Predicting Housing Prices with XGBoost Methodology

Capstone Projects for Data Science - Spring 2026

Douglas Bogan and Susanna Brown (Advisor: Dr. Cohen)

2026-04-02

Introduction

  • Develop a storyline that captures attention and maintains interest.

  • Your audience is your peers

  • Clearly state the problem or question you’re addressing.

  • Introduce why it is relevant needs.

  • Provide an overview of your approach.

Methods

  • Detail the models or algorithms used.

  • Justify your choices based on the problem and data.

Data Exploration and Visualization

  • Describe your data sources and collection process.

  • Present initial findings and insights through visualizations.

  • Highlight unexpected patterns or anomalies.

Modeling and Results

  • Explain your data preprocessing and cleaning steps.

  • Present your key findings in a clear and concise manner.

  • Use visuals to support your claims.

  • Tell a story about what the data reveals.

Conclusion

  • Summarize your key findings.

  • Discuss the implications of your results.

References

Avanijaa, Jangaraj et al. 2021. “Prediction of House Price Using Xgboost Regression Algorithm.” Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12 (2): 2151–55.
Bentéjac, Candice, Anna Csörgo, and Gonzalo Martı́nez-Muñoz. 2019. “A Comparative Analysis of Xgboost.” ArXiv Abs 392.
Chen, Tianqi. 2016. “XGBoost: A Scalable Tree Boosting System.” Cornell University.
Kapoor, Sanyam, and Valerio Perrone. 2021. “A Simple and Fast Baseline for Tuning Large XGBoost Models.” arXiv Preprint arXiv:2111.06924.
Li, Sheng, Yi Jiang, Shuisong Ke, Ke Nie, and Chao Wu. 2021. “Understanding the Effects of Influential Factors on Housing Prices by Combining Extreme Gradient Boosting and a Hedonic Price Model (XGBoost-HPM).” Land 10 (5): 533.
Moreno-Foronda, Inmaculada, Marı́a-Teresa Sánchez-Martı́nez, and Montserrat Pareja-Eastaway. 2025. “Comparative Analysis of Advanced Models for Predicting Housing Prices: A Review.” Urban Science 9 (2): 32.
Ramraj, Santhanam, Nishant Uzir, R Sunil, and Shatadeep Banerjee. 2016. “Experimenting XGBoost Algorithm for Prediction and Classification of Different Datasets.” International Journal of Control Theory and Applications 9 (40): 651–62.
Sharma, Hemlata, Hitesh Harsora, and Bayode Ogunleye. 2024. “An Optimal House Price Prediction Algorithm: XGBoost.” Analytics 3 (1): 30–45.
Verma, Vibhu. 2024. “Exploring Key XGBoost Hyperparameters: A Study on Optimal Search Spaces and Practical Recommendations for Regression and Classification.” International Journal of All Research Education and Scientific Methods (IJARESM), ISSN, 2455–6211.
Zaki, John, Anand Nayyar, Surjeet Dalal, and Zainab H Ali. 2022. “House Price Prediction Using Hedonic Pricing Model and Machine Learning Techniques.” Concurrency and Computation: Practice and Experience 34 (27): e7342.
Zhang, Ping, Yiqiao Jia, and Youlin Shang. 2022. “Research and Application of XGBoost in Imbalanced Data.” International Journal of Distributed Sensor Networks 18 (6): 15501329221106935.
Zhao, Yun, Girija Chetty, and Dat Tran. 2019. “Deep Learning with XGBoost for Real Estate Appraisal.” In 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 1396–1401. IEEE.