Predicting Boston Housing Prices with Linear Regression

Explore a practical machine learning project using linear regression to predict Boston housing prices. Learn how to prepare the dataset, train a linear regression model, make predictions, and evaluate the model's performance. Discover the key features influencing housing prices and gain insights into applying linear regression for real-world prediction tasks.

Boston Housing Price Prediction with Linear Regression

Introduction

This project uses the Boston Housing dataset to build a linear regression model for predicting house prices. The dataset contains 506 instances and 13 features. We'll explore data preparation, model training, prediction, and evaluation.

The Boston Housing Dataset

The dataset includes the following features (attributes):

Boston Housing Dataset Features

Feature Name	Description
crim	Crime rate per capita by town.
zn	Proportion of residential land zoned for lots over 25,000 sq.ft.
indus	Proportion of non-retail business acres per town.
chas	Charles River dummy variable (1 if tract bounds river; 0 otherwise).
nox	Nitric oxides concentration (parts per 10 million).
rm	Average number of rooms per dwelling.
age	Proportion of owner-occupied units built prior to 1940.
dis	Weighted distances to five Boston employment centres.
rad	Index of accessibility to radial highways.
tax	Full-value property-tax rate per \$10,000.
ptratio	Pupil-teacher ratio by town.
black	1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.
lstat	% lower status of the population.
medv	Median value of owner-occupied homes in \$1000's. (Target Variable)

(Data source: Carnegie Mellon University StatLib library)

Data Loading and Preparation


import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error
import matplotlib.pyplot as plt

# Load the dataset (replace with your file path)
boston = pd.read_csv("boston_housing.csv")

# Separate features (X) and target (y)
X = boston.drop('medv', axis=1)
y = boston['medv']

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Model Training and Prediction


# Create and train the linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

Model Evaluation


# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
print(f"Mean Squared Error (MSE): {mse}")
print(f"Mean Absolute Error (MAE): {mae}")

#Plot the results (optional)
plt.scatter(y_test, y_pred, color='blue')
plt.xlabel("Actual Prices")
plt.ylabel("Predicted Prices")
plt.title("Actual vs. Predicted House Prices")
plt.show()


Mean Squared Error (MSE): 21.57659368987862
Mean Absolute Error (MAE): 3.191472667461123

Scatter Plot of Actual vs. Predicted Prices

(The MSE and MAE values indicate the model's error. A lower value is better. The scatter plot visually represents the model's predictions against actual prices.)

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