Project Motivation / Definition:

This project touches on four different topics that are important to enabling robust predictive models from the AirBnB dataset - these topics include:

1. Data Exploration and Data Pre-processing using CRISP-DM as a framework
2. Describe the process used to transform datasets to a form useful for predictive modeling and review the use of data visualization used to gain business understanding and to elucidate correlations
3. Review constructing a neural net to perform AirBnB price predictions
4. Explore Whether or not using language embeddings from a pre-trained model in-place of categorical variables enables price prediction at least as well as using randomly initialized feature embeddings

Libraries used during this analysis include: torch, fastai, numpy, matplotlib, collections, pandas, seaborn, ranger, fasttext, re

Results Summary:

Data processing enabled successful price predictions with a exp-MSE of 0.067. Furthermore, the use of pre-trained embedddings in place of categorical variables enabled 60% faster training while still maintaining high predictive power.

Files: AirBnB-price-prediction.ipynb - provides a detailed look at all analyses performed on the AirBnB datasets.

Note: a full description of processes used during this analysis is available on Medium at https://medium.com/@brad.evan.davis/time-series-prediction-using-deep-learning-and-language-embeddings-e482ab9cd109?source=friends_link&sk=7a5d11d0e0e6bd767e654fe31b1eceb3