README.md

Immo Prediction together with 🦀 Charlie 🦀

🏢 Description

My first Machine Learning project. Exciting! I use a dataset of houses that we scraped from the internet and in this repo I will apply Linear Regression together with Charlie 🦀 to predict the price of a house based on its features.

📦 Repo structure

├── assets  # some images
├── data
│   ├── external_data
│   │   ├── HouseholdIncome.xlsx
│   │   ├── PopDensity.xlsx
│   │   ├── PropertyValue.xlsx  
│   │   ├── REFNIS_CODES.geojson  # download this file!! Look below for more info
│   │   └── REFNIS_Mapping.xlsx  
│   ├── intermediate
│   │   └── joined_data.csv  # joining external data with the scraped data
│   └── raw
│       └── data.csv
├── MODELCARD.md
├── models  # the trained models
│   ├── basic_linearregression.pkl
│   ├── linearregression_log10.pkl
│   └── random_forest.pkl
├── README.md
├── requirements.txt
└── src
    ├── config.py
    ├── features  # building and transforming features
    │   ├── build_features.py
    ├   ├── pipeline.py
    │   └── transformers.py
    ├── models  # training the models and some model utils
    │   ├── model_utils.py
    │   ├── train_basic_linearregression.py
    │   ├── train_linearregression_log10.py
    │   └── train_random_forest.py
    └── utils.py  # generic utils

🚀 To retrain a model

install requirements

Before charlie can predict the price of a house, we need to install the requirements.

pip install -r requirements.txt

OPTIONAL: Update external data

If you want to update the external data, you can download the latest data from the following links: Go to statbel.fgov.be to download the latest geojson (ZIP), extract the file and copy the sh_statbel_statistical_sectors_31370_20230101.geojson file and copy it to ./data/external_data/REFNIS_2023.geojson and run the following command in the terminal:

cd src # move to the src folder
python join_external_data.py

Train a model

Now Charlie is all set and ready to be trained. To train a model, run the following command in the terminal:

cd src # move to the src folder

# train a model
python ./models/train_basic_linearregression.py
# or
python ./models/train_linearregression_log10.py
# or
python ./models/train_random_forest.py

Charlie will print an R-squared score and save the model in the models folder with a similar name as the train_model.py file.

Screenshot

Basic Linear regression model

Advanced Linear regression model

Random Forest model

More data about all the above models

⏱️ Timeline

This project was done in 4 days including studying the theory and implementing the code.

📌 Personal Situation

This project was done as part of my AI trainee program at BeCode.

📚 Credits

Thank you at Bear Revels for providing the external datasets, which boosted my scores!

Connect with me!