This repository is my project for the Check24 coding challenge. Copy right of the data (hotels.csv and offers.csv) belongs to Check24. More details could be found here.
To run my app, you'll need the following:
- Python3/Jupter-Notebook
- Pandas
- Docker
- Java 17
- Maven
Note that the java version when you run
mvn -vshould also be 17 or later version
- Node js
I have cleaned and adapted the given data to optimize the performance of my code. See Data for more Details. The actual data.csv file in holiday-backend/src/main/resources/data/ contains only 700000 rows. For starters, you can just run the program without changing anything. Or clean your data like the following:
-
Use the given jupyter-notebook script to clean your data.
-
Alternatively, you can download the cleaned version here.
Replace data.csv file in holiday-backend/src/main/resources/data/
In holiday-backend folder, run:
-
mvn clean installto install the jar file. -
docker-compose up --buildto build docker container and run backend.
If you made any changes inside backend or replace the data, rerun those commands.
In holiday-frontend folder, run:
npm ito install all packages.npm run devto start the project.
The original offer dataset looks like so:
| hotelid | outbounddeparturedatetime | inbounddeparturedatetime | countadults | countchildren | price | inbounddepartureairport | inboundarrivalairport | inboundarrivaldatetime | outbounddepartureairport | outboundarrivalairport | outboundarrivaldatetime | mealtype | oceanview | roomtype |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 90 | 2022-10-05T09:30:00+02:00 | 2022-10-12T08:35:00+02:00 | 1 | 1 | 1243 | PMI | DUS | 2022-10-12T14:40:00+02:00 | DUS | PMI | 2022-10-05T14:25:00+02:00 | halfboard | FALSE | double |
| 1096 | 2022-07-01T11:00:00+02:00 | 2022-07-08T07:30:00+02:00 | 2 | 0 | 1710 | PMI | LEJ | 2022-07-08T10:00:00+02:00 | LEJ | PMI | 2022-07-01T13:30:00+02:00 | none | FALSE | apartment |
| 656 | 2022-09-13T15:50:00+02:00 | 2022-09-21T07:50:00+02:00 | 2 | 0 | 2093 | PMI | FRA | 2022-09-21T10:10:00+02:00 | FRA | PMI | 2022-09-13T17:55:00+02:00 | breakfast | FALSE | double |
| 1880 | 2022-08-07T18:00:00+02:00 | 2022-08-12T08:20:00+02:00 | 2 | 0 | 1707 | PMI | MUC | 2022-08-12T10:35:00+02:00 | MUC | PMI | 2022-08-07T20:15:00+02:00 | none | FALSE | double |
| 1190 | 2022-09-20T17:35:00+02:00 | 2022-09-26T07:20:00+02:00 | 2 | 0 | 1866 | PMI | SCN | 2022-09-26T09:30:00+02:00 | SCN | PMI | 2022-09-20T19:40:00+02:00 | breakfast | FALSE | juniorsuite |
Since all the flights are one-way to Mallorca (PMI), I found the following three columns redundant and removed them:
1. inbounddepartureairport 2. outboundarrivalairport 3. inboundarrivalairport
Additionally, I merged the following three columns into a single column to reduce file size and improve performance:
1. mealtype 2. roomtype 3. oceanview
| mealtype | decoded-value | roomtype | decoded-value | oceanview | decoded-value |
|---|---|---|---|---|---|
| ACCORDINGDESCRIPTION | A | ACCORDINGDESCRIPTION | A | FALSE | F |
| ALLINCLUSIVE | B | APARTMENT | B | TRUE | T |
| ALLINCLUSIVEPLUS | C | BUNGALOW | C | ||
| BREAKFAST | D | DELUXE | D | ||
| FULLBOARD | E | DOUBLE | E | ||
| FULLBOARDPLUS | F | ECONOMY | F | ||
| HALFBOARD | G | FAMILY | G | ||
| HALFBOARDPLUS | H | FOURBEDROOM | H | ||
| NONE | I | HOLIDAYHOUSE | I | ||
| JUNIORSUITE | K | ||||
| MULTISHARE | L | ||||
| SINGLE | M | ||||
| STUDIO | N | ||||
| SUITE | O | ||||
| SUPERIOR | P | ||||
| TRIPLE | Q | ||||
| TWINROOM | R | ||||
| VILLA | S |
The column called mtr, which now contains the decoded value that was previously stored across the three columns: mealtype, roomtype, and oceanview. By consolidating this information into a single column, we have reduced the file size and improved overall performance.
| mtr | example |
|---|---|
| AAT | ACCORDINGDESCRIPTION + APARTMENT + TRUE |
| ... | ... |
By merging redundant columns and optimizing the data structure, the size of the CSV file has been significantly reduced. The file size has been reduced from around 20GB to 14GB, while preserving all the necessary information. This reduction in file size helps to save storage space and potentially improves the performance of data operations.
I used Spring Boot and the Model-View-Controller (MVC) architectural pattern to build an API service that supports HTTP methods such as GET, POST, and DELETE. The frontend client interacts with the Controller, which handles the user requests and responses. The Service layer contains all the business logic of the backend application, while the Repository layer interacts directly with the database to perform data operations.
Inspired by the default frontend, I developed a ReactJS application. By utilizing Material-UI (MUI), I was able to create a modern and responsive user interface (UI). I took advantage of the existing codebase and reused certain components and functions. Additionally, I enhanced these components by incorporating attributes such as BorderRadius and BoxShadow, which improved the visual appeal and added visual effects to elements within the UI.
My web application has the following features:
- Users can search for hotels that match their query.
- Users can see the offers available at a - chosen hotel.
- Users can add offers to their cart.
- Users can view and remove offers from their cart.