Introduction The aim of this competition was to predict the price of AirBnB listings in major U.S. cities.

​Instructions

1. Using the software of our choice, they asked us to build a model with the training data (train.csv) that predicts the variable "log_price"

2. Then we needed to run the model on the test dataset (test.csv) to generate predictions in the format found in the sample submission (sample_submission.csv)

Evaluation The evaluation metric for the competition was the Root Mean Squared Error (RMSE) score.

Data The training data for the competition consist of the following columns:

1. id : Record identifier
2. log_price : log(price) [The logarithm used is natural log]
3. ..... : 25 features for the listing

There are five files provided for this competition:

1. train.csv . : Training data
2. test.csv . : Test data
3. sample_submission.csv : Sample submission file
4. sample_script.py : Sample python model
5. sample_script.R : Sample R model

Submission

The submission file for the competition should consist of two columns:

1. id : Record identifier for the test observation
2. log_price : Prediction for the log(price) of the listing