Housing Prices Challenge

Overview

Buying a house is one of the biggest decisions in life — and one of the most data-driven. In this challenge, your task is to build a regression model that accurately predicts the final sale price of a house based on various features like:

• Living area
• Number of bedrooms/bathrooms
• Location ...and many more.

This is a beginner-friendly machine learning challenge designed to help participants practice the full data science pipeline — from data exploration and cleaning to training powerful predictive models.

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Practice Skills

In this challenge, you will gain hands-on experience with:

• Python for data analysis
• Creative feature engineering
• Advanced regression techniques like:
  • Random Forest
  • Gradient Boosting (e.g., XGBoost)

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Evaluation

Goal

Your objective is to predict the sale price for each house in the test set. For each house_index in the test data, predict the corresponding price.

Metric

Submissions will be evaluated using the Root Mean Squared Error (RMSE).

Lower RMSE values indicate better model performance.

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Submission Format

Submission guide

Your final submission file must be a CSV file with the following structure:

house_index	price
35000	10000000
35001	200000
35002	150000

• Include the header row.
• Make sure the house_index matches the house_index values from the test dataset.
• The price should be your predicted sale price.

The challenge includes two types of submissions:

Public Submission

• Use the public testing dataset (provided in the Data section) to predict your submission file.

Private Submission

• You must submit your model.
• It will be evaluated on a public testing dataset.
• Important: Your code must recursively scan the entire data directory (Code section for details).