Boston Housing Price Prediction

This repository contains a machine learning project that predicts housing prices in the Boston area using the Boston Housing dataset. The project demonstrates the complete workflow of a regression problem, including data preprocessing, feature scaling, model training, evaluation, and prediction.

Repository: https://github.com/codebyimran-projects/boston-housing-price-prediction

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Project Overview

The goal of this project is to predict the median value of owner-occupied homes (medv) based on various economic, environmental, and housing-related features.

This project covers:

• Loading and cleaning the dataset
• Handling missing values
• Feature scaling
• Training a regression model
• Evaluating model performance
• Predicting house prices using custom input

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Dataset Information

The dataset contains 506 rows and 14 columns.

Feature Description

Column	Description
crim	Per capita crime rate by town
zn	Proportion of residential land zoned for large lots
indus	Proportion of non-retail business acres
chas	Charles River dummy variable (1 if tract bounds river, else 0)
nox	Nitric oxides concentration
rm	Average number of rooms per dwelling
age	Proportion of owner-occupied units built before 1940
dis	Distance to employment centers
rad	Accessibility to radial highways
tax	Property tax rate
ptratio	Pupil-teacher ratio
b	Proportion of Black residents
lstat	Percentage of lower-status population
medv	Median house value (Target variable)

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Requirements

Install the required Python packages:

pip install numpy pandas matplotlib scikit-learn

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How to Run the Project

1. Clone the repository:

git clone https://github.com/codebyimran-projects/boston-housing-price-prediction.git
cd boston-housing-price-prediction

2. Make sure BostonHousing.csv is present in the project folder.

3. Run the main script:

python main.py

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Model Used

• Linear Regression

This model is suitable because the target variable (medv) is continuous.

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Model Evaluation

The model is evaluated using:

• Root Mean Squared Error (RMSE)
• R² Score (Coefficient of Determination)

These metrics help measure prediction error and how well the model explains variance in house prices.

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Predicting House Price Using User Input

The project supports predicting house prices using custom input values:

import numpy as np

print("Enter house feature values:")

user_data = []
for col in X.columns:
    value = float(input(f"{col}: "))
    user_data.append(value)

user_array = np.array(user_data).reshape(1, -1)
user_scaled = scaler.transform(user_array)

predicted_price = model.predict(user_scaled)
print("Predicted House Price (MEDV):", predicted_price[0])

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Visualization

The project includes:

• Histogram analysis of room distribution
• Scatter plot of actual vs predicted house prices

These plots help in understanding data distribution and model accuracy.

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Learning Outcomes

This project helps understand:

• Regression problems in machine learning
• Data preprocessing techniques
• Feature scaling
• Model training and evaluation
• Real-world prediction workflow

ell me what you want to do next. `