Ames Report

In this project, I embarked on a comprehensive analysis of the Ames property values dataset, unearthing valuable insights and developing a predictive model with the aim of forecasting property prices. This project combined statistical analysis, feature engineering, and machine learning to offer a robust solution for property valuation in the Ames housing market.

Highlights

• Data Exploration and Statistical Analysis: In-depth correlation studies revealing relationships between property features, market trends, and pricing dynamics.
• Feature Engineering: Transformed and encoded key variables to better capture the nuances of property valuation.
• Support Vector Machine (SVM) Regression: Trained on historical data to predict prices using a wide array of features.
• Model Evaluation and Tuning: Fine-tuned hyperparameters to improve predictive accuracy and generalization.

Outcomes

The project yielded a predictive model capable of estimating property values in the Ames housing market with strong accuracy. This model demonstrates the power of statistical analysis and feature engineering when paired with machine learning techniques for real-world forecasting.

Attachments