The code here is for training, validating, and deploying an Airbnb Country Recommendation System, using an XGB Classifier and SkLearn packages.

Our Airbnb Recommendations is a machine learning library for predicting the new countries that a new user will book an airbnb in. There are a total of 10+ countries of interest, that need to be recommended given the Airbnb dataset.

1. Python 3.7 or higher
2. Python libraries: pandas, scikit-learn, matplotlib, numpy, tqdm
3. Example: Create a python environment called airbnb_env and install required libraries using pip:

• virtualenv airbnb_env
• source airbnb_env/bin/activate
• pip install --user -r requirements.txt

Download the dataset and store it within the project dir as airbnb-recruiting-new-user-bookings. Next create the dataseet. Here we will create our engineered features and process the dataset.

cd data
python make_dataset.py

Doing this, you'll output a processed baseline csv file airbnb-recruiting-new-user-bookings/train_users_2-processed.csv

cd <PROJECT_DIR>
python train_model.py

The directory structure of the Airbnb Recommendations project looks like this:

├── README.md                               <- The top-level README for developers using this project.
├── data                                    <- Scripts to download or generate data
│   ├── merge_baseline_sessions.py
│   ├── d_utils.py
│   └── make_dataset.py
├── sessions-data                           <- Scripts to preprocess and feature engineer sessions data
│   ├── create-sessions-casting+ratio-csv.py
│   ├── merge-sessions.py
│   ├── pearson-features.py
│   └── generate_session_distinct_counts_and_time_features.py
├── models                                  <- Scripts to select and evaluate model
│   ├── select_model.py
│   └── eval_model.py
├── airbnb-recruiting-new-user-bookings     <- Expected data file
│
└── train_model.py                          <- Main script