solve this question in the document

Homework 3 Background: As a hotel chain, o he biggest issues in managing capacity occurs when guests cancel their reservations. You work for a hotel chain and are interested in whether or not there are ways to predict who is likely to cancel their reservation. Please use the data in the tile \"Hotel Reservations.csv\" to answer the following questions: 1. Create a distribution of Booking Status. What percentage of bookings were canceled (5 points)? 2. Use the Make Validation Colum utility to create a training, validation, and test sets. What percentage of reservations where cancelled in each of the three sets of data (training, validation, and tests)? (5 points) 3. Create at least three different models, using different modeling techniques (e.g., decision tree, boosting, bagging, logistic) predicting who is likely or not likely to cancel. For each model, create what you believe is the best version of that model. 4. Use the model comparison platform to compare the model's performance. Which model is the best model? Please explain why (30 points). 5. Based on the models you created, what are major insights? What variables are important in predicting which customers are likely to cancel? What characteristics of the stay impact the likelihood customers cancel the reservation? What are the general business insights gleaned from these model builds? (30 points) 6. Please describe how you might use this model to generate business value in your hotel chain (30 points). Data Description: Booking_ID: unique identifier of each booking no of adults: Number of adults no_of children: Number of Children no_of weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel no_of week nights: Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel type of meal plan: Type of meal plan booked by the customer: required_car_parking_space: Does the customer require a car parking space? (0 - No, 1- Yes) room_type_reserved: Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels. lead_time: Number of days between the date of booking and the arrival date arrival_year: Year of arrival date arrival month: Month of arrival date arrival_date: Date of the month market_segment_type: Market segment designation. repeated_guest: [s the customer a repeated guest? (0 - No, 1- Yes) no_of_previous_cancellations: Number of previous bookings that were canceled by the customer prior to the current booking no_of_previous_bookings not_canceled: Number of previous bookings not canceled by the customer prior to the current booking avg_price_per_room: Average price per day of the reservation; prices of the rooms are dynamic. (in curos) no_of_special requests: Total number of special requests made by the customer (e.g. high floor, view from the room, etc) booking_status: Flag indicating if the booking was canceled or not