pyplot(fig)
+
+    # Barplot type room 
+    st.write('###### Room type reserved')
+    fig=plt.figure(figsize=(15,5))
+    sns.barplot(x=df['room_type_reserved'].value_counts().index, y=df['room_type_reserved'].value_counts())
+    st.pyplot(fig)    
+
+    # Barplot lead time 
+    st.write('###### lead time date reservation to date stay (1 = low, 2 = medium, 3 = high)')
+    st.write('###### 1 = < 3 days, 2 = 3-7 days, 3 = 7-14 days, 4 = 14 -30 days, 5 = 30 - 90 days, 6 = > 90 days)')
+    bins = [-1, 3, 7, 14,30,90,500]
+    labels =[1,2,3,4,5,6]
+    df['binned_lead_time'] = pd.cut(df['lead_time'], bins,labels=labels).astype(int)
+    fig=plt.figure(figsize=(15,5))
+    sns.barplot(x=df['binned_lead_time'].value_counts().index, y=df['binned_lead_time'].value_counts())
+    st.pyplot(fig)
+
+
+    
+
+if __name__ == '__main__':
+    run()

grid_rfc_best.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8b7d5fd666a9858a977e3ee8c1a7d0d80e0a49e3bf80083f80abda498e9a66d8
+size 472064636

prediction.py

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+import streamlit as st
+import pandas as pd
+import pickle
+
+
+
+# Load All Files
+
+with open('grid_rfc_best.pkl', 'rb') as file_1:
+  grid_rfc_best = pickle.load(file_1)
+
+
+
+
+# bikin fungsi  
+def run():
+
+  with st.form(key='Reserved_data'):
+      no_of_adults = st.selectbox('adult(s)', (1,2,3,4,5,6,7,8), index=1, help='How many adults will stay')
+      no_of_children = st.selectbox('children', (0,1,2,3,4), index=0, help='How many adults will stay')
+      no_of_weekend_nights = st.selectbox('Weekend Night', (0,1,2,3,4,5,6,7,8,9,10), index=1, help='How many Night will stay at weekend')
+      no_of_week_nights = st.number_input('Weekdays Night', min_value=0, max_value=20, value=1, help='How many Night will stay at weekdays')
+      type_of_meal_plan = st.selectbox('Type of Meal', (1,2,3,4), index=1, help='1 = type 1 \n 2 = type 2 \n 3 = type 3 \n 4 = Not Selected')
+      required_car_parking_space = st.radio('Car Parking', (0, 1), index=1, help='0 = No \n 1 = Yes')
+      room_type_reserved = st.selectbox('Room Type', (1,2,3,4,5,6,7), index=1)
+      arrival_month  = st.selectbox('Arrival Month', (1,2,3,4,5,6,7,8,9,10,11,12), index=1)
+      market_segment_type =  st.radio('Source order', (1, 2, 3, 4, 5), index=1, help='1=online \n 2=offline \n 3=corporate \n 4=complementary \n 5=aviation')
+      no_of_previous_cancellations = st.selectbox('History Cancel', (0,1,2,3,4,5,6,7,8,9,10), index=0)
+      no_of_special_requests = st.selectbox(' Special Request', (0,1,2,3,4,5), index=0)
+      binned_lead_time = st.radio('Lead time', (1, 2, 3, 4, 5,6), index=1, help='1= <3 days \n 2= 3-7 days \n 3= 7-14 days \n 4= 14-30 days \n 5=30-90 days \n 6= >90 days')
+      binned_no_cancel = st.radio('History not Cancel', (0,1), index=1)
+      binned_price = st.radio('Type of Price', (1, 2, 3), index=1, help='1 = low \n 2 = medium \n 3 = high')
+      st.markdown('---')
+
+      submitted = st.form_submit_button('Predict')
+
+  data_inf = {
+    'no_of_adults': no_of_adults,
+    'no_of_children':no_of_children,
+    'no_of_weekend_nights' :no_of_weekend_nights,
+    'no_of_week_nights':no_of_week_nights,
+    'type_of_meal_plan':type_of_meal_plan,  
+    'required_car_parking_space': required_car_parking_space,
+    'room_type_reserved': room_type_reserved,
+    'arrival_month': arrival_month,
+    'market_segment_type' : market_segment_type,
+    'no_of_previous_cancellations' : no_of_previous_cancellations,
+    'no_of_special_requests' : no_of_special_requests,
+    'binned_lead_time' : binned_lead_time,
+    'binned_no_cancel' : binned_no_cancel,
+    'binned_price': binned_price
+  }
+
+  data_inf = pd.DataFrame([data_inf])
+  st.dataframe(data_inf)
+
+  if submitted:
+
+      # Predict using grid_rfc
+      y_pred_inf = grid_rfc_best.predict(data_inf)
+      st.write('# Rating: ', str(int(y_pred_inf)))
+      
+if __name__ == '__main__':
+    run()

requirements.txt

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+# daftar library yang dibutuhkan semua
+streamlit
+pandas
+seaborn
+matplotlib
+numpy
+scikit-learn==1.2.1
+plotly