Boston Housing Dataset

Boston Housing Dataset

About the Boston Housing Dataset

The Boston Housing Dataset contains information about housing and socioeconomic characteristics of Boston-area communities. It contains 506 records and 14 columns: 13 input features and 1 target variable, MEDV.

The dataset is commonly used for demonstrating regression, exploratory data analysis, feature analysis and machine-learning workflows.

Target Variable: MEDV — median value of owner-occupied homes, expressed in thousands of dollars.

1

Download the Dataset

Download or access the Boston Housing dataset from the Kaggle source provided below.

2

Load the Data

Load the CSV file into Python, R, Excel, or another data-analysis application.

3

Understand the Columns

Study the 13 input features and the MEDV target variable before building a model.

4

Explore the Data

Use statistics, charts and visualizations to understand the distribution of the variables.

5

Prepare the Data

Check data types, missing values, unusual observations and prepare features for analysis.

6

Split the Data

Separate the data into training and testing portions when developing a predictive model.

7

Train a Model

Apply an appropriate regression algorithm to learn the relationship between the features and MEDV.

8

Evaluate the Model

Compare predictions with actual target values using appropriate regression metrics.

All Column Names & Their Meaning

No.	Column	Meaning
1	CRIM	Per-capita crime rate by town.
2	ZN	Proportion of residential land zoned for lots over 25,000 square feet.
3	INDUS	Proportion of non-retail business acres per town.
4	CHAS	Charles River dummy variable: 1 if the tract bounds the river, otherwise 0.
5	NOX	Nitric oxides concentration, measured in parts per 10 million.
6	RM	Average number of rooms per dwelling.
7	AGE	Proportion of owner-occupied units built before 1940.
8	DIS	Weighted distance to five Boston employment centres.
9	RAD	Index measuring accessibility to radial highways.
10	TAX	Full-value property-tax rate per $10,000.
11	PTRATIO	Pupil-teacher ratio by town.
12	B	Derived variable calculated as 1000(Bk - 0.63)², where Bk is the proportion represented in the original dataset.
13	LSTAT	Percentage of the population classified as lower status in the original dataset.
14	MEDV	Median value of owner-occupied homes, expressed in thousands of dollars. This is the target variable.

Sample Data - First 10 Records

Sl No.	CRIM	ZN	INDUS	CHAS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT	MEDV
1	0.00632	18.00	2.31	0	0.538	6.575	65.2	4.0900	1	296	15.3	396.90	4.98	24.0
2	0.02731	0.00	7.07	0	0.469	6.421	78.9	4.9671	2	242	17.8	396.90	9.14	21.6
3	0.02729	0.00	7.07	0	0.469	7.185	61.1	4.9671	2	242	17.8	392.83	4.03	34.7
4	0.03237	0.00	2.18	0	0.458	6.998	45.8	6.0622	3	222	18.7	394.63	2.94	33.4
5	0.06905	0.00	2.18	0	0.458	7.147	54.2	6.0622	3	222	18.7	396.90	5.33	36.2
6	0.02985	0.00	2.18	0	0.458	6.430	58.7	6.0622	3	222	18.7	394.12	5.21	28.7
7	0.08829	12.50	7.87	0	0.524	6.012	66.6	5.5605	5	311	15.2	395.60	12.43	22.9
8	0.14455	12.50	7.87	0	0.524	6.172	96.1	5.9505	5	311	15.2	396.90	19.15	27.1
9	0.21124	12.50	7.87	0	0.524	5.631	100.0	6.0821	5	311	15.2	386.63	29.93	16.5
10	0.17004	12.50	7.87	0	0.524	6.004	85.9	6.5921	5	311	15.2	386.71	17.10	18.9

⬇ Download / View Boston Housing Dataset on Kaggle

Created by Bijan Krishna Paul