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

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Week Introduction
This week focuses on evaluating AI and ML models using various metrics. Students will learn about different evaluation metrics and their applications, performing a comparative analysis on a dataset selected by the student.
Week Objectives 
At the end of this week students will be able to:
· Describe different evaluation metrics for ML models. (CO2)
· Apply various evaluation metrics to a classification problem. (CO3)
· Compare the effectiveness of different evaluation metrics. (CO4)
 
Online Lecture 
 
The instructor will post the video recorded from this week's synchronous session here.
 
Below is an outline of the items for which you will be responsible throughout the week.
 
Reading with a due date
DUE: Early in the week
12 Important Model Evaluation Metrics for Machine Learning Everyone Should Know (Updated 2023)
https:
www.analyticsvidhya.com
log/2019/08/11-important-model-evaluation-e
or-metrics/Links to an external site.
Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics
https:
www.mdpi.com/ XXXXXXXXXX/10/5/593/pdfLinks to an external site.
Model Evaluation Techniques in Machine Learning (python example at end)
https:
medium.com/@fatmanurkutlu1/model-evaluation-techniques-in-machine-learning-8cd88deb8655Links to an external site.
 
Lecture with a due date
DUE: Early in the week
How to evaluate ML models | Evaluation metrics for machine learning
https:
www.youtube.com/watch?v=LbX4X71-TFILinks to an external site.
Metrics and evaluation of machine learning models
https:
www.youtube.com/watch?v=u1-m_hsF7DELinks to an external site.
Evaluating Machine Learning Models
https:
www.youtube.com/watch?v=FeKSQy5t_TILinks to an external site.
Evaluating Classification and Regression Machine Learning Models
https:
www.youtube.com/watch?v=pyl6fO4C7h4Links to an external site.
 
Additional Resources (reading + videos)
No Due date
No additional resources.
 
Submit your completed written assignment by Day 7 of this week.  For detailed instructions on completing this assignment, see the associated course page.
Week 6 Assignment – DUE: Day 7
Week 6 Assignment
Instructions:
Conduct a comparative analysis of different evaluation metrics for a classification problem. Use a dataset and demonstrate the application of at least three metrics. Submit your code AND a separate 1-page report on your approach and results. Use Jupyter notebooks for coding.
Your essay should be at least 2-3 pages in length, not including cover sheet and reference page, and fully explore all of the following items described above. Include at least 2 outside citations and use proper APA formatting.
This assignment is worth 100 points towards the maximum 1460 points you can earn in class.
Answered 3 days After Oct 16, 2024

Solution

Shubham answered on Oct 19 2024
3 Votes
Hotel Booking Cancellation Prediction
Hotel Booking Cancellation Prediction
Decision Systems
Proprietary content. © Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited.
Contents / Agenda
Data Dictionary
Business Problem Overview and Solution Approach
EDA Results
Data Preprocessing
Model Performance Summary
Conclusions and Recommendations
Appendix
Proprietary content. © Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited.
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
Proprietary content. © Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited.
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
Proprietary content. © Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited.
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
Proprietary content. © Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited.
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.
Proprietary content. © Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited.
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
Proprietary content. © Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited.
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
Proprietary content. © Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited.
Cancellation Rate: 32.8% of bookings are...
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