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Deployment.py
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Deployment.py
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#!/usr/bin/env python
# coding: utf-8
# In[3]:
import pandas as pd
import streamlit as st
from imblearn.combine import SMOTEENN
from collections import Counter
from sklearn.ensemble import RandomForestClassifier
st.title('Model Development \n Telecommunication Churning')
st.sidebar.header('Input Features')
def input_features():
Voice_Plan=st.sidebar.selectbox('Voice Plan',('1','0'))
International_Plan=st.sidebar.selectbox('International Plan',('1','0'))
International_Calls=st.sidebar.number_input('Insert Number Of Calls')
International_Charges=st.sidebar.number_input('Insert International Charge')
Day_Charges=st.sidebar.number_input('Insert Day Charge')
Evening_Mins=st.sidebar.number_input('Insert Evening Minutes')
Night_Mins=st.sidebar.number_input('Insert Night Minutes')
data={'Voice_Plan':Voice_Plan,
'International_Plan':International_Plan,
'International_Calls':International_Calls,
'International_Charges':International_Charges,
'Day_Charges':Day_Charges,
'Evening_Mins':Evening_Mins,
'Night_Mins':Night_Mins}
features=pd.DataFrame(data,index=[0])
return features
df=input_features()
st.subheader('User Input Features')
st.write(df)
churn=pd.read_csv('Churn_Without_Outliers',encoding='utf_8')
x=churn.iloc[:,1:8]
y=churn.iloc[:,8]
sm=SMOTEENN()
X,Y = sm.fit_resample(x,y)
RF=RandomForestClassifier()
RF.fit(X,Y)
predict=RF.predict(df)
prediction_probability=RF.predict_proba(df)
st.subheader('Prediction Result')
st.write('Yes' if prediction_probability[0][1]>0.5 else 'No')
st.subheader('Prediction Probability')
st.write(prediction_probability)