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How to load Boston Housing data in python?

Last Updated : 23 Jul, 2025

The Boston Housing dataset, which is used in regression analysis, provides insights into the housing values in the suburbs of Boston. This dataset has been a staple for algorithm demonstration, from simple linear regression to more complex machine learning models in predictive analytics. In this article, we will see how we can load the

What is the Boston housing data?

The Boston Housing dataset is a collection of data from the 1970s on housing prices in various Boston districts, commonly used in machine learning to demonstrate regression analysis.

Originally curated by the U.S. Census Service, it includes 506 instances, each with 13 features, and the target variable is the median value of owner-occupied homes in $1000s.

How to load Boston Housing data in sklearn?

To load the Boston Housing dataset in Python using scikit-learn, you can use the load_boston() function.

As of version 1.2, scikit-do has deprecated this function due to ethical concerns. However, for educational purposes and where necessary, we can still load the dataset using online repositories.

Earlier we could load the dataset using the load_boston function,

Output:

👁 file


As stated before the dataset is not present, we can use the following methods to load it:

Method 1: Using a CSV file

To know in detail about this: Click here

Output:

CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B LSTAT MEDV
0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98 24.0
1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14 21.6
2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03 34.7
3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94 33.4
4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 NaN 36.2

Method 2: Using Keras

To know more about this: Click here

Output:

Training data shape: (404, 13)
Training targets shape: (404,)
Test data shape: (102, 13)
Test targets shape: (102,)

Use Cases of Boston Housing data

The Boston Housing dataset is a well-known dataset commonly used in machine learning and data science for regression tasks. Here are some typical use cases in Python:

  1. Predicting Housing Prices:
    • The primary use case for the Boston Housing dataset is to predict housing prices based on various features such as crime rate, number of rooms, accessibility to highways, and more.
  2. Exploratory Data Analysis (EDA):
    • The dataset can be used to perform EDA to understand the relationships between different features and the target variable (housing prices). Techniques include visualizations (scatter plots, histograms, box plots), summary statistics, and correlation analysis.
  3. Feature Engineering:
    • The dataset provides a good platform to practice feature engineering techniques like creating new features, handling missing values, scaling features, and transforming variables.
  4. Regression Models:
    • You can use the dataset to train and evaluate various regression models like Linear Regression, Ridge Regression, Lasso Regression, and Polynomial Regression.
  5. Model Evaluation and Validation:
    • The dataset can be used to practice different model evaluation techniques like cross-validation, train-test split, and performance metrics (RMSE, MAE, R²).
  6. Regularization Techniques:
    • The Boston Housing dataset is often used to demonstrate the effects of regularization techniques like Lasso and Ridge on reducing overfitting and improving model performance.
  7. Feature Selection:
    • You can use the dataset to practice feature selection techniques like forward selection, backward elimination, and recursive feature elimination (RFE).
  8. Ensemble Methods:
    • The dataset can be used to implement and compare ensemble methods like Random Forest, Gradient Boosting, and XGBoost to see how they perform in predicting housing prices.
  9. Hyperparameter Tuning:
    • It provides a good platform for practicing hyperparameter tuning techniques using Grid Search, Random Search, or Bayesian Optimization to find the best parameters for models.
  10. Dimensionality Reduction:
    • Techniques like Principal Component Analysis (PCA) can be applied to the dataset to reduce the dimensionality and visualize the data in 2D or 3D plots.
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