机器学习入门 kaggle房价预测 精讲（含代码）

Next, we’ll look at a few rows using the DataFrame.head() method.

We should have the data dictionary available in our folder for the

competition. You can also nd it here.

Here’s a brief version of what you’ll nd in the data description le:

SalePrice - the property’s sale price in dollars. This is the target

variable that you’re trying to predict.

LotShape - General shape of property

LandContour - Flatness of the property

Utilities - Type of utilities available

LotConfig - Lot conguration

ing prices, Ames, Iowa, and what we might expect to see in this dataset.

Looking at the data, we see features we expected, like YrSold (the year

the home was last sold) and SalePrice . Others we might not have an-

ticipated, such as LandSlope (the slope of the land the home is built

upon) and RoofMatl (the materials used to construct the roof). Later,

we’ll have to make decisions about how we’ll approach these and other

features.

We want to do some plotting during the exploration stage of our

project, and we’ll need to import that functionality into our environ-

ment as well. Plotting allows us to visualize the distribution of the data,

predict is often called the target variable.

We can use Series.describe() to get more information.