Rent Price prediction around UCSD

Data Collection

• Used rapid API for data collection Check the API for data collection

Note

If you want to duplicate the process, please create a .env file and paste the API key that you created after subscribing API

Model Development

• Please refer to model_dev/model_development.ipynb to follow through the process of model choice, variable decision, hyperparameter fine-tuning, and final development.

Note

Currently using SKLearn's Random Forest Regressor with the version of 1.3.0. It is important to use the same version in order to load the model. If not, please run model_dev/model_save.py to recreate the model with the intended version.

Limitation

Model performance is not optimal when the given number of bedroom is lower than bathroom(though it is unlikely to happen in real life)