##**Overview**

The learning goal of ADAMS is to familiarize you with recent developments in deep learning and to enable you to use the corresponding methodology for solving real-world problems. In accordance with this objective, the evaluation of the module is based on an assignment.

Your submission will consist of a Jupyter notebook that includes all steps that we typically encounter in a machine learning pipeline (explanatory data analysis, data preparation, parameter tuning, etc.). Grading of the notebook will be based on several criteria including:
a) the quality of the exposition (e.g., a healthy mix of prose, code, comments, math. formalism)
b) the organization of the notebook
c) the scope of your analysis
d) whether you have solved the task in a sound and rigorous manner

The final assignment is hosted as a Kaggle in-class competition. The main purpose of this setting is to give you the possibility to interactively query the quality of your modeling pipeline by submitting the model predictions to the online leaderboard. You may submit up to 5 entries daily to track the quality of your solution on unseen test data. Please remember that the actual accuracy of your solution is not the main factor that will determine your grade.

##**Modeling task**
Airbnb is an online marketplace for accommodation rental founded in 2008. Properties hosted on the Airbnb platform are owned by private companies and individuals. The hosts determine the rental price themselves and pay a service fee to Airbnb.

In this assignment, you will predict property prices for Airbnb listings in London. You will be provided with data on more than 55.000 properties containing the listing attributes (e.g., location, number of rooms, property type), texts with the listing description, and rental price per night.

Using a broad range of NLP techniques, you will need to build an NN-based model that predicts the price of listings in the test set based on both text and tabular data. If you wish to further refine the accuracy of your model, you will have access to additional data containing the listing reviews.

Please refer to other sections of the competition page to download the data and read more about competition rules, evaluation and data description.

##**Deadline**
The submission deadline is the 2th of September 2023