| import pandas as pd | |
| import gradio as gr | |
| import joblib | |
| le=joblib.load('le_col.pkl') | |
| mix=joblib.load('mimx_col.pkl') | |
| lr=joblib.load('model.pkl') | |
| le_col=['type_of_meal_plan','room_type_reserved','market_segment_type'] | |
| mimx_col=['no_of_adults','no_of_children','no_of_weekend_nights','no_of_week_nights','required_car_parking_space','lead_time','arrival_year','arrival_month','arrival_date','repeated_guest','no_of_previous_cancellations','no_of_previous_bookings_not_canceled','avg_price_per_room','no_of_special_requests'] | |
| def prediction_Hotel_Customer_Churn_Model(no,of,w,n,t,r,s,l,a,aa,ad,ms,rg,oc,pb,av,sr): | |
| try: | |
| input_data=pd.DataFrame({ | |
| 'no_of_adults':[no], | |
| 'no_of_children':[of], | |
| 'no_of_weekend_nights':[w], | |
| 'no_of_week_nights':[n], | |
| 'type_of_meal_plan':[t], | |
| 'required_car_parking_space':[r], | |
| 'room_type_reserved':[s], | |
| 'lead_time':[l], | |
| 'arrival_year':[a], | |
| 'arrival_month':[aa], | |
| 'arrival_date':[ad], | |
| 'market_segment_type':[ms], | |
| 'repeated_guest':[rg], | |
| 'no_of_previous_cancellations':[oc], | |
| 'no_of_previous_bookings_not_canceled':[pb], | |
| 'avg_price_per_room':[av], | |
| 'no_of_special_requests':[sr] | |
| }) | |
| for col in le_col: | |
| input_data[col]=le[col].transform(input_data[col]) | |
| input_data[mimx_col]=mix.transform(input_data[mimx_col]) | |
| prediction=lr.predict(input_data) | |
| if prediction[0]==0: | |
| return 'Not_Canceled' | |
| else: | |
| return 'Canceled' | |
| except Exception as e: | |
| return str(e) | |
| gr.Interface( | |
| inputs=[ | |
| gr.Number(label='no_of_adults'), | |
| gr.Number(label='no_of_children'), | |
| gr.Number(label='no_of_weekend_nights'), | |
| gr.Number(label='no_of_week_nights'), | |
| gr.Radio(['Meal Plan One', 'Not Selected', 'Meal Plan Two','Meal Plan Three'],label='type_of_meal_plan'), | |
| gr.Number(label='required_car_parking_space'), | |
| gr.Radio(['Room_Type 1', 'Room_Type 4', 'Room_Type 2', 'Room_Type 6','Room_Type 5', 'Room_Type 7', 'Room_Type 3'],label='room_type_reserved'), | |
| gr.Number(label='lead_time'), | |
| gr.Number(label='arrival_year'), | |
| gr.Number(label='arrival_month'), | |
| gr.Number(label='arrival_date'), | |
| gr.Radio(['Offline', 'Online', 'Corporate', 'Aviation', 'Complementary'],label='market_segment_type'), | |
| gr.Number(label='repeated_guest'), | |
| gr.Number(label='no_of_previous_cancellations'), | |
| gr.Number(label='no_of_previous_bookings_not_canceled'), | |
| gr.Number(label='avg_price_per_room'), | |
| gr.Number(label='no_of_special_requests') | |
| ], | |
| fn=prediction_Hotel_Customer_Churn_Model, | |
| outputs=gr.Textbox(label='Prediction'), | |
| title='Prediction Program', | |
| description='This App for work predict the Customer in hotel Not_Canceled or Canceled Booking' | |
| ).launch() |