Project 1: predicting housing prices

Authors

Affiliation

Daffy Duck

Mickey Mouse

Technical level	Tasks
Beginner	Doing the exercises of steps 1 to 3, relying on clues and solutions if needed.
Intermediate	Doing the exercises of steps 1 to 3, without relying on clues and solutions.
Expert	Doing the exercises of steps 1 to 3, without relying on clues and solutions, and completing steps 4 (logging) and 5 (deployment).

Introduction

What is the project about? What data, what methods, what skills?

1 Structure of the project

This project has five steps (listed in the banner at the top of the page):

data generation;
data preprocessing;
model fitting and evaluation;
model logging with MLflow;
deployment.

2 Initialization of the project

2.1 Clone the project with `Git`

Attention

Pour se lancer dans le sujet, commencer par ouvrir un service Vscode-python. Pour éviter les problèmes plus tard, il faut impérativement modifier deux éléments de configuration du service:

Dans l’onglet Networking de la configuration, cocher la case “Enable a custom service port” ;
Dans l’onglet Kubernetes de la configuration, changer le rôle en admin.

Create a VScode service on SSP Cloud. In the service, open a terminal by clicking on , then Terminal > New Terminal. Clone the project repository with:

git clone https://github.com/AIML4OS/funathon-project1.git

Le projet a la structure suivante :

The .qmd files and the _quarto.yaml file are necessary to build the website ;
The file pyproject.toml describes the dependencies of the project ;
Intermediate solutions to the exercices are available in the intermediate_solutions folder;
The final solution is available in the final_solution folder;
TO BE COMPLETED DEPENDING ON THE PROJECT (Dockerfile, kubernetes…)/

2.2 Installation of dependencies

Install the project dependencies by running the following command in the terminal:

uv sync