Hotel Booking Cancellation Prediction Analysis
Budget: $30 – $250 USD
I'm in a "No Code AI and Machine Learning" course and I need the Project+Presentation+Template+-+Business+Interpretation.pptx completed.
The goal is to complete and deliver the Project+Presentation+Template+-+Business+Interpretation.pptx file.
You will be required interpret the data and fill in the powerpoint which is the final project to be submitted. I've also included the raw data and RapidMiner files if helpful.
Attached are the files and below is the project description:
Project Problem Statement - Hotel Booking Cancellation Prediction
Context
A significant number of hotel bookings are called off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc. This is often made easier by the option to do so free of charge or preferably at a low cost which is beneficial to hotel guests but it is a less desirable and possibly revenue-diminishing factor for hotels to deal with. Such losses are particularly high on last-minute cancellations.
The new technologies involving online booking channels have dramatically changed customers’ booking possibilities and behavior. This adds a further dimension to the challenge of how hotels handle cancellations, which are no longer limited to traditional booking and guest characteristics.
The cancellation of bookings impacts a hotel on various fronts:
1. Loss of resources (revenue) when the hotel cannot resell the room.
2. Additional costs of distribution channels by increasing commissions or paying for publicity to help sell these rooms.
3. Lowering prices last minute, so the hotel can resell a room, resulting in reducing the profit margin.
4. Human resources to make arrangements for the guests.
Objective
The increasing number of cancellations calls for a Machine Learning based solution that can help in predicting which booking is likely to be canceled. INN Hotels Group has a chain of hotels in Portugal, they are facing problems with the high number of booking cancellations and have reached out to your firm for data-driven solutions. You as a data scientist have to analyze the data provided to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.
Data Description
The data contains the different attributes of customers' booking details. The detailed data dictionary is given below.
Data Dictionary
Booking_ID: the 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 weeknights (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:
Not Selected – No meal plan selected
Meal Plan 1 – Breakfast
Meal Plan 2 – Half board (breakfast and one other meal)
Meal Plan 3 – Full board (breakfast, lunch, and dinner)
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 Group
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: Is 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 euros)
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.
Submission Guidelines
There are two ways to work on this project:
i. Hands-on Tool Assessment Method: The high-complexity way is to use the provided sample RapidMiner / Dataiku / KNIME file. You must run the RapidMiner / Dataiku / KNIME file to execute the flow and generate results. This task should be accomplished using the knowledge gained from the module. Once completed, include relevant RapidMiner / Dataiku / KNIME screenshots in the presentation or slide deck to support your insights and answers.
ii. Business Interpretation Method: The low-complexity way is to use the provided slide deck which contains questions and screenshots from rapid miner to assess your skills in understanding data visualizations, identifying patterns/insights, and formulating hypotheses. You need to customize the deck by replacing the questions with appropriate answers as needed.
The primary purpose of providing these two options is to allow learners to opt for the approach that aligns with their learning aspirations and outcomes. The below table elaborates on these two options.
Submission type
Who should choose
Final submission file
Submission Format
Hands-on Tool Assessment
Learners who aspire to be hands-on exploring the no-code tools to create ML workflows
Business report/slide deck in .pdf format with problem definition, insights, and recommendations
.pdf
Business Interpretation
Learners who prioritize comprehending the concepts rather than working on tools, with an emphasis on extracting insights, making recommendations, and so on.
Business report/slide deck in .pdf format with problem definition, insights, and recommendations
.pdf
Please follow the below steps to complete the assessment and make sure that all the sections mentioned in the rubric have been covered in your submission.
i. Hands-on Tool Assessment Method
You are provided with a RapidMiner (.rmp) / Dataiku (.zip) / KNIME (.knwf) file which is present on the page "No-Code Tool Files - Hotel Booking Cancellation Prediction" that already has the process prepared. Your task is to run the RapidMinee / Dataiku file or run the KNIME file.
After executing the file, kindly include relevant screenshots from RapidMiner / Dataiku / KNIME utilized for deriving insights while answering the questions in the presentation or slide deck.
Please note that the provided RapidMiner / Dataiku / KNIME file is intended as a guide to help you complete the project. You are not restricted to using only the provided flow. You are encouraged to use your methods to reach the solution as well. These templates can be used as a reference to understand the structure of the project but feel free to explore other techniques to generate insights for the given problem.
Please use the deck from "Project Presentation Template - Hands-on Tool Assessment" to make the final submission and you can edit the file directly for the final submission
ii. Business Interpretation Method
You will be provided with a Presentation/slide deck that serves as a comprehensive template for your project submission
Within this deck, you will come across various questions that are intended to test your ability to understand data visualizations, discover patterns/insights, and postulate hypotheses. Think thoroughly and provide answers to these questions
You are encouraged to modify this deck as required, by replacing the questions with suitable answers
Please feel free to incorporate additional points if you deem necessary
Please use the deck from "Project Presentation Template - Business Interpretation" to make the final submission and you can edit the file directly for the final submission
Best Practices for Submission
The presentation or slide deck should be made keeping in mind that the audience will be the Data Science lead of a company.
The key points in the presentation/slide deck should be the following:
Business Overview of the Problem and solution approach
Key findings and insights
Business recommendations
Focus on explaining the key takeaways in an easy-to-understand manner.
