Please follow the context and description to build a presentation using the given template and dataset in .csv file

Hotel Booking Cancellation Prediction
Hotel Booking Cancellation Prediction
Decision Systems
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Contents / Agenda
Data Dictionary
Business Problem Overview and Solution Approach
EDA Results
Data Preprocessing
Model Performance Summary
Conclusions and Recommendations
Appendix
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Data Dictionary
The data contains the different attributes of customers' booking details. The detailed data dictionary is given below:
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
equired_car_parking_space: Does the customer require a car parking space? (0 - No, 1- Yes)
oom_type_reserved: Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels Group
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Data Dictionary
lead_time: Number of days between the date of booking and the a
ival date
a
ival_year: Year of a
ival date
a
ival_month: Month of a
ival date
a
ival_date: Date of the month
market_segment_type: Market segment designation.
epeated_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 cu
ent booking
no_of_previous_bookings_not_canceled: Number of previous bookings not canceled by the customer prior to the cu
ent booking
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How to use this deck?
This slide deck 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 hypothesis. 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
Note: The data visualizations you see in this deck are obtained from RapidMine
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Business Problem Overview and Solution Approach
Problem Definition
High hotel booking cancellations lead to significant revenue losses.
Unsold rooms increase operational costs and reduce profit margins.
Online booking channels complicate cancellation patterns.
Predicting cancellations is crucial for effective revenue management.
Solution Approach / Methodology
Analysis of customer booking data to identify cancellation factors.
Development of predictive machine learning model for cancellations.
Implementation of data-driven strategies for managing cancellations.
Formulate policies for refunds that optimize revenue.
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Implementation of dynamic pricing to maximize revenue based on demand.
Offer flexible cancellation policies to reduce last-minute cancellations.
Include data analytics for targeted marketing and inventory management.
EDA - Univariate Analysis
X-axis: A
ival month
Y-axis: Number of bookings
Description: Number of bookings per month
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Online Bookings Dominate: The majority of bookings (63.9%) are made online that shows strong preference for digital channels.
Offline Bookings Still Significant: Although less prevalent, offline bookings still account for nearly 30%, suggesting that is sizable portion of customers prefer traditional methods
Corporate and Specialized Segments: Corporate bookings (5.7%) and small segments like Complementary and Aviation make up minor part of the total.
EDA - Univariate Analysis
X-axis: Market Segment Type
Y-axis: Number of customers
Description: Number of Bookings received in each market segment
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Cancellation Rate: 32.8% of bookings are...