Python Seaborn Statistical Exploration
Using Python, Seaborn, and Matplotlib, I performed deep statistical exploration on 80+ variables to identify the strongest predictors of real estate value.
The methodology focused on identifying data patterns and cleaning potential noise. I utilized a **correlation heatmap** to visualize the relationship between variables like "Ground Living Area" and "Overall Quality" against the sale price. To ensure the reliability of future models, I addressed missing data through mean/mode imputation and treated skewed numerical distributions using **log transformations**. This process reduced the complexity of the dataset while preserving the variables with the highest predictive power.
In Data Science, model accuracy is entirely dependent on data quality. This project demonstrates my commitment to the "Zero-Discrepancy" mindset—I don't just "run models"; I investigate the data's story first. By performing a rigorous EDA, I prevent bias and ensure that the features selected for a machine learning pipeline are statistically significant. For a business, this means more accurate valuations and a deeper understanding of market drivers.
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