Firstly, I conducted a geographical analysis. This analysis helped identify geographic patterns, disparities, or clusters within the dataset, shedding light on potential regional variations.
In addition to geographical analysis, I dived into predictive modeling. Specifically, I employed ridge and linear regression techniques to develop models to understand and predict key relationships within the data. Ridge regression was used to address multicollinearity and prevent overfitting, enhancing the robustness of the predictive models. Linear regression, on the other hand, provided insights into the linear relationships between variables.
Beyond model development, I thoroughly evaluated the performance of these models. This evaluation involved assessing their predictive accuracy, goodness-of-fit, and statistical significance. Through these analyses, I aimed to not only understand the dataset better but also derive actionable insights that could inform decision-making or further research in the field.
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