What is the Boston dataset?
The Boston dataset is a classic regression analysis dataset containing data on house prices in the Boston area of the United States, as well as related attribute information. The dataset has a total of 506 samples and 13 attributes, including 12 characteristic variables and 1 target variable (median house price).
Attribute information for the dataset
Information on the 13 attributes of the Boston dataset is given below:
- CRIM: Per capita crime rate in towns and cities
- ZN: Percentage of residential land
- INDUS: Proportion of non-residential land in towns and cities
- CHAS: proximity to the Charles River (1 means proximity, 0 means no proximity)
- NOX: Nitric oxide concentration
- RM: Average number of rooms in a house
- AGE: Proportion of owner-occupied housing built before 1940
- DIS: Weighted distance from 5 Boston employment centers
- RAD: Radiating Distance from Green Park
- TAX: Full property tax rate per $10,000
- PTRATIO: Pupil-teacher ratio in towns and cities
- B: Percentage of Blacks
- MEDV: Median house price (in thousands of dollars)
Application of data sets
The Boston dataset is a very classical dataset that is widely used in machine learning and data science. It can be used for regression analysis, feature engineering, data visualization and model evaluation. Some common applications include:
- Home price prediction: using a machine learning model trained on the Boston dataset to predict median home prices in the Boston area.
- Feature engineering: feature engineering of the dataset, such as feature selection, feature scaling, feature dimensionality reduction, etc., to improve the accuracy and generalization of the model.
- Data Visualization: Data visualization and exploratory data analysis using attribute information from the Boston dataset to understand the characteristics and relationships of the dataset.
- Model evaluation: machine learning model evaluation and comparison using the Boston dataset to select the best model and parameter configuration.
Boston dataset for house price forecasting
Boston dataset is a very useful dataset for regression analysis, feature engineering, data visualization and model evaluation. By learning and applying the Boston dataset, we can improve our data analysis and machine learning skills for real-world problem solving.
Below is a sample code for house price prediction using the Boston dataset:
pythonCopy codeimport pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from import mean_squared_error
# Load the Boston dataset
boston_data = pd.read_csv('')
# Extraction of characteristic and target variables
X = boston_data.drop('MEDV', axis=1)
y = boston_data['MEDV']
# Divide the dataset into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create linear regression models
model = LinearRegression()
# Fit the model on the training set
(X_train, y_train)
# Predictions on test sets
y_pred = (X_test)
# Calculate the root mean square error (RMSE)
rmse = mean_squared_error(y_test, y_pred, squared=False)
print("Root Mean Square Error (RMSE):", rmse)In this example, we first load the Boston dataset using the pandas library and separate the feature variable (x) from the target variable (y). Then, we use thetrain_test_splitfunction divides the dataset into a training set and a test set. Next, we create a linear regression model and fit the model on the training set. Finally, we use the trained model to make predictions on the test set and calculate the root mean square error (RMSE) between the predictions and the true values as a model evaluation metric. This example shows the basic steps of how to perform house price prediction using the Boston dataset, which allows for further model tuning and feature engineering based on specific needs.
The Boston dataset is a very classic dataset for regression analysis, but it has some drawbacks. Below are the drawbacks of the Boston dataset and a description of similar datasets:
Disadvantages of the Boston dataset
- The dataset is relatively small: with only 506 samples, the Boston dataset is relatively small in relation to the actual problem and may not cover all situations.
- Older dataset: The Boston dataset was collected in 1978, and home prices and urban environments may have changed considerably to reflect current market conditions.
- The dataset is not comprehensive enough: the Boston dataset contains only 13 attributes and the correlation between the attributes is relatively strong, which may not satisfy some of the more complex problems.
Similar data sets
- California Housing dataset: This dataset contains house price data and related attribute information for each region of California in 1990, with 20,640 samples and 8 attributes, which can be used for regression analysis and feature engineering.
- Ames Housing dataset: This dataset contains house price data and related attribute information, with 2,930 samples and 80 attributes, which is a larger volume of data and more attributes than the Boston dataset, and can be used for more complex problems.
- Kaggle House Prices dataset: This dataset contains house price data as well as related attribute information, with 1460 samples and 80 attributes. It is a very popular house price prediction dataset, which is widely used in the field of house price prediction and feature engineering. These datasets are similar to the Boston dataset in that they both contain house price data as well as related attribute information, and can be used for regression analysis, feature engineering, data visualization, and model evaluation. However, they differ in terms of data volume, number of attributes and data collection time, and can be selected and applied according to specific needs.
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