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: The project began with an in-depth exploration of the Ames property values dataset, encompassing various statistical analyses. I delved into correlation studies, uncovering relationships between property features, market trends, and pricing dynamics.
- Feature Engineering: To enhance the predictive power of the model, I engineered features, extracting valuable information from the dataset. This process involved transforming and encoding variables, ensuring that the model could effectively capture the nuances of property valuation.
- Support Vector Machine (SVM) Regression Model: For predictive modeling, I employed a Support Vector Machine (SVM) regression approach. The model was trained on historical property data, learning to predict prices based on a wide array of features
- Model Evaluation and Tuning: Model evaluation was performed to assess predictive accuracy and generalization capabilities. I fine-tuned model hyperparameters to optimize performance and minimize errors.
Outcomes:
The project yielded a predictive model capable of estimating property values in the Ames housing market with remarkable accuracy. This model leverages the insights gained from statistical analyses and feature engineering to provide valuable pricing forecasts. By employing machine learning techniques.
Full report in PDF