This is so Title: Predicting House Sale Prices Regression Analysis on the Ames Housing Dataset Student: Muhohio Nyambura (use your student ID) Unit code & name: NIT3171 ICT Business Analysis & Data Visualization Submission date: (fill date) Abstract This report completes Stage II of the individual assignment by performing a business analysis task selected from the group project: Regression Analysis predicting SalePrice. The document explains the business problem, data used (Ames Housing Dataset), chosen data-mining method (Gradient Boosting Regression), preprocessing steps, modeling pipeline, evaluation strategy, results interpretation (expected outcomes and how to interpret them), business recommendations for stakeholders, and a suggested classroom presentation outline. Although the raw dataset and code execution are not included here, reproducible code, evaluation measures, and a deployment-ready pipeline are provided so the model can be run and validated by the student. Introduction Background. Real estate agents, valuation services, and investment firms need accurate, data-driven property valuations to price homes, evaluate portfolios, and recommend renovations that yield the highest return. The Ames Housing Dataset (2,930 observations, ~79 attributes) provides a rich set of structural, locational and qualitative features suitable for a predictive pricing model. Problem / Objective. Build a regression model that predicts SalePrice for houses in the