Pricing a home depends on many things at once: its size, age, number of rooms, and location. A regression model can learn how those features relate to price from past sales, then estimate the price of a house it has never seen. It is the classic first prediction project, and it teaches the full workflow you will reuse in every later project.
The lessons start with a project introduction and the tools involved, then move to data cleaning. Next comes a full end-to-end build that explores the data, engineers features, and compares a linear regression with a random forest. The last lesson is a case study on the well-known Ames, Iowa housing dataset.
Watch the lessons in order and code along in a Jupyter notebook. If a video runs long, treat it as a reference you can dip back into while you build your own version.
A short tour of the full house price project: what problem it solves and the tools used, including Python, NumPy, Pandas, Matplotlib, scikit-learn and Jupyter.
Video 2minLoad a messy real-estate dataset and clean it with Pandas: drop unneeded columns, handle missing values, and convert raw fields into usable numbers.
Video 15minA complete walkthrough of the data science process: loading and exploring the data, preprocessing, feature engineering, then training and comparing a linear regression and a random forest model.
Video 40minA case study on the Kaggle House Prices dataset using NumPy, pandas and scikit-learn, showing how data cleaning and feature generation lead to better predictions.
Video 1h 43min