1. AirBnB Pricing Predictions
David Seamon

2. Executive Summary
Goal of creating a model that can allow potential AirBnB hosts to estimate how much their property is worth as a short-term rental
Developed a k-nearest neighbor baseline to be compared against
Performed feature vector selection using linear regression and the k-nearest neighbor model
Developed and optimized a multi-layer perceptron
The MLP was able to outperform the k-nearest neighbor model, but didn’t perform well enough to be a useful model

3. Approach Data taken from Inside AirBnB
92 feature vectors  narrowed down to nine
Removed any entries that has a blank as one of the nine selected vectors
Program developed with the help of Scikit-Learn
KNeighborsRegresor
MLPRegressor
Used the Comp Sci computers to run the program

4. Results
KNN RMSE:
MLP RMSE:

5. Discussion MLP outperforms the KNN model (59.8472 to 68.8693)
Not good enough to be used as an accurate model
$ is a large average error considering prices are often in the $ range
Only size data was used
Location data is difficult to quantify, but is likely a large reason why the model is not very accurate
Ran into issues with not having enough quality feature vectors
Felt good starting out, but during feature vector selection the data wasn’t as useful as anticipated