Predict the House Prices - King County

We will be working on a dataset that has sales prices of houses in King County. As a data scientist, you are given a responsibility to create a machine learning model that would predict the sales price for each house in future based on certain input variables. The target variable in this dataset is 'price' and you are given a new unseen test dataset on which you will have to predict the price of each house.

Evaluation Criteria

Submissions are evaluated on Root-Mean-Squared-Error (RMSE) between the predicted value of your model and true value of sales price on the unseen new test dataset mentioned under submission guidelines below.

Submissions are evaluated using Root-Mean-Squared-Error (RMSE). How do we do it? 

Once you generate and submit the target variable predictions on evaluation dataset, your submissions will be compared with the true values of the target variable. 

The True or Actual values of the target variable are hidden on the DPhi Practice platform so that we can evaluate your model's performance on evaluation data. Finally, a Root-Mean-Squared-Error (RMSE) for your model will be generated and displayed