The data science community obsesses over algorithm selection. In my experience deploying ML models in production: the algorithm choice rarely determines the outcome as much as the features you feed it.
A logistic regression with carefully engineered features consistently outperforms a gradient boosting model with raw features.
Domain knowledge encoded as features: for a credit scoring model, "days since last missed payment" is more predictive than raw payment history. For fraud, "transaction amount as a ratio of the 30-day account average" beats raw transaction amount.
Spend 60% of your ML project time on data preparation and feature engineering. The algorithm will thank you by performing better than expected.
— Dick Bassey | DevDick | 2022