Hi i am unsure of Question 1 part A to C.

Question 1 A research study reported that one in five hotel bookings are canceled before the guest arrives at the hotel. A last-minute cancellation or no-show means the room would be left unoccupied, which results in a loss of revenue for the day. Even if the hotel manages to sell the room again, it would probably be at a much lower rate because it is too near to the check-in date. For a hotel, a high cancellation ratio not only impacts the revenue but also leads to poor marketing and low market share. To improve business, the marketing manager of hotel H would like to learn more about the booking cancellation issue. As a data analyst in the marketing department, you are given a relevant dataset Hotel_Booking_Cancellation.csy, which contains a total of 2,417 hotel booking records. Your job is to help the hotel understand the possible factors that would affect the booking cancellation and assist your manager in evaluating the usefulness of the constructed models for the given problem. The details of the dataset are as following: FIELD DESCRIPTION lead_time # of days that elapsed between the date of the booking and the date of arrival, integer value between [0, 504] arrival_date_quarter Quarter of the arrival date, four categories: Q1, Q2, Q3, and Q4. arrival_part_of_month Part of the month of the arrival date, three categories: Early (1th to 10th), Mid (11th to 20th), Late (21th to the end)Question 1 A research study reported that one in five hotel bookings are canceled before the guest arrives at the hotel. A last-minute cancellation or no-show means the room would be left unoccupied, which results in a loss of revenue for the day. Even if the hotel manages to sell the room again, it would probably be at a much lower rate because it is too near to the check-in date. For a hotel, a high cancellation ratio not only impacts the revenue but also leads to poor marketing and low market share. To improve business, the marketing manager of hotel H would like to learn more about the booking cancellation issue. As a data analyst in the marketing department, you are given a relevant dataset Hotel_Booking_Cancellation.csy, which contains a total of 2,417 hotel booking records. Your job is to help the hotel understand the possible factors that would affect the booking cancellation and assist your manager in evaluating the usefulness of the constructed models for the given problem. The details of the dataset are as following: FIELD DESCRIPTION lead_time # of days that elapsed between the date of the booking and the date of arrival, integer value between [0, 504] arrival_date_quarter Quarter of the arrival date, four categories: Q1, Q2, Q3, and Q4. arrival_part_of_month Part of the month of the arrival date, three categories: Early (1th to 10th), Mid (11th to 20th), Late (21th to the end)stays_in_weekend_nights # of weekend nights (Saturday or Sunday) the customer stayed or booked to stay at the hotel, three values: 0, 1, 2 stays_in_week_nights # of weeknights (Monday to Friday) the customer stayed or booked to stay at the hotel, six values: 0, 1, 2, 3, 4, 5 #adults # of adults, five values: 0, 1, 2, 3, 4 #children # of children, four values: 0, 1, 2, 3 distribution_channel The customer booked the hotel through which channel. There are four different channels: . Corporate - through corporate, such as business travel; . Direct - by the customer self; . GDS - through the hotel Global Distribution System. GDS is a worldwide conduit between travel bookers and suppliers; . TA/TO - TA is short for "Travel Agents" and TO is short for "Tour Operators" is_repeated_guest If the booking was from a repeated guest (1) or not (0) booking_changes # of changes/amendments made to the booking from the moment the booking was made until the moment of check-in or cancellation, six values: 0, 1, 2, 3, 4, 5 deposit_type Indicates if the customer made a deposit to guarantee the booking. There are three categories: . No Deposit - no deposit was made; . Non Refund - a deposit was made in the value of the total stay cost; . Refundable - a deposit was made with a value under the total cost of the stay. customer_type The type of booking. There are four categories: . Contract - when the booking has an allotment or other type of contract associated with it; . Group - when the booking is associated with a group; . Transient - when the booking is not part of a group or contract and is not associated with otherstays_in_weekend_nights # of weekend nights (Saturday or Sunday) the customer stayed or booked to stay at the hotel, three values: 0, 1, 2 stays_in_week_nights # of weeknights (Monday to Friday) the customer stayed or booked to stay at the hotel, six values: 0, 1, 2, 3, 4, 5 #adults # of adults, five values: 0, 1, 2, 3, 4 #children # of children, four values: 0, 1, 2, 3 distribution_channel The customer booked the hotel through which channel. There are four different channels: . Corporate - through corporate, such as business travel; . Direct - by the customer self; . GDS - through the hotel Global