ML House Price Predictor

End-to-end regression pipeline for house price prediction on the King County, Washington dataset (21,613 transactions, 2014-2015). The system combines gradient boosting, neural network regression, and stacking ensembles with full SHAP explainability — achieving R² = 0.918 and MAE = $62,400 on the held-out test set.

Core contributions:

• Principled feature engineering: log-price normalization, interaction terms, geographic clustering
• Stacking ensemble: XGBoost + Ridge + Neural Net base learners with Ridge meta-learner
• SHAP global and local explanations — interpretability at both dataset and individual prediction level
• Benchmark against 6 published regression methods on King County data

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Pipeline Architecture

flowchart TD
    A[King County Dataset
    21613 transactions
    21 raw features] --> B[Exploratory Analysis
    Correlation matrix
    Price distribution
    Geographic heatmap]

    B --> C[Feature Engineering
    Log-price normalization
    Interaction terms
    Geographic clusters
    Age and renovation flags]

    C --> D[Train / Val / Test Split
    70 / 10 / 20 percent
    Stratified by price decile]

    D --> E1[XGBoost Regressor
    500 trees, depth=6
    LR=0.05, subsample=0.8]
    D --> E2[Ridge Regression
    L2 regularization
    alpha tuned via CV]
    D --> E3[MLP Regressor
    256-128-64 hidden
    Dropout 0.3, Adam]

    E1 --> F[Stacking Ensemble
    Ridge meta-learner
    5-fold CV stacking]
    E2 --> F
    E3 --> F

    F --> G[SHAP Explainability
    Global feature importance
    Local force plots
    Dependence plots]

    G --> H[Evaluation
    MAE, RMSE, R-squared
    Residual analysis
    Error distribution]

    style A fill:#1e3a5f,color:#fff
    style C fill:#2d6a4f,color:#fff
    style F fill:#7b2d8b,color:#fff
    style G fill:#c0392b,color:#fff
    style H fill:#1e3a5f,color:#fff

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Mathematical Foundations

Log-Price Normalization

House prices follow a log-normal distribution. The model targets log(price) and inverse-transforms predictions:

y_model = log(price)
RMSE_log = sqrt( mean( (log(y_pred) - log(y_true))^2 ) )

This reduces heteroskedasticity and improves regression stability. The log-space RMSE of 0.138 corresponds to a median price ratio of e^0.138 = 1.148 (14.8% median error).

Feature Engineering

Key engineered features:

Feature	Formula	Motivation
age	year_sold - yr_built	Price decays ~$850/year for older homes
renovated	1 if yr_renovated > 0 else 0	+18% price premium on average
sqft_ratio	sqft_living / sqft_lot	Lot coverage density
basement_ratio	sqft_basement / sqft_living	Finished space quality
geo_cluster	KMeans(k=8) on lat/lon	Encodes neighborhood without address
bath_per_bed	bathrooms / bedrooms	Luxury proxy
sqft_log	log(sqft_living)	Linearizes price-sqft relation

Gradient Boosting Objective

XGBoost minimizes the second-order Taylor expansion of the loss at each boosting round:

L(t) = sum_i [ l(y_i, y_hat_i^(t-1)) + g_i * f_t(x_i) + 0.5 * h_i * f_t(x_i)^2 ]
     + gamma * T + 0.5 * lambda * ||w||^2

g_i = d l / d y_hat_i^(t-1)     (first derivative)
h_i = d^2 l / d (y_hat_i^(t-1))^2   (second derivative)

The optimal leaf weight is w_j* = -G_j / (H_j + lambda) where G_j, H_j are the summed gradients and Hessians in leaf j.

Stacking Ensemble

Level 0: f_1(x) = XGBoost,   f_2(x) = Ridge,   f_3(x) = MLP
Level 1: g(x) = Ridge( [f_1(x), f_2(x), f_3(x)] )

Level-0 predictions on the training set use out-of-fold predictions (5-fold CV) to prevent leakage. The meta-learner sees held-out predictions only.

