first commit

app.py

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+import streamlit as st
+import eda
+import pred
+
+page = st.sidebar.selectbox('Pilih Halaman: ', ('Prediction', 'EDA'))
+
+if page == 'EDA':
+    eda.run()
+else:
+    pred.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
+
+st.set_page_config(
+    page_title= 'FIFA 2022',
+    layout='wide',
+    initial_sidebar_state='expanded'
+)
+
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+
+
+
+
+def run():
+    
+    st.title('Heart Failure Prediction')
+    # st.subheader('Heart Failure Prediction Exploratory Data Analysis')
+    # #Show Dataframe
+    # d = pd.read_csv('h8dsft_P1G3_Ardifa-Rizky-Saputra.csv')
+    # st.dataframe(d)
+
+    # st.write('#### scatterplot berdasarkan Input User')
+    # pilihan1 = st.selectbox('Pilih column : ', ('age', 'creatinine_phosphokinase','ejection_fraction', 'platelets','serum_creatinine', 'serum_sodium', 'time'),key=1)
+    # pilihan2 = st.selectbox('Pilih column : ', ('age', 'creatinine_phosphokinase','ejection_fraction', 'platelets','serum_creatinine', 'serum_sodium', 'time'),key=2)
+    # pilihan3 = st.selectbox('Pilih column : ', ('anaemia', 'diabetes','high_blood_pressure', 'sex','smoking', 'DEATH_EVENT'),key=3)
+    # fig = plt.figure(figsize=(15, 5))
+    # sns.scatterplot(data=d,x=d[pilihan1],y=d[pilihan2],hue=d[pilihan3])
+    # st.pyplot(fig)
+    
+
+if __name__ == '__main__':
+    run()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:853b83ac1f391f0e7740ce417fb179e18e50ad49dd79e2b70ca06d8bcb32fbee
+size 403252

pred.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import json
+  
+#Load all files
+
+with open('model.pkl', 'rb') as file_1:
+  model = pickle.load(file_1)
+
+def run():
+  
+  hide_streamlit_style = """
+              <style>
+              #MainMenu {visibility: hidden;}
+              footer {visibility: hidden;}
+              </style>
+              """
+  st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+
+
+  with st.form(key='Form Hotel'):
+      hotel = st.selectbox('Select Hotel',('Resort Hotel', 'City Hotel'))
+      lead_time = st.slider('Select lead time', 1, 1, 10000,step=1)
+      arrival_date_year = st.selectbox('Year',('2015','2016','2017'))
+      arrival_date_month = st.selectbox('Month',
+                                        ('January','February','March','April','May','June','July','August','September','October','November','December',))
+      arrival_date_day_of_month = st.number_input('Day',1,31,step=1)
+      adults = st.number_input('Select Number of Adults',0,100,step=1)
+      children = st.number_input('Select Number of Children',0,100,step=1)
+      babies = st.number_input('Select Number of Babies',0,100,step=1)
+      previous_cancellations  = st.number_input('Cancellation Number',0,100,step=1)
+      days_in_waiting_list = st.number_input('Days in Waiting List',0,1000,step=1)
+      
+      submitted = st.form_submit_button('Predict')
+      
+  data_inf = {
+      'hotel' : hotel, 
+      'lead_time' : lead_time, 
+      'arrival_date_year' : arrival_date_year,
+      'arrival_date_month' : arrival_date_month, 
+      'arrival_date_day_of_month' : arrival_date_day_of_month,
+      'adults' : adults,
+      'children' : children,
+      'babies' : babies,
+      'previous_cancellations' : previous_cancellations,
+      'days_in_waiting_list' : days_in_waiting_list,
+  }
+
+  data_inf = pd.DataFrame([data_inf])
+  st.dataframe(data_inf)
+  data_trans = data_inf[['lead_time','adults','children','babies','previous_cancellations','days_in_waiting_list']]
+  
+  if submitted:
+      data_pred_inf = model.predict(data_trans)
+      if data_pred_inf == 1:
+        st.write('likely to be canceled')
+      else:
+        st.write('likely will not be canceled')
+      #st.write(f'# (1 = Yes, 0 = No) : {str(int(data_pred_inf))}')
+      
+if __name__ == '__main__':
+    run()

requirements.txt

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