House Price Prediction Machine Learning Pipeline: From Data Engineering to Regularized Model Optimization

Project Basic Information

• Original Author/Maintainer: niharn23122006-sys
• Source Platform: GitHub
• Original Title: House-Price-Prediction-ML-Pipeline
• Original Link: https://github.com/niharn23122006-sys/House-Price-Prediction-ML-Pipeline
• Release Date: 2026-06-13

Core Overview

This project builds an end-to-end house price prediction machine learning pipeline based on the Kaggle Advanced Regression Dataset. Through complete data engineering, feature engineering, and comparison of regularized models, the final Lasso regression solution achieves 87.42% prediction accuracy, verifying the key role of regularization techniques in house price prediction.

Business Background

House price prediction is a classic regression problem in machine learning, providing decision support for the real estate industry, financial institutions, and urban planning departments, helping homebuyers, banks, governments, and other entities make informed choices.

Dataset and Challenges

• Dataset: Kaggle House Prices competition dataset, containing 79 house features and sale price labels for Ames, USA, obtained automatically via kagglehub.
• Core Challenges: High feature dimensionality (253 dimensions after one-hot encoding), widespread missing values, multicollinearity, overfitting risk, and large differences in feature scales.

Data Engineering

• Automated Acquisition: Use kagglehub to ensure data consistency and traceability.
• Missing Value Handling: Fill continuous features with median values and categorical features with mode values.

Feature Engineering

• Domain Features: Construct composite features such as sqft_per_bedroom (average area per bedroom) and total_bathrooms (total number of bathrooms).
• Feature Scaling: Standardize to zero mean and unit variance, laying the foundation for regularized models.

Model Performance Comparison

Model	Validation RMSE	Validation MAE	Validation R²	Overfitting Risk
Linear Regression	$51,364.99	$20,263.19	0.6560	0.2799
Ridge (α=10.0)	$36,082.81	$19,673.26	0.8303	0.0991
Lasso (α=1000)	$31,058.23	$18,187.55	0.8742	0.0135

Key Findings

• Linear regression has severe overfitting;
• Ridge regression (L2) reduces error by 30% and alleviates overfitting;
• Lasso regression (L1) has the best performance with R² reaching 87.42% and overfitting risk close to zero.

1. Necessity of Regularization: Unregularized linear regression is prone to overfitting in high-dimensional data;
2. L1 vs L2: Lasso is more suitable for this dataset due to its feature selection capability;
3. Value of Domain Knowledge: Composite features (e.g., sqft_per_bedroom) capture deep business logic;
4. Multi-dimensional Evaluation: Need to combine R² and overfitting risk to select production models.

Application Scenarios

• Property valuation, mortgage assessment, investment decision-making, market trend analysis.

Expansion Directions

1. Try gradient boosting models like XGBoost/LightGBM;
2. Explore feature interactions and nonlinear effects;
3. Integrate GIS spatial data and time series trends;
4. Introduce deep learning models to capture complex patterns.

Project Value

Provides an end-to-end engineering example for machine learning learners, proving that traditional linear models can balance performance and interpretability after optimization.