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import streamlit as st | |
from pybanking.churn_prediction import model_churn | |
import pandas as pd | |
x = st.slider('Select a value') | |
st.write('This is the input data') | |
df = model_churn.get_data() | |
st.dataframe(df.head(5)) | |
model_names = [ | |
"Logistic_Regression", | |
"Support_Vector_Machine", | |
"Support_Vector_Machine_Optimized", | |
"Decision_Tree", | |
"Neural_Network", | |
"Random_Forest" | |
] | |
option = st.selectbox( | |
'Which model would you like to use?', | |
model_names) | |
option2 = st.selectbox( | |
'Which dataset would you like to use for prediction?', | |
['Sample dataset','Upload custom']) |