# MODEL BUILDING - [Overview](https://book.thedatascienceinterviewproject.com/model-building/overview.md): This page broadly summarizes the steps needed to go from data gathering to model building - [Data](https://book.thedatascienceinterviewproject.com/model-building/data.md) - [Scaling](https://book.thedatascienceinterviewproject.com/model-building/data/scaling.md) - [Missing Value](https://book.thedatascienceinterviewproject.com/model-building/data/missing-value.md) - [Outlier](https://book.thedatascienceinterviewproject.com/model-building/data/outlier.md) - [Sampling](https://book.thedatascienceinterviewproject.com/model-building/data/sampling.md) - [Categorical Variable](https://book.thedatascienceinterviewproject.com/model-building/data/categorical-variable.md): In the realm of data analysis, categorical variables play a vital role in representing non-numeric data. To utilize these variables effectively it is essential to convert them into numerical form. - [Hyperparameter Optimization](https://book.thedatascienceinterviewproject.com/model-building/hyperparameter-optimization.md) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://book.thedatascienceinterviewproject.com/model-building.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.