Machine Learning aplication created for House Price Kaggle competition as part of my final Big Data Master degree project.
Model prediction with Gradient Boosting and Grid Search for Hyperparameter tuning in HousePriceModel.py.
Implemented Kaggle API functionality for Python at kaggleMethods.py.
Join House Price Competition as a team: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/overview/tutorials
Create New API Token in your Kaggle Account Settings and download kaggle JSON file. Open it, copy username and key values and place them into kaggleMethods.py autUser dictionary to set them as Kaggle enviroment variables.
autUSer = { 'username': 'JSON username' , 'key: 'JSON key' }
Alternative: You can add this JSON file into your '/Users/User/.kaggle' directory after installing Kaggle library and delete autUser and os.environ lines in kaggleMethods.py to keep your credentials private.
Install modules with pip at project work directory.
pip install -r requirements.txt
Execute via command line defineModel.py to create Gradient Boosting Model with test and train data of House Price Kaggle competition.
python defineModel.py example_submission.csv
Alternative: Before executing defineModel.py you can download data competition files manually and place them into Kaggle_data folder.
Execute via command line makeSubmission.py to submit your predictions generated at Submissions folder.
python makeSubmission.py example_submission.csv