semvec/pypi-packages huggingface dataset:
smeller-california-dataset 0.2.0
California housing dataset regression model
Requires Python: >=3.13

# California Housing Dataset Regression Model
This repository contains a regression model built using the California Housing Dataset. The model is designed to predict housing prices based on various features of the dataset.

## Dataset
The California Housing Dataset is a popular dataset in machine learning, which contains information about housing prices in
California. The dataset includes features such as median income, average house age, average number of rooms, and more.

## Model
The regression model is implemented using Python and scikit-learn. It utilizes a linear regression algorithm to predict housing prices based on the features in the dataset. The model is trained on a portion of the dataset and evaluated using metrics such as Mean Absolute Error (MAE) and R-squared. 

## Usage
To use the model, you can run the `train_model.py` script, which will load the dataset, preprocess the data, train the regression model, and evaluate its performance. The script will output the evaluation metrics and can also save the trained model for future use.

## Conclusion
This project demonstrates how to build a regression model using the California Housing Dataset. It serves as a useful example for those interested in learning about regression analysis and machine learning techniques for predicting housing prices.