The inclusion of the potential benefits of implementing the solution will give you the edge.
The goal is to complete and deliver the Project+Presentation+Template+-+Business+Interpretation.pptx file.
You will be required interpret the data and fill in the powerpoint which is the final project to be submitted. I've also included the raw data and RapidMiner files if helpful.
Attached are the files and below is the project description:
Project Problem Statement - Hotel Booking Cancellation Prediction
Context
A significant number of hotel bookings are called off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc. This is often made easier by the option to do so free of charge or preferably at a low cost which is beneficial to hotel guests but it is a less desirable and possibly revenue-diminishing factor for hotels to deal with. Such losses are particularly high on last-minute cancellations.
The new technologies involving online booking channels have dramatically changed customers’ booking possibilities and behavior. This adds a further dimension to the challenge of how hotels handle cancellations, which are no longer limited to traditional booking and guest characteristics.
The cancellation of bookings impacts a hotel on various fronts:
1. Loss of resources (revenue) when the hotel cannot resell the room.
2. Additional costs of distribution channels by increasing commissions or paying for publicity to help sell these rooms.
3. Lowering prices last minute, so the hotel can resell a room, resulting in reducing the profit margin.
4. Human resources to make arrangements for the guests.
Objective
The increasing number of cancellations calls for a Machine Learning based solution that can help in predicting which booking is likely to be canceled. INN Hotels Group has a chain of hotels in Portugal, they are facing problems with the high number of booking cancellations and have reached out to your firm for data-driven solutions. You as a data scientist have to analyze the data provided to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.
Data Description
The data contains the different attributes of customers' booking details. The detailed data dictionary is given below.
Data Dictionary
Booking_ID: the 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 weeknights (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:
Not Selected – No meal plan selected
Meal Plan 1 – Breakfast
Meal Plan 2 – Half board (breakfast and one other meal)
Meal Plan 3 – Full board (breakfast, lunch, and dinner)
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 Group
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: Is 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 euros)
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.
Submission Guidelines
There are two ways to work on this project:
i. Hands-on Tool Assessment Method: The high-complexity way is to use the provided sample RapidMiner / Dataiku / KNIME file. You must run the RapidMiner / Dataiku / KNIME file to execute the flow and generate results. This task should be accomplished using the knowledge gained from the module. Once completed, include relevant RapidMiner / Dataiku / KNIME screenshots in the presentation or slide deck to support your insights and answers.
ii. Business Interpretation Method: The low-complexity way is to use the provided slide deck which contains questions and screenshots from rapid miner to assess your skills in understanding data visualizations, identifying patterns/insights, and formulating hypotheses. You need to customize the deck by replacing the questions with appropriate answers as needed.
The primary purpose of providing these two options is to allow learners to opt for the approach that aligns with their learning aspirations and outcomes. The below table elaborates on these two options.
Submission type
Who should choose
Final submission file
Submission Format
Hands-on Tool Assessment
Learners who aspire to be hands-on exploring the no-code tools to create ML workflows
Business report/slide deck in .pdf format with problem definition, insights, and recommendations
Business Interpretation
Learners who prioritize comprehending the concepts rather than working on tools, with an emphasis on extracting insights, making recommendations, and so on.
Business report/slide deck in .pdf format with problem definition, insights, and recommendations
Please follow the below steps to complete the assessment and make sure that all the sections mentioned in the rubric have been covered in your submission.
i. Hands-on Tool Assessment Method
You are provided with a RapidMiner (.rmp) / Dataiku (.zip) / KNIME (.knwf) file which is present on the page "No-Code Tool Files - Hotel Booking Cancellation Prediction" that already has the process prepared. Your task is to run the RapidMinee / Dataiku file or run the KNIME file.
After executing the file, kindly include relevant screenshots from RapidMiner / Dataiku / KNIME utilized for deriving insights while answering the questions in the presentation or slide deck.
Please note that the provided RapidMiner / Dataiku / KNIME file is intended as a guide to help you complete the project. You are not restricted to using only the provided flow. You are encouraged to use your methods to reach the solution as well. These templates can be used as a reference to understand the structure of the project but feel free to explore other techniques to generate insights for the given problem.
Please use the deck from "Project Presentation Template - Hands-on Tool Assessment" to make the final submission and you can edit the file directly for the final submission
ii. Business Interpretation Method
You will be provided with a Presentation/slide deck that serves as a comprehensive template for your project submission
Within this deck, you will come across various questions that are intended to test your ability to understand data visualizations, discover patterns/insights, and postulate hypotheses. Think thoroughly and provide answers to these questions
You are encouraged to modify this deck as required, by replacing the questions with suitable answers
Please feel free to incorporate additional points if you deem necessary
Please use the deck from "Project Presentation Template - Business Interpretation" to make the final submission and you can edit the file directly for the final submission
Best Practices for Submission
The presentation or slide deck should be made keeping in mind that the audience will be the Data Science lead of a company.
The key points in the presentation/slide deck should be the following:
Business Overview of the Problem and solution approach
Key findings and insights
Business recommendations
Focus on explaining the key takeaways in an easy-to-understand manner.
The inclusion of the potential benefits of implementing the solution will give you the edge.
Related categories:
Statistics
Machine Learning (ML)
Statistical Analysis
Data Science
Data Analytics