Description People interested in renting an apartment or home, share information about themselves…

Description People interested in renting an apartment or home, share information about themselves and their property on Airbnb. Those who end up renting the property share their experiences through reviews. The dataset describes property, host, and reviews for over 40,000 Airbnb rentals in New York along 90 variables.* Goal Construct a model using the dataset supplied and use it to predict the price of a set of Airbnb rentals included in scoringData.csv. Metric Submissions will be evaluated based on RMSE (root mean squared error) (Wikipedia). Lower the RMSE, better the model. Submission
Description People interested in renting an apartment or home, share information about themselves and their property on Airbnb. Those who end up renting the property share their experiences through reviews. The dataset describes property, host, and reviews for over 40,000 Airbnb rentals in New York along 90 variables.* Goal Construct a model using the dataset supplied and use it to predict the price of a set of Airbnb rentals included in scoringData.csv. Metric Submissions will be evaluated based on RMSE (root mean squared error) (Wikipedia). Lower the RMSE, better the model. Submission File The submission file should be in text format (.csv) with only two columns, id and price. The price column must contain predicted price. Number of decimal places to use is up to you. The file should contain a header and have the following format: “id”,”price” 25850, XXXXXXXXXX26433, XXXXXXXXXX26588, XXXXXXXXXX30272, XXXXXXXXXX34712, XXXXXXXXXX An example of the sample submission file (sample_submission.csv) is shared with the set of files. Sample Code Here is an illustration in R of how you can create a model, apply it to scoringData.csv and prepare a submission file (sample_submission.csv). # For the following code to work, ensure analysisData.csv and scoringData.csv are in your working directory. # Read data and construct a simple model data = read.csv('analysisData.csv') model = lm(price~minimum_nights+review_scores_accuracy,data) # Read scoring data and apply model to generate predictions scoringData = read.csv('scoringData.csv') pred = predict(model,newdata=scoringData) # Construct submission from predictions submissionFile = data.frame(id = scoringData$id, price = pred) write.csv(submissionFile, 'sample_submission.csv',row.names = F)

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