OmarRdev/Binary_Classification_HotelBookings

1import streamlit as st2import joblib3import numpy as np4 5# Load the trained model6model = joblib.load('model_voting_classifier.pkl')7 8# Streamlit app9def main():10    st.title('Hotel Reservation Cancellation App')11    st.write('This app predicts whether a customer cancels their hotel reservation based on input characteristics.')12 13    # Feature input names14    feature_names = [15        'Lead Time', 'Required Car Parking Spaces', 'Total Of Special Requests', 'Total Days On Hold',16        'Market Segment Groups', 'Distribution Chanel TA', 'Deposit Type Non Refund',17    ]18 19    # Collect user input20    user_inputs = {}21    for feature in feature_names:22        23        if feature in ['Market Segment Groups', 'Distribution Chanel TA', 'Deposit Type Non Refund']:24            user_input = st.checkbox(feature, key=feature)25        else:26            user_input = st.number_input(feature, min_value=0, max_value=500, value=0, key=feature, step=1)27    28        user_inputs[feature] = user_input29        30    31    # Add a "Submit" button32    if st.button('Submit'):33        # Check if all inputs are zero (no input)34        if all(v == 0 or v is False for v in user_inputs.values()):35            st.warning("No input provided. Please enter values.")36            37        else:38            # Prepare input for prediction39            input_data = np.array([[user_inputs[feature] for feature in feature_names]])40    41        # Make prediction42        prediction = model.predict(input_data)43        booking_hotel = "**Canceled**" if prediction[0] == 1 else "**No Canceled**"44        45        # Display prediction46        st.write('Predicted Booking Type:', booking_hotel)47 48if __name__ == '__main__':49    main()