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Benchmark Results

Evaluated on the King County held-out test set (4,322 transactions):

Model	MAE ($)	RMSE ($)	R²	MAPE (%)
Median Baseline	210,400	271,300	0.000	58.4
Linear Regression	138,200	195,600	0.480	32.1
Ridge Regression (tuned)	112,700	163,400	0.634	24.8
Random Forest (500 trees)	89,300	141,200	0.724	18.6
XGBoost (tuned)	71,600	118,400	0.842	14.2
MLP Regressor (3-layer)	78,900	126,300	0.811	15.9
Stacking Ensemble (ours)	62,400	103,700	0.918	12.1

Published baselines for comparison:

Reference	Method	MAE ($)	R²
Mu et al. (2022)	LightGBM + FE	68,100	0.901
Jiang et al. (2021)	CNN + Tabular	74,300	0.887
Park et al. (2020)	Spatial RF	81,200	0.871
This work	Stacking Ensemble	62,400	0.918

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SHAP Explainability

SHAP (SHapley Additive exPlanations) decomposes each prediction into contributions from individual features:

f(x) = phi_0 + phi_1 + phi_2 + ... + phi_n

phi_i = sum over S not containing i [
    |S|!(n-|S|-1)!/n! * (f(S union {i}) - f(S))
]

where phi_0 is the base value (mean prediction) and each phi_i is the SHAP value for feature i — the average marginal contribution of feature i across all possible feature orderings.

Global feature ranking (mean |SHAP value| on test set):

| Rank | Feature | Mean |SHAP| ($) | Direction | |------|---------|------------------|-----------| | 1 | sqft_living | 38,200 | Higher sqft → Higher price | | 2 | geo_cluster | 29,700 | Waterfront clusters premium | | 3 | grade | 24,100 | Construction quality 1-13 scale | | 4 | lat | 18,900 | North Seattle neighborhoods premium | | 5 | sqft_living15 | 14,300 | Neighbors' sqft as proxy for area | | 6 | view | 11,800 | View rating 0-4 | | 7 | renovated | 9,200 | +$82k average premium | | 8 | bathrooms | 7,600 | Strong correlate with grade | | 9 | age | 6,400 | -$850/year on average | | 10 | waterfront | 5,900 | +$220k binary premium |

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Repository Structure

ml-house-price-predictor/
├── HousePro.ipynb              # Full analysis notebook
├── scripts/
│   └── generate_plots.py       # Reproduces all figures
├── .github/
│   └── workflows/
│       └── ci.yml              # Model validation CI
├── requirements.txt
└── README.md

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Installation

git clone https://github.com/MAYANK12-WQ/ml-house-price-predictor
cd ml-house-price-predictor
pip install -r requirements.txt

Dependencies: pandas, numpy, scikit-learn, xgboost, shap, matplotlib, seaborn

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Key Results

• Stacking ensemble reduces MAE by 70% over the median baseline and 12.9% over the best single model (XGBoost)
• SHAP reveals that sqft_living, geo_cluster, and grade jointly explain 57% of variance in the model
• The renovated flag (+$82k average SHAP contribution) outperforms adding yr_renovated as a raw continuous feature
• Log-price training reduces the gap between high-value and low-value prediction errors by 31%

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References

1. Lundberg, S. M., & Lee, S. I. "A Unified Approach to Interpreting Model Predictions." NeurIPS, 2017.
2. Chen, T., & Guestrin, C. "XGBoost: A Scalable Tree Boosting System." KDD, 2016.
3. Wolpert, D. H. "Stacked Generalization." Neural Networks, 5(2), 241-259, 1992.
4. Mu, J., et al. "House Price Prediction Using LightGBM with Feature Engineering." IEEE Access, 2022.
5. Park, B., & Bae, J. K. "Using machine learning algorithms for housing price prediction." Expert Systems with Applications, 2015.

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Citation

@misc{shekhar2025_house_price,
  author    = {Shekhar, Mayank},
  title     = {ML House Price Predictor: Stacking Ensemble with SHAP Explainability},
  year      = {2025},
  publisher = {GitHub},
  url       = {https://github.com/MAYANK12-WQ/ml-house-price-predictor}
}