House rent price predictor

This repository contains a machine learning project aimed at predicting house rent prices based on various features. The model is created and tested in a Jupyter notebook, ensuring a thorough and interactive analysis. The final model is presented as a Voting Regressor, which consists of a Lasso, Extra Trees, Gradient Boosting Regressor, and XGBoost Regressor. Link to the web application: https://ukrainianhouserentpricepredictor.streamlit.app/

Project Structure

house_rent_price_predictor/
├── .streamlit/
│   └── config.toml                       # Configuration file to customize Streamlit application
├── data/
│   ├── clean/                            
│   │   ├── data.csv                      # Clean dataset in .csv format
│   │   └── data.joblib                   # Serialized clean dataset for use in app
│   └── raw/
│       └── house_rent_data.csv           # Raw dataset containing house rent information
├── model/
│   └── voting.joblib                     # Serialized trained model for predicting house rent prices
├── notebooks/
│   └── house_rent_price_predictor.ipynb  # Jupyter notebook for data analysis, model training, and evaluation
├── src/
│   ├── app/                              
│   │   └── main.py                       # Main web app file
│   └── model/
│       ├── interval_voting_regressor.py  # Custom interval voting regressor model
│       └── log_scaler.py                 # Custom log scaler
├── .gitignore                            # Specifies files to be ignored by Git
├── README.md                             # Project description, installation instructions, and usage guide
├── app.py                                # Entry point for web app
├── config.json                           # Сonfig file with the paths to the dataset/model files
├── config.py                             # Script for loading dataset/model files
├── requirements.txt                      # List of dependencies required to run the project application
├── requirements_dev.txt                  # List of dependencies required to run the project for development
└── setup.py                              # Defines how to build, install, and distribute for Python project 

Installation

To get started with the project, follow these steps:

1. Clone the repository:

git clone https://github.com/Vladfsociety/house_rent_price_predictor.git
cd house_rent_price_predictor

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required packages:

3.1. For run notebook:

pip install -r requirements_dev.txt

3.2. For run web app:

pip install -r requirements.txt

4. Install the project in editable mode:

pip install -e .

Usage

1. Run the Jupyter notebook:

jupyter lab

2. Run the Streamlit app:

streamlit run app.py

Ending Work

deactivate # On Windows, use `venv\Scripts\deactivate`