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:
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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.
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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.
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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
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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