Build an ML Pipeline for Short-Term Rental Prices in NYC

This project aims to build a machine learning pipeline to predict short-term rental prices in New York City. The pipeline is built using MLflow and the model is trained on a dataset of rental listings.

Table of Contents

• Getting Started
  • Prerequisites
  • Installation
• Usage
  • Running with Python
  • Running with MLflow
• Wandb Project
• Contributing
• License

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

Please ensure you have the following software installed on your system:

• Python 3.7+
• Conda

The specific Python packages required are listed in the environment.yml file.

Installation

1. Clone the repository:

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

2. Navigate to the project directory:

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

3. Create a new Conda environment:

   conda env create -f environment.yml

4. Activate the new environment:

   conda activate nyc_airbnb_dev

5. Follow the instructions in installation.txt to complete the setup.

Usage

Running with Python

To run the ML pipeline, execute the following command:

python main.py

Running with MLflow

To run the ML pipeline with MLflow, first ensure that the MLflow server is running. You can start the server with the following command:

mlflow server

Then, you can run the pipeline with the following command:

mlflow run . -P steps=test_regression_model

Replace test_regression_model with the name of the step you want to run.

(OR)

You can run the release using mlflow without any other pre-requisite. We will train the model on a new sample of data that is (sample2.csv):

mlflow run https://github.com/AndrewAungKo/build-ml-pipeline-for-short-term-rental-prices.git \
         -v 1.0.1 \
         -P hydra_options="etl.sample='sample2.csv'"

Wandb Project

You can view the project on Wandb here.

Contributing

• see CODEOWNERS

License

• see the LICENSE.txt file for details

Code Owners

The code owners for this repository are @udacity/active-public-content. They will be automatically requested for review when you open a pull request.