🏠 House Price Prediction Using Machine Learning

A comparative study of Linear Regression and Gradient Boosting Regression

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📄 Abstract

This project performs a comparative analysis of Linear Regression and Gradient Boosting Regression techniques for predicting house prices using the King County housing dataset. The goal is to evaluate the performance of both models using R² and RMSE metrics and demonstrate the benefits of advanced ensemble methods in real estate forecasting.

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📊 Dataset

• Name: kc_house_data.csv
• Source: Kaggle - King County House Sales
• Size: 21,613 rows × 21 columns
• Target Variable: price
• Features: sqft_living, bedrooms, bathrooms, floors, zipcode, waterfront, view, etc.

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🔍 Problem Statement

Real estate pricing is inconsistent due to varying economic conditions and location preferences. This study aims to build models that accurately predict house prices and compare regression techniques in terms of accuracy, complexity, and interpretability.

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🚀 Project Scope

• 🔧 Data Preprocessing (missing values, feature selection, normalization)
• 📈 EDA using visualizations (scatter plots, heatmaps)
• 🤖 Model Implementation: Linear Regression & Gradient Boosting
• ⚙️ Hyperparameter Tuning: Grid Search, Cross-validation
• 📊 Model Evaluation: MSE, RMSE, R² Score
• 🔁 Comparative Analysis of performance, interpretability, scalability

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📉 Limitations

Linear Regression

• Assumes linear relationships
• Sensitive to outliers
• Multicollinearity issues
• Struggles with non-linear data

Gradient Boosting

• Computationally expensive
• Sensitive to hyperparameters
• Risk of overfitting
• Less interpretable

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📚 Literature Review Summary

Feature	Linear Regression	Gradient Boosting Regressor
Interpretability	High	Low
Complexity	Low	High
Handles Non-Linearity	Poor	Excellent
Speed	Fast	Slower
Hyperparameter Tuning	Minimal	Extensive
Overfitting Risk	High	Lower
Test Score (R²)	~0.73	~0.91

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🧪 Model Results

Model	R² Score (Test)	RMSE (Approx)
Linear Regression	0.73	Varies
Gradient Boosting	0.91	Lower RMSE

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📈 Visualizations

• Price vs Square Feet
• Price vs Location (Latitude, Longitude)
• Bedrooms vs Price
• Zipcode vs Price
• Waterfront vs Price

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📌 Future Work

• 🔧 Hyperparameter optimization
• 💡 Feature engineering (e.g., polynomial terms)
• 🧹 Regularization: L1 (Lasso), L2 (Ridge)
• 🌲 Compare with other models: XGBoost, LightGBM
• 🌐 Real-time deployment and streaming data support

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⚙️ How to Run

1. Clone the repository:

   git clone https://github.com/<your-username>/house-price-prediction.git
   cd house-price-prediction