Have you ever had to build a regression model from scratch?

A regression model is a statistical model that describes the dependence of one variable on one or more other variables.

If you have had to create a regression model from scratch, the process usually involves the following steps:

1. Data collection and preparation: cleaning, handling missing values, feature selection.
2. Data analysis: visualization, checking correlations.
3. Choosing the type of regression (linear, polynomial, logistic, etc.) depending on the task.
4. Training the model on training data.
5. Evaluating the model's quality using metrics (e.g., R², RMSE).
6. If necessary, tuning hyperparameters and refining the model.

Example: to predict the price of an apartment, you can build a linear regression where features are area, number of rooms, floor, etc. After training the model, it can be used for predictions on new data.

In QA automation, knowledge of regression analysis helps in assessing the impact of changes on product quality and identifying patterns in defects.