3. 机器学习之实战 之 线性回归处理波士顿房价问题

3. 机器学习之实战 之 线性回归处理波士顿房价问题

波士顿房价问题

波士顿的房价为什么那么贵呢？ 可以用什么方法预测房价呢？ 这里的波士顿房价显然是连续变量，所以这个问题我们可以用回归来尝试解决。

今天我们就从最简单的线性回归来入门吧

数据加载与模型训练

波士顿房价的数据源Kaggle上也是免费开放的，Python的sklearn 数据集也可以直接load

from sklearn.datasets import load_boston
boston_dataset = load_boston()

我们一起来看一下这个数据集

print(boston_dataset.keys())
dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])print(boston_dataset.DESCR)
. _boston_dataset:Boston house prices dataset
---------------------------**Data Set Characteristics:**  :Number of Instances: 506 :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.:Attribute Information (in order):- CRIM     per capita crime rate by town- ZN       proportion of residential land zoned for lots over 25,000 sq.ft.- INDUS    proportion of non-retail business acres per town- CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)- NOX      nitric oxides concentration (parts per 10 million)- RM       average number of rooms per dwelling- AGE      proportion of owner-occupied units built prior to 1940- DIS      weighted distances to five Boston employment centres- RAD      index of accessibility to radial highways- TAX      full-value property-tax rate per $10,000- PTRATIO  pupil-teacher ratio by town- B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town- LSTAT    % lower status of the population- MEDV     Median valu