Boston Dataset in Sklearn
1. What kind of data does the Boston Dataset contain?
The dataset contains 506 entries, each with 14 attributes or features. The primary features include average number of rooms per dwelling, property tax rate, pupil-teacher ratio, and others, along with the median value of owner-occupied homes in $1000s (the target variable).
2. What are the common uses of the Boston Dataset?
The Boston Dataset is primarily used to predict housing prices based on various features and to practice and understand regression techniques in machine learning. It is also utilized for educational purposes to teach data preprocessing, linear regression, and feature selection.
3. What are some alternatives to the Boston Dataset for housing price prediction?
A popular alternative is the California Housing dataset, also available in sklearn. It covers a more recent time period and involves a larger sample size, making it more appropriate for current studies.
4. Can I see a description of the dataset?
Yes, you can view a detailed description by printing boston.DESCR. This provides a full description of the dataset, including the context of the data collection, attribute information, and statistics.
Boston Dataset in Sklearn
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.