House price predictor

The problem

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Estimate housing values from the California Housing Dataset and explore how its features relate to prices. This is a machine learning study, not a commercial property valuation tool.

How it works

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• Python and scikit-learn to load data and split training and test sets.
• Linear regression as a baseline; Ridge, cross-validation and GridSearchCV in the enhanced version.
• RMSE to evaluate predictions on the test set.
• Matplotlib and Seaborn to visualize data features and compare actual and predicted values.

Run it

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With Python 3.9 or later (for the repository’s pinned dependencies), in a virtual environment:

git clone https://github.com/rogeriojunior31/house_price_predictor.git
cd house_price_predictor
python -m venv .venv
# Linux/macOS; on Windows, use .venv\Scripts\activate
source .venv/bin/activate
pip install -r requirements.txt
python main.py

Running the script produces RMSE and analysis plots. The initial dataset download may require internet access. Results depend on the run; no metric published here establishes performance in other housing markets.

View code and instructions on GitHub