# Delhi House Price Prediction — Data

来源：https://www.kaggle.com/competitions/delhihousepriceprediction/data

本地 Meta Kaggle 快照；不是本次实时页面。

# Dataset Description

The data captures house prices for Indore and other important attributes. The aim is to predict the median house resale price for the test data.

Participants should be able to answer questions like these after reading the data description: What files do I need? What should I expect the data format to be? What am I predicting? What acronyms will I encounter?


File descriptions

IndoreHP_train.csv - the training set

IndoreHP_test.csv - the test set

IndoreHP_Sample.csv - a sample submission file in the correct format; but the predicted valued are set at 18 for all houses...i.e, incorrect.. :)

Data fields:

CRIM: per capita crime rate

ZN: the proportion of residential land zoned for lots larger than 25,000 sq.ft.

INDUS: the proportion of non-retail business acres per town.

RIVER_FLG: River Dummy Var (this is equal to 1 if tract bounds river; 0 otherwise)

nitric oxides concentration: the nitric oxides concentration (parts per 10 million)

rooms/dwelling: the average number of rooms per dwelling

AGE: the proportion of owner-occupied units built prior to 1940

DIS: the weighted distances to five employment centers of the town

RAD: the index of accessibility to radial highways

TAX: the full-value property-tax rate per INR 10 millions (1 crores)

PTRATIO: the pupil-teacher ratio by area

B: calculated as 1000(Bk — 0.63)², where Bk is the proportion of people living is slums in the area

LSTAT: This is the percentage lower status and income of the population

MEDV: This is the median value of owner-occupied homes in INR millions (10 lakhs)