What factors would you consider when selecting a machine learning model for a regression task?

In a recent college project, I worked on predicting housing prices in a local area using a dataset with features like square footage, number of bedrooms, and location. To select the right regression model, I first analyzed the data distribution and identified that the relationship between features and target was not linear. I considered models like Linear Regression, Decision Trees, and Random Forests. Ultimately, I chose Random Forest due to its ability to handle non-linear relationships and provide good accuracy. I also implemented cross-validation to avoid overfitting and used metrics like RMSE to evaluate performance. This hands-on experience taught me how to approach model selection thoughtfully.

During my time volunteering for a local business, I was tasked with helping predict sales based on various factors such as marketing efforts, seasonality, and product categories. I initially considered a simple Linear Regression model, but after analyzing the data, I realized there were interactions between features that a linear model couldn’t capture effectively. I collaborated with team members and decided to use a Decision Tree model for its interpretability and ability to handle categorical data. This experience emphasized the importance of understanding the data and the audience's needs when selecting a model.

In my first job as a data analyst, I worked on a project to predict customer churn for a subscription service. With access to a rich dataset, I explored various models, including Linear Regression, Support Vector Machines, and Gradient Boosting. I considered factors like the nature of the data, which included many categorical features and potential non-linear relationships. I ultimately chose Gradient Boosting due to its robustness against overfitting and superior performance on our validation set. This experience reinforced the idea that model selection is not just about picking an algorithm but understanding the problem deeply and aligning it with the business goals.