About the Competition

This competition is designed to help participants learn and apply regression techniques using a straightforward dataset related to apartment prices. The dataset contains information about various features that influence the price of an apartment, such as the number of rooms, floor level, and building type. By participating, you will gain experience in predictive modeling, data visualization, and exploratory data analysis (EDA).

The Learning Objective

The primary goal of this competition is to help participants at the entry level understand the process of predicting continuous variables (apartment prices in this case) by applying regression models. Participants are encouraged to explore the data through EDA to uncover relationships between features and the target variable, which will help them build more accurate models.

Why Participate?

This competition is ideal for beginners who want to:

- Learn how to apply linear regression and other regression techniques.
- Gain experience in model tuning.
- Practice visualizing data using common tools like Matplotlib or Seaborn.
- Understand the process of building a machine learning model from start to finish.
![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22215069%2Fc7a48abbcf16585bc1730f7fb815cd15%2Fvis_data.jpg?generation=1728988081350051&alt=media)

What to Expect

Participants will be provided with a dataset containing 11,884 apartments, each described by features like the number of rooms, the district, the year of construction, and the apartment’s total area. Your task is to develop a model that can accurately predict the price of apartments based on these features.

This is a great opportunity for those new to data science or machine learning to practice their skills and learn through hands-on experience. We encourage participants to share their notebooks and insights as part of the learning experience!

Feel free to modify or expand this description as needed!