Boston Housing Analysis

This repository contains a comprehensive analysis of the Boston Housing dataset using various regression models, including Linear Regression, Lasso Regression, and Ridge Regression. The project explores the dataset, visualizes the relationships between features and target variables, and evaluates the performance of the different regression models, providing valuable insights into the housing market in Boston.

Dataset

The dataset used in this project is the Boston Housing Dataset, which contains information collected by the U.S Census Service concerning housing in the area of Boston, Massachusetts. The dataset has 506 samples, with 13 input features and a target variable (MEDV), which represents the median value of owner-occupied homes in $1000's.

Important Note: The load_boston function is deprecated in scikit-learn 1.0 and will be removed in 1.2 due to ethical issues related to the dataset. It is strongly recommended to use alternative datasets like the California housing dataset or the Ames housing dataset. To run this Python program without issues, please use a scikit-learn version lower than 1.0.

Contents

The repository contains a single Python script, boston_housing_analysis.py, which includes the following:

1. Importing necessary libraries
2. Loading and exploring the dataset
3. Preprocessing and feature engineering
4. Visualizing the dataset, including histograms, scatterplots, and heatmaps
5. Implementing and evaluating various regression models
6. Calculating and visualizing regression errors and residuals

Applications

The analysis of the Boston Housing dataset can be beneficial for various purposes, including:

• Identifying factors that influence housing prices
• Evaluating the effectiveness of different regression models
• Predicting housing prices based on given features
• Enhancing understanding of the relationships between variables in the housing market

Requirements

To run the code, you will need the following libraries:

• numpy
• pandas (version 1.5.3)
• matplotlib
• seaborn
• scikit-learn (version lower than 1.0)

Note: This project uses pandas version 1.5.3 instead of the latest version (2.0.0) because the append() function is deprecated and replaced by concat() in version 2.0.0. For more information, check the pandas 2.0.0 release notes.

The repository includes a requirements.txt file that lists the necessary library versions. After cloning the repository and navigating to the project root folder (boston-housing-analysis), run the following command to install the required packages:

pip install -r requirements.txt

Usage

To run the analysis, simply execute the boston_housing_analysis.py script in your Python environment:

python boston_housing_analysis.py

The script will output various visualizations, performance metrics, and model coefficients for the different regression models.

If you want to use an alternative dataset like the California housing dataset or the Ames housing dataset, please follow the instructions below and modify the code accordingly:

For the California housing dataset:

from sklearn.datasets import fetch_california_housing
housing = fetch_california_housing()

For the Ames housing dataset:

from sklearn.datasets import fetch_openml
housing = fetch_openml(name="house_prices", as_frame=True)

To use one of these alternative datasets, replace the code that loads the Boston Housing dataset in the boston_housing_analysis.py script with one of the code snippets above. Please note that these alternative datasets have different features, so you may need to update the feature engineering, preprocessing, and visualization sections of the script accordingly.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• pandas - Data manipulation and analysis library
• numpy - Numerical computing library
• matplotlib - Plotting library
• seaborn - Statistical data visualization library
• scikit-learn - Machine learning library