Boston House Prices

### Context

To Explore more on Regression Algorithm

### Content

Each record in the database describes a Boston suburb or town. The data was drawn from the Boston Standard Metropolitan Statistical Area (SMSA) in 1970. The attributes are defined as follows (taken from the UCI Machine Learning Repository1): CRIM: per capita crime rate by town
2. ZN: proportion of residential land zoned for lots over 25,000 sq.ft.
3. INDUS: proportion of non-retail business acres per town
4. CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
5. NOX: nitric oxides concentration (parts per 10 million)
1https://archive.ics.uci.edu/ml/datasets/Housing
123
20.2. Load the Dataset 124
6. RM: average number of rooms per dwelling
7. AGE: proportion of owner-occupied units built prior to 1940
8. DIS: weighted distances to five Boston employment centers
9. RAD: index of accessibility to radial highways
10. TAX: full-value property-tax rate per $10,000
11. PTRATIO: pupil-teacher ratio by town 12. B: 1000(Bk−0.63)2 where Bk is the proportion of blacks by town 13. LSTAT: % lower status of the population
14. MEDV: Median value of owner-occupied homes in $1000s
We can see that the input attributes have a mixture of units.

### Acknowledgements

Thanks to Dr.Jason

### 背景
旨在深入研究回归算法。

### 内容
数据库中的每条记录均描述了波士顿的一个郊区或城镇。数据源自1970年的波士顿标准大都市统计区（SMSA）。属性定义如下（参考UCI机器学习存储库1）：
CRIM：城镇人均犯罪率
2. ZN：住宅土地中划定为超过25,000平方英尺地块的比例
3. INDUS：城镇非零售商业地块的比例
4. CHAS：查尔斯河虚拟变量（如果地块边界有河流则为1，否则为0）
5. NOX：一氧化氮浓度（每千万分之一的部分）
1https://archive.ics.uci.edu/ml/datasets/Housing
6. RM：每套住宅的平均房间数
7. AGE：1940年以前建造的业主自住单元的比例
8. DIS：到五个波士顿就业中心的加权距离
9. RAD：通往辐射高速公路的可达性指数
10. TAX：每10,000美元的全值物业税税率
11. PTRATIO：城镇的师生比例
12. B：1000(Bk−0.63)²，其中Bk是城镇的黑人比例
13. LSTAT：人口中较低社会地位的比例
14. MEDV：业主自住房屋的中值（以千美元为单位）
我们可以看到，输入属性的单位各不相同。

### 致谢
感谢Jason博士。