# Description

The "Become a Kaggle Master" course is an essential component of the IASD Master 2 program, offered in collaboration among Université Paris Dauphine, ENS, and Mines ParisTech. This course introduces students to Kaggle competitions, which are recognized globally for their challenging, practical data science problems.

## The Dataset

In this assignment, students will work with a modified version of the Ames Housing dataset. The dataset contains 79 variables describing various attributes of residential homes, such as the year built, lot size, number of rooms, material quality, and house condition. This rich variety of data types offers a realistic challenge similar to those encountered in professional data science roles.

## The Problem

Students are tasked with developing a model to predict house prices based on the dataset's extensive features. This predictive modeling exercise requires understanding the complex interactions between various house attributes and their impact on pricing, which is vital for real-world real estate valuation.

## Learning Objectives

Key learning outcomes of this competition include:

- Practical experience with data cleaning and preprocessing.
- Data exploration and visualization to identify patterns.
- Building and tuning machine learning models using Python libraries such as scikit-learn and TensorFlow.
- Evaluating model performance using metrics like the Root Mean Square Error (RMSE).

## Submission and Evaluation

Submissions will be evaluated on a provided test set, with the RMSE metric determining performance. The goal for participants is to achieve the lowest possible RMSE, indicating high accuracy in their price predictions.

## Conclusion

This competition is designed to provide foundational skills in data science, preparing students to address complex problems through data-driven decision making and predictive modeling in their future careers.