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app.py

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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",
+            )

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bc592f32d00a419f2962d0ef2eaaf82a584b4536b01421b5573264f7a3d952ad
+size 5746681