Overview
Today, you'll apply what you've learned to a real-world problem: predicting house prices based on various features.
This is a classic regression problem and a great way to practice the skills you've acquired so far.
Key Concepts
- End-to-end machine learning workflow
- Feature selection and engineering
- Model selection and evaluation
- Hyperparameter tuning
- Interpreting model results
Practice Exercise
Exercise: Kaggle House Prices Competition
Participate in the Kaggle House Prices competition:
- Explore and clean the dataset
- Perform feature engineering to create useful features
- Build and evaluate multiple regression models
- Fine-tune your best model
- Make a submission to the Kaggle competition
Resources
Kaggle House Prices Competition
Main resource for today
Top Notebooks for House Prices
Learn from top-performing solutions
Feature Engineering for House Prices
Comprehensive guide to feature engineering for this dataset
Stacked Models for House Prices
Advanced technique using model stacking
Complete Today's Task
Mark today's task as complete to track your progress and earn achievements.