This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. It's a great dataset for evaluating simple regression models. the dataset contains 18 house features plus the price ( the target ), along with 21613 observations.
The aim of this project is to predict house prices. So that the friend agency can make capital gains by buying undervalued houses and selling them thereafter.
"How much can I sell a house ?"
As a real estate agent, you will be trying to answer this question by digging the data you have been given. In fact, this is a tricky question: you want to sell it at the higher price, but you also want to sell it and nobody will buy it if it's way overpriced. Your real estate agency in King County, a county of the state of Washington in the US, has all the records needed. You will look at the features of a lot of houses and the price they were sold at. We can guess that some properties are important like a bigger house will probably be sold at a higher price than a smaller if they are both located in the same place, but perhaps there are some other factors not so obvious that might be taken into account when determining the price of a house. We want you to find those hidden factors that will allow you to make precise estimations of the right price of a house so we can make capital gains by buying undervalued houses and selling them thereafter.
King country real estate market analysis for October 2017
According to a study made in October 2017, the average sale price for King County homes is 673,628 dollars. In comparison 5 years ago the average sales price was 414,403 dollars. That is a 62 percent increase over 5 years ago.
Friend Team: email@example.com
The competition protocol was designed by Isabelle Guyon.
This challenge was generated using ChaLab.
The problem is a regression problem. Each sample (a house) is characterized by 18 features. You must predict the house price. You are given for training a data matrix X_train of dimension 12967 x 18 and an array y_train of houses prices 12967. You must train a model which predicts the price of houses for two test matrices X_valid and X_test.
Each example is characterized by the following features:
Preparing your submission with the starting kit is the easiest.
There are 2 phases:
This sample competition allows you to submit either:
The submissions are evaluated using the R-squared metric.
R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression.
The definition of R-squared is fairly straight-forward; it is the percentage of the response variable variation that is explained by a linear model. Or:
R-squared = Explained variation / Total variation
R-squared is always between 0 and 100%:
In general, the higher the R-squared, the better the model fits your data. However, there are important conditions for this guideline that I’ll talk about both in this post and my next post.
Plotting fitted values by observed values graphically illustrates different R-squared values for regression models.
The regression model on the left accounts for 38.0% of the variance while the one on the right accounts for 87.4%. The more variance that is accounted for by the regression model the closer the data points will fall to the fitted regression line. Theoretically, if a model could explain 100% of the variance, the fitted values would always equal the observed values and, therefore, all the data points would fall on the fitted regression line.
But while making your model, you need to be careful to not overfit. A model that has learned the noise instead of the signal is considered “overfit” because it fits the training dataset but has a poor fit with new datasets.
Learn More about R-squared :
Submissions must be made before the end of phase 1. You may submit 5 submissions every day and 100 in total.
This challenge is governed by the general ChaLearn contest rules.
This competition is organized solely for test purposes. No prizes will be awarded.
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Start: Oct. 22, 2017, 6:53 p.m.
Description: Development phase: create models and submit them or directly submit results on validation and/or test data; feed-back are provided on the validation set only.
Start: April 30, 2018, 6:53 p.m.
Description: Final phase: submissions from the previous phase are automatically cloned and used to compute the final score. The results on the test set will be revealed when the organizers make them available.
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