Understanding Boston Dataset

These datasets are pre-build datasets in sklearn. To load and return the boston house-prices dataset (regression).

• Samples total – 506
• Dimensionality – 13
• Features – real, positive
• Targets – real 5. – 50.

Description of Boston Dataset in Sklearn

The Boston Housing dataset contains several columns that are used to describe various aspects of residential homes in Boston. Here is a description of each column in the dataset:

1. CRIM: Per capita crime rate by town. It indicates the level of crime in the area.
2. ZN: Proportion of residential land zoned for lots over 25,000 sq.ft. This feature reflects the area’s residential density.
3. INDUS: Proportion of non-retail business acres per town. This is an indicator of the commercial use of land away from residential areas.
4. CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise). This indicates whether the property is near the Charles River, which may add to the aesthetic value of the neighborhood.
5. NOX: Nitric oxides concentration (parts per 10 million). It represents the level of industrial pollutants in the area.
6. RM: Average number of rooms per dwelling. More rooms typically indicate more spacious accommodation.
7. AGE: Proportion of owner-occupied units built prior to 1940. Older structures might lack newer amenities or could be considered more prestigious depending on the architecture and condition.
8. DIS: Weighted distances to five Boston employment centres. This feature measures the accessibility to workplaces, which can influence housing prices.
9. RAD: Index of accessibility to radial highways. Higher values indicate easier access to major roadways.
10. TAX: Full-value property-tax rate per $10,000. This reflects the annual property tax rate.
11. PTRATIO: Pupil-teacher ratio by town. Lower values typically indicate better educational facilities, which is a significant factor for families when choosing a home.

In this article, we are going to see how to use Boston Datasets using Sklearn.

The Boston Housing dataset, one of the most widely recognized datasets in the field of machine learning, is a collection of data derived from the Boston Standard Metropolitan Statistical Area (SMSA) in the 1970s. This dataset is commonly used in regression analysis to predict the median value of homes in the Boston area based on various predictive variables.