Linear Regression on California Housing Data
Marie Gondeck

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Introduction
This repository contains one of my first projects, completed during a basic Python course at university. It explores linear regression analysis using the California housing dataset to predict median house values in various districts during the 1990s.

Project Structure
Linear_regression_california_housing.ipynb: Jupyter notebook containing the full analysis pipeline, from data loading to visualization and model evaluation.

Data
The dataset comprises 20,640 entries, each with 8 features describing aspects of housing in California, such as average number of rooms and median income of the area. The target variable is the median house value, scaled to hundreds of thousands of dollars (MedHouseVal).

Usage
To run the notebook, ensure you have Python installed along with the packages: matplotlib, seaborn, pandas, numpy, scikit-learn

You can install all necessary libraries using the provided requirements.txt file:

pyenv local 3.11.3
python -m venv .venv
source .venv/bin/activate 
pip install -U pip
pip install -r requirements.txt  

Then run:

jupyterlab