Predicting Hotel Guests' Car Parking Needs Using Big Data

MKTG 746 Big Data Predicting Car Parking Space Requirement for Hotel Guests using Big Data Analytics

Introduction Problem statement Objectives and measurement Data source Data set intro Initial data preparation Data Exploration Preliminary exploration Data exploration and visualization EDA Summary Data preparation Feature selection Missing values – Imputation Non-numeric values – recode Extreme values Model exploration Decision trees Logistic regression Neural network Results and analysis Performance/model assessment (ROC/ASE) Feature importance Best model result Conclusion and recommendations Conclusion Recommendation Further research Bibliography

Did you understand the variables right and have you decided on the variable that would help you in telling a data story? Have you identified the right independent variables that would help you add to the story; did you see some variables that are not important or wouldn't help with a compelling storyboard for your client? ANSWER: We want to predict if the customer is going to require a car parking space when they’re book a hotel reservation - customer's likelihood of requiring car parking space (such as their travel itinerary, previous booking history, etc.), it may be possible to develop a predictive model to estimate the likelihood of a customer requiring parking Did you run your decision tree? YES All the decision trees? YES How many leaves? (was it at least 4 splits on different variables?) YES Did you deal with your missing values? NOT YET Did you try a forward regression model? NOT YET Did it converge? YES Where there any problems that you have to meet me and Prof specifically for? QUESTIONS:  Which variables should we reject?  Should we used the Booking status as our target instead of requires a car parking space?  NOTE:  Changed the Leaf size to 4 to 3  Method – Largest  Assessment Measure – Misclassification

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DATA DICTIONARY NAME MODEL ROLE MEASUREME NT LEVEL DESCRIPTION Booking_ID ID Nominal unique identifier of each booking no_of_adults Input Interval Number of adults no_of_children Input Interval Number of Children no_of_weekend_nights Input Interval Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel no_of_week_nights Input Interval Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel type_of_meal_plan Input Nominal Type of meal plan booked by the customer required_car_parking_space Target Binary Does the customer require a car parking space? (0 - No, 1- Yes) room_type_reserved Input Nominal Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels. lead_time Input Interval Number of days between the date of booking and the arrival date arrival_year Rejected Nominal Year of arrival date

arrival_month Input Nominal Month of arrival date arrival_date Input Nominal Date of the month market_segment_type Input Nominal Market segment designation. repeated_guest Input Binary Is the customer a repeated guest? (0 - No, 1- Yes) no_of_previous_cancellations Rejected Interval Number of previous bookings that were canceled by the customer prior to the current booking no_of_previous_bookings_not_canc eled Rejected Interval Number of previous bookings not canceled by the customer prior to the current booking avg_price_per_room Input Interval Average price per day of the reservation; prices of the rooms are dynamic. (in euros) no_of_special_requests Input Interval Total number of special requests made by the customer (e.g. high floor, view from the room, etc) booking_status Input Nominal Flag indicating if the booking was cancelled or not.

REQUIRED PARKING SPACE DECISION TREE

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Big Data Group Project

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