This repository contains a ML pipeline for short-term rental prices in NYC.

• GitHub repository: https://github.com/yuichielectric/build-ml-pipeline-for-short-term-rental-prices.git
• W&B project: https://wandb.ai/yuichielectric/nyc_airbnb/overview

This pipeline has the following steps:

• Data download (download)
• Basic cleaning (basic_cleaning)
• Data testing (data_check)
• Data splitting (data_split)
• Train Random Forest (train_random_forest)
• Test Random Forest (test_regression_model)

Clone this repository:

git clone https://github.com/yuichielectric/build-ml-pipeline-for-short-term-rental-prices.git

and go into the repository:

cd build-ml-pipeline-for-short-term-rental-prices

Make sure to have conda installed and ready, then create a new environment using the environment.yml file provided in the root of the repository and activate it:

> conda env create -f environment.yml
> conda activate nyc_airbnb_dev

Let's make sure we are logged in to Weights & Biases. Get your API key from W&B by going to https://wandb.ai/authorize and click on the + icon (copy to clipboard), then paste your key into this command:

> wandb login [your API key]

You should see a message similar to:

wandb: Appending key for api.wandb.ai to your netrc file: /home/[your username]/.netrc

As usual, the parameters controlling the pipeline are defined in the config.yaml file defined in the root of the starter kit. We will use Hydra to manage this configuration file. Open this file and get familiar with its content. Remember: this file is only read by the main.py script (i.e., the pipeline) and its content is available with the go function in main.py as the config dictionary. For example, the name of the project is contained in the project_name key under the main section in the configuration file. It can be accessed from the go function as config["main"]["project_name"].

NOTE: do NOT hardcode any parameter when writing the pipeline. All the parameters should be accessed from the configuration file.

In order to run the pipeline when you are developing, you need to be in the root of the starter kit, then you can execute as usual:

>  mlflow run .

This will run the entire pipeline.

When developing it is useful to be able to run one step at the time. Say you want to run only the download step. The main.py is written so that the steps are defined at the top of the file, in the _steps list, and can be selected by using the steps parameter on the command line:

> mlflow run . -P steps=download

If you want to run the download and the basic_cleaning steps, you can similarly do:

> mlflow run . -P steps=download,basic_cleaning

You can override any other parameter in the configuration file using the Hydra syntax, by providing it as a hydra_options parameter. For example, say that we want to set the parameter modeling -> random_forest -> n_estimators to 10 and etl->min_price to 50:

> mlflow run . \
  -P steps=download,basic_cleaning \
  -P hydra_options="modeling.random_forest.n_estimators=10 etl.min_price=50"

License