California Housing Price Prediction

We are trying to predict housing prices in california. The dataset is from Kaggle. The chosen Data science lifecycle is CRISP-DM.

📖 Table of Contents

1. Prerequisites
2. ⚙️ Setup Guide
   • Create a Virtual Environment
   • Install Dependencies
   • Run the Project
3. Project Organization
4. Results
5. For Contributors: Configuring Git for Jupyter Notebooks

Prerequisites

Project works on:

• Python 3.13.2
• pip 25.01

⚙️ Setup Guide

After you have cloned the repo and are in the root of the project run the following commands:

1️⃣ Create a Virtual Environment

python3 -m venv venv
source venv/bin/activate  # macOS/Linux
venv\Scripts\activate     # Windows

2️⃣ Install Dependencies

pip install -r requirements.txt

Run the project

• The whole documentation and code can be found in notebooks/california_houseprices_crispdm.ipynb.

Project Organization

For the project structure we used Cookie-Cutter. Since this is a simple ML-project and not a whole automated ML-System with pipelines, we just make use of the notebooks folder where you can find the code and the data folder, where our data is stored.

├── Makefile           <- Makefile with convenience commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- NOT USED
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── pyproject.toml     <- Project configuration file with package metadata for 
│                         california_housing_price_prediction and configuration for tools like black
│
├── references         <- NOT USED
│
├── reports            <- NOT USED
│   └── figures        <- NOT USED
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.cfg          <- Configuration file for flake8
│
└── california_housing_price_prediction   <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes california_housing_price_prediction a Python module
    │
    ├── config.py               <- NOT USED
    │
    ├── dataset.py              <- NOT USED
    │
    ├── features.py             <- NOT USED
    │
    ├── modeling                
    │   ├── __init__.py 
    │   ├── predict.py          <- NOT USED
    │   └── train.py            <- NOT USED
    │
    └── plots.py                <- NOT USED

---

Results

Results of our best Model:

Model	R²	RMSE	MAE
XGBoost	0.839152	45910.471093	30029.313347

For Contributors: Configuring Git for Jupyter Notebooks

Enable Automatic Output Stripping for Notebooks to prevent unnecessary notebook output changes in Git commits:

pre-commit install