aenoboa1 / Project_MlZoomcamp

Introduction

• In the following midterm project , I decided to use the Hotel booking demand data set from Kaggle, to predict whenever a customer's booking will be cancelled or not. I thought this was a really good data set to practice and learn a lot about ML techniques and challenges , as this dataset contained tons of features and clean data to work with , furthermore , the proposed Article was really helpful to understand how this dataset worked.

This data set contains a single file which compares various booking information between two hotels: a resort hotel(H1), and a city hotel(H2) , comprehending bookings due to arrive between July of 2015 and the 31st of August 2017, including bookings that effectively arrived and bookings that were canceled. Article

Project Navigation

Notebook Description: -Reading the data set and Introduction -Exploratory Data Analysis with Pandas and -NumPy -Data Preparation using Sklearn -Selecting and Training a few Machine -Learning Models -Cross-Validation and Hyperparameter -Tuning using Sklearn

Description	Link
Notebook	Explanatory notebook with EDA and Training
Hotel booking demand data set	csv file
DockerFile commands	dockerfile
Pipenv file	Pipfile
Train.py	Training of final XGBoost model script
Predict.py	Predict app
predict_test.py	To predict customer cancelling probability

Data description

Variable	Type	Description
ADR	Numeric	Average Daily Rate
Adults	Integer	Number of adults
Agent	Categorical	ID of the travel agency that made the bookinga
ArrivalDateDayOfMonth	Integer	Day of the month of the arrival date
ArrivalDateMonth	Categorical	Month of arrival date with 12 categories: “January” to “December”
ArrivalDateWeekNumber	Integer	Week number of the arrival date
ArrivalDateYear	Integer	Year of arrival date
AssignedRoomType	Categorical	Code for the type of room assigned to the booking. Sometimes the assigned room type differs from the reserved room type due to hotel operation reasons (e.g. overbooking) or by customer request. Code is presented instead of designation for anonymity reasons
Babies	Integer	Number of babies
BookingChanges	Integer	Number of changes/amendments made to the booking from the moment the booking was entered on the PMS until the moment of check-in or cancellation
Children	Integer	Number of children
Company	Categorical	ID of the company/entity that made the booking or responsible for paying the booking. ID is presented instead of designation for anonymity reasons
Country	Categorical	Country of origin.
CustomerType	Categorical	Type of booking, assuming one of four categories:
DaysInWaitingList	Integer	Number of days the booking was in the waiting list before it was confirmed to the customer
DepositType	Categorical	Indication on if the customer made a deposit to guarantee the booking. This variable can assume 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 stay.
DistributionChannel	Categorical	Booking distribution channel. The term “TA” means “Travel Agents” and “TO” means “Tour Operators”
IsCanceled	Categorical	Value indicating if the booking was canceled (1) or not (0)
IsRepeatedGuest	Categorical	Value indicating if the booking name was from a repeated guest (1) or not (0)
LeadTime	Integer	Number of days that elapsed between the entering date of the booking into the PMS and the arrival date
MarketSegment	Categorical	Market segment designation. In categories, the term “TA” means “Travel Agents” and “TO” means “Tour Operators”
Meal	Categorical	Type of meal booked. Categories are presented in standard hospitality meal packages: Undefined/SC – no meal package; BB – Bed & Breakfast; HB – Half board (breakfast and one other meal – usually dinner); FB – Full board (breakfast, lunch and dinner)
PreviousBookingsNotCanceled	Integer	Number of previous bookings not cancelled by the customer prior to the current booking
PreviousCancellations	Integer	Number of previous bookings that were cancelled by the customer prior to the current booking
RequiredCardParkingSpaces	Integer	Number of car parking spaces required by the customer
ReservationStatus	Categorical	Reservation last status, assuming one of three categories: Canceled – booking was canceled by the customer; Check-Out – customer has checked in but already departed; No-Show – customer did not check-in and did inform the hotel of the reason why
ReservationStatusDate	Date	Date at which the last status was set. This variable can be used in conjunction with the ReservationStatus to understand when was the booking canceled or when did the customer checked-out of the hotel
ReservedRoomType	Categorical	Code of room type reserved. Code is presented instead of designation for anonymity reasons
StaysInWeekendNights	Integer	Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel
StaysInWeekNights	Integer	Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel
TotalOfSpecialRequests	Integer	Number of special requests made by the customer (e.g. twin bed or high floor)

Obviously there's a lot of features in this dataset, In this case ,I deleted some features I thought harmed the performance and generality of the model predictions , such as : Country, ReservationStatus,ReservationStatusDate,AssignedRoomType, among others.

With the main one being ReservationStatus , this feature being the clear one that can cause data leakage , and the model may reach 100 % accurate predictions with it , causing over-fitting .

Ml Models Used

4 Types of Models were used in this project. Including :

• Logistic Regression
• Decision Trees
• Random Forests (Tuned and not Tuned)
• XGBoost (Tuned and not Tuned)

Virtual Environment : Pipenv 2021.5.29

Python version: Python 3.9.7🐍

Versions/requirements used inside the virtual environment:

• xgboost 1.5.0
• scikit-learn 1.0
• gunicorn 20.1.0
• Flask 2.0.2

Before running this dockerbuild , please verify you got docker daemon running.

 $ sudo systemctl start docker

OR:

$ sudo /etc/init.d/docker start

Running this Docker image locally

To build the docker image from this project , move inside the code directory , and run the following command :

$ docker build -t zoomcamp_project .

Now, wait for it to install the pipenv dependencies, it should look like this:

Now run the docker build mapping the port 9696 to your host computer.

docker run -it --rm -p 9696:9696 zoomcamp_project

Inside another terminal , simply run:

python predict_test.py

You should see:

Deployment in the cloud | AWS Elastic Beanstalk

In this project , I used AWS Elastic Beanstalk to deploy my docker container to the cloud , I followed the steps described in zoomcamp week 5 . To run this easily , you should simply uncomment the following lines from predict_test.py

host = 'hotel-serving-env.eba-sf8r37gc.us-east-1.elasticbeanstalk.com.'
url = f'http://{host}/predict'


#url = 'http://localhost:9696/predict'

Now, simply run

python predict_test.py

Inside the code Folder, you should see the following output: