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import streamlit as st
import pandas as pd
import pickle
import numpy as np
st.markdown("<h1 style='text-align: center; font-size: 48px; color: red;'>Bank Customer Churn Prediction </h1>", unsafe_allow_html=True)
# Load the dataset
@st.cache_data
def load_dataset():
return pd.read_csv('BankChurners.csv') # Update with the path to your dataset
# Cache function to convert DataFrame to CSV
@st.cache_data
def convert_df(df):
return df.to_csv(index=False).encode("utf-8")
# Load pre-trained model
@st.cache_resource
def load_model():
with open("model.pkl", "rb") as f:
model = pickle.load(f)
return model
# Load the dataset
df = load_dataset()
# Display the dataset preview
st.write("Dataset Preview:")
st.dataframe(df.head())
# Ensure the dataset has the required columns
required_columns = ['CLIENTNUM', 'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1', 'Total_Revolving_Bal',
'Avg_Utilization_Ratio', 'Total_Trans_Amt', 'Total_Relationship_Count',
'Total_Amt_Chng_Q4_Q1', 'Gender', 'Credit_Limit', 'Card_Category', 'Avg_Open_To_Buy']
if not all(col in df.columns for col in required_columns):
st.error(f"The dataset must contain the following columns: {', '.join(required_columns)}")
else:
# Add functionality for row selection
st.markdown("### Select a Customer Row for Prediction:")
# Option to select a row by index
selected_row_index = st.selectbox("Select a Row Index", options=range(len(df)), index=0)
# Add a button below the row selection for churn prediction
predict_button = st.button("Predict Churn")
# If the "Predict" button is clicked
if predict_button:
# Row to use for the model
row_to_use = df.iloc[selected_row_index]
# Prepare input data for the model
gender_mapping = {"M": 0, "F": 1}
card_category_mapping = {"Blue": 0, "Gold": 1, "Platinum": 2, "Titanium": 3}
# Map categorical values to numerical ones
row_to_use_for_model = [
row_to_use['CLIENTNUM'],
row_to_use['Total_Trans_Ct'],
row_to_use['Total_Ct_Chng_Q4_Q1'],
row_to_use['Total_Revolving_Bal'],
row_to_use['Avg_Utilization_Ratio'],
row_to_use['Total_Trans_Amt'],
row_to_use['Total_Relationship_Count'],
row_to_use['Total_Amt_Chng_Q4_Q1'],
gender_mapping[row_to_use['Gender']],
row_to_use['Credit_Limit'],
card_category_mapping[row_to_use['Card_Category']],
row_to_use['Avg_Open_To_Buy']
]
# Load the model
model = load_model()
# Check if the number of features matches the model's expectations
if len(row_to_use_for_model) != model.n_features_in_:
st.error(f"The model expects {model.n_features_in_} features, but {len(row_to_use_for_model)} were provided.")
else:
# Apply the model for churn prediction
prediction = model.predict([row_to_use_for_model])
# Display the row and the churn prediction result
st.write("Row selected for churn prediction:")
st.write(row_to_use)
# Show the prediction result
result = "Likely to Churn" if prediction[0] == 1 else "Likely to Stay"
st.write(f"Churn Prediction Result: {result}")
# Provide option to download the result
result_df = row_to_use.to_frame().T # Convert Series to DataFrame
result_df['Churn Prediction'] = result
result_csv = convert_df(result_df)
st.download_button(
label="Download Prediction Result",
data=result_csv,
file_name="Churn_Prediction_Result.csv",
mime="text/csv",
)
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