About this project
This project applies machine learning — multiple regression, random forest and gradient boosting — to residential property valuation, comparing model predictions with valuations from traditional comparable methods. Using a dataset of property characteristics and transaction prices, it evaluates accuracy and discusses what automated valuation means for estate surveyors.
Suggested tech stack
- Python
- scikit-learn
- Pandas
Chapters 1–5 outline
Chapter 1
Introduction: background to automated property valuation, statement of the problem (traditional valuation is slow, subjective and inconsistent across valuers), aim and objectives, research questions, scope, significance of the study, and definition of terms.
Chapter 2
Literature Review: theoretical framework, review of related works on automated property valuation, gaps in existing studies, and a summary positioning this project.
Chapter 3
Methodology / System Analysis and Design: quantitative design; model training and validation; accuracy metrics. Includes data collection method, system requirements, use-case and architecture diagrams (or population, sample size, and instrument).
Chapter 4
Implementation and Results: model accuracy comparison and implications for valuation practice. Presentation of findings, testing, evaluation, and discussion of results.
Chapter 5
Summary, Conclusion and Recommendations: key findings, contribution to knowledge, limitations, and recommendations for further research.
Get this project done — chapters, code, defence support
Final Year writes the full project for you. Original content, on time, with chat support up to defence day.
Preview sample