Distribution System. GDS is a worldwide conduit between travel bookers and suppliers; . TA/TO - TA is short for "Travel Agents" and TO is short for "Tour Operators" is_repeated_guest If the booking was from a repeated guest (1) or not (0) booking_changes # of changes/amendments made to the booking from the moment the booking was made until the moment of check-in or cancellation, six values: 0, 1, 2, 3, 4, 5 deposit_type Indicates if the customer made a deposit to guarantee the booking. There are three categories: . No Deposit - no deposit was made; . Non Refund - a deposit was made in the value of the total stay cost; . Refundable - a deposit was made with a value under the total cost of the stay. customer_type The type of booking. There are four categories: . Contract - when the booking has an allotment or other type of contract associated with it; . Group - when the booking is associated with a group; . Transient - when the booking is not part of a group or contract and is not associated with othertransient bookings; . Transient-party - when the booking is transient but is associated with at least other transient bookings required_car_parking_spaces # of car parking spaces required by the customer total_of_special_requests # of special requests made by the customer (e.g. twin bed, ocean view room or garden view room), six values: 0, 1, 2, 3, 4, 5 is_canceled If the booking was canceled (1) or not (0) (a) Describe the business objective and data mining objective of the given business problem. Appraise whether the Association Rule Mining method is appropriate to study this problem. (10 marks) (b) Construct an Apriori model on the dataset using IBM SPSS Modeler. The model details and interpretation of the results should include the following: (i) Based on the given business problem, discuss how to set the measurement and role setting of the fields and report the screenshot of the final settings. (Note: exclude variable lead_time from the association analysis). (ii) Set the Minimum Support = 10%, Minimum Confidence = 60%, Maximum number of antecedents = 5. Report the number of rules generated, and give a screenshot of the rules. (iii) Analyse the generated rules by considering various evaluation measures, is there any pattern to help understand booking cancellations? (iv) Change the parameter setting to: Minimum Support = 1%, Minimum Confidence = 10%, Maximum number of antecedents = 5, Evaluation measure = Confidence Difference, and Evaluation measure lower bound = 50. Report the generated rules, compare and discuss the similarity and (or) differences amoung them and the rules generated in part (iii). (35 marks) (c) Suppose your line manager would like to study the possible impact of lead time using Apriori algorithm, discuss whether data preparation is required. If your answer is Yes, illustrate the details of data preparation and report the processed result. (Note: you may use IBM SPSS Modeler or any other tools to do the data preparation if any, but no need to implement association analysis). (15 marks)transient bookings; . Transient-party - when the booking is transient but is associated with at least other transient bookings required_car_parking_spaces # of car parking spaces required by the customer total_of_special_requests # of special requests made by the customer (e.g. twin bed, ocean view room or garden view room), six values: 0, 1, 2, 3, 4, 5 is_canceled If the booking was canceled (1) or not (0) (a) Describe the business objective and data mining objective of the given business problem. Appraise whether the Association Rule Mining method is appropriate to study this problem. (10 marks) (b) Construct an Apriori model on the dataset using IBM SPSS Modeler. The model details and interpretation of the results should include the following: (i) Based on the given business problem, discuss how to set the measurement and role setting of the fields and report the screenshot of the final settings. (Note: exclude variable lead_time from the association analysis). (ii) Set the Minimum Support = 10%, Minimum Confidence = 60%, Maximum number of antecedents = 5. Report the number of rules generated, and give a screenshot of the rules. (iii) Analyse the generated rules by considering various evaluation measures, is there any pattern to help understand booking cancellations? (iv) Change the parameter setting to: Minimum Support = 1%, Minimum Confidence = 10%, Maximum number of antecedents = 5, Evaluation measure = Confidence Difference, and Evaluation measure lower bound = 50. Report the generated rules, compare and discuss the similarity and (or) differences amoung them and the rules generated in part (iii). (35 marks) (c) Suppose your line manager would like to study the possible impact of lead time using Apriori algorithm, discuss whether data preparation is required. If your answer is Yes, illustrate the details of data preparation and report the processed result. (Note: you may use IBM SPSS Modeler or any other tools to do the data preparation if any, but no need to implement association analysis). (15 marks)