- 📌 Project Overview
- Project Structure
- 🔮 Future Plans
- 🎓 Learned
- 🤝 Contributing
- 👤 Author
- 📌 Contact
- 🪪 License
- Author Name: Althaf Muhammad
- Author Contact: Email and Github
- Dataset Source: Kaggle - Housing Price Prediction Data
- Project Name: House Price Prediction
- Objective: Build a Machine Learning model to predict house prices.
- Training Strategy: Batch Training
- Type of ML: Supervised Learning
- Type of Problem: Regression
-
Evaluation Metrics: RMSE, MAE, and
$R^2$ Score - Python Version: 3.12.10
-
Python Dependency Manager:
uv(v0.7.2) - Hardware: No hardware limitations. This is a very lightweight and simple project that is runnable on any modern machine, including local setups, Google Colab, and Kaggle.
- OS: This project was initially developed on a Debian Devcontainer machine. I tried to maintain this project OS independent. But there is no guarantee that it is.
-
Project Configuration and Dependencies: Listed in
pyproject.toml
house-price-pred-ml/ # Root project directory
├── .devcontainer/ # Dev Container directory
│ ├── devcontainer.env # Dev Container environment variables file passed to Docker
│ ├── devcontainer.json # Dev Container configuration file
│ ├── Dockerfile # Dockerfile used to build the Dev Container
│ └── postCreateCommand.sh # Shell script run after Dev Container build completes
├── .git/ # Git version control metadata directory
├── .venv/ # Local Python virtual environment directory
├── data/ # Directory used to store all dataset-related files
│ ├── interim/ # Directory for intermediate datasets processed from raw data
│ │ └── interim.csv # CSV file containing intermediate processed data
│ ├── processed/ # Directory for storing train-test split datasets
│ │ ├── test.csv # CSV file containing the test split of the dataset
│ │ └── train.csv # CSV file containing the train split of the dataset
│ └── raw/ # Directory for unprocessed raw data files
│ └── raw.csv # CSV file containing raw dataset
├── logs/ # Directory for storing log files generated during execution
├── notebooks/ # Directory used for storing Jupyter notebooks for experimentation
│ └── 1-althaf07-experimentation.ipynb # Notebook for initial experimentation and data analysis
├── reports/ # Directory for project reports and documentation
│ ├── figures/ # Directory for saving generated figures and plots
│ │ └── univariate/ # Directory for univariate analysis figures
│ │ ├── bathrooms.png
│ │ ├── bedrooms.png
│ │ ├── neighborhood.png
│ │ ├── numc_describe.md
│ │ ├── price.png
│ │ ├── square_feet.png
│ │ └── year_built.png
│ ├── environment.md # Markdown file documenting the environment setup
│ └── experiment_document.md # Markdown file detailing experiment results and analysis
├── src/ # Source code root directory
│ └── house_price_pred_ml/ # Main package containing all project modules
│ ├── plot/ # Plotting utilities module
│ │ └── univariate/ # Module for univariate plot functions
│ │ ├── cat_numd.py # Plots for categorical vs numerical data distributions
│ │ └── numc.py # Plots for numerical data distributions
│ ├── __init__.py # Initializes the house_price_pred_ml Python package
│ ├── api.py # Defines API endpoints for model serving
│ ├── auto_gen_table.py # Automatically generates data summary tables
│ ├── config.py # Handles configuration loading logic
│ ├── config.yaml # YAML configuration file for model parameters and settings
│ ├── evaluate.py # Evaluates model performance metrics
│ ├── predict.py # Generates predictions using the trained model
│ ├── process_data.py # Processes raw data into cleaned, usable format
│ ├── split_data.py # Splits dataset into training and testing sets
│ ├── train.py # Trains the machine learning model
│ ├── tree.py # Implements tree-based model utilities
│ └── utils.py # Utility functions for data processing and evaluation
├── tmp/ # Temporary directory for intermediate or disposable files
├── .gitignore # Specifies files and directories ignored by Git
├── .pre-commit-config.yaml # Configuration for pre-commit hooks to enforce code standards
├── .python-version # Specifies the Python version used in the project
├── Dockerfile # Dockerfile for building the project container for deployment
├── pyproject.toml # Configuration file for Python dependencies and build system
├── README.md # Main project documentation and usage instructions
└── uv.lock # Lock file for managing UV-based dependencies