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app.py
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app.py
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# end to end model py file
import pickle
import pandas as pd
import numpy as np
import tensorflow as tf
import streamlit as st
from sklearn.preprocessing import StandardScaler,LabelEncoder,OneHotEncoder
# load the model
model = tf.keras.models.load_model('model1.h5')
# load the encoders
with open('ohe_encoder.pkl','rb') as file:
ohe_geography = pickle.load(file)
with open('data_scaler.pkl','rb') as file:
scaler = pickle.load(file)
with open('label_encoder_gender.pkl','rb') as file:
le_gender = pickle.load(file)
# using streamlit app
st.title('customer churn model')
# User input
geography = st.selectbox('Geography', ohe_geography.categories_[0])
gender = st.selectbox('Gender', le_gender.classes_)
age = st.slider('Age', 18, 92)
balance = st.number_input('Balance')
credit_score = st.number_input('Credit Score')
estimated_salary = st.number_input('Estimated Salary')
tenure = st.slider('Tenure', 0, 10)
num_of_products = st.slider('Number of Products', 1, 4)
has_cr_card = st.selectbox('Has Credit Card', [0, 1])
is_active_member = st.selectbox('Is Active Member', [0, 1])
# Prepare the input data
input_data = pd.DataFrame({
'CreditScore': [credit_score],
'Gender': [le_gender.transform([gender])[0]],
'Age': [age],
'Tenure': [tenure],
'Balance': [balance],
'NumOfProducts': [num_of_products],
'HasCrCard': [has_cr_card],
'IsActiveMember': [is_active_member],
'EstimatedSalary': [estimated_salary]
})
# One-hot encode 'Geography'
geo_encoded = ohe_geography.transform([[geography]])
geo_encoded_df = pd.DataFrame(geo_encoded, columns=ohe_geography.get_feature_names_out(['Geography']))
# Combine one-hot encoded columns with input data
input_data = pd.concat([input_data.reset_index(drop=True), geo_encoded_df], axis=1)
# Scale the input data
input_data_scaled = scaler.transform(input_data)
# Predict churn
prediction = model.predict(input_data_scaled)
prediction_proba = prediction[0][0]
st.write(f'Churn Probability: {prediction_proba:.2f}')
if prediction_proba > 0.5:
st.write('The customer is likely to churn.')
else:
st.write('The customer is not likely to churn.')