How do you decide which ML algorithm to start with for a new dataset?

Hi richa_gupta_224 ,

In real-world ML projects, we usually don’t try to identify the “best” algorithm upfront. Instead, the focus is on understanding the data and establishing baselines.

A typical approach looks like this:

Start with a simple baseline (Linear or Logistic Regression). This helps validate the data, uncover leakage, and set a reference point. Evaluate a few strong models in parallel, usually one linear model, one tree-based model (Random Forest), and one boosting model (XGBoost/LightGBM). The data often makes the choice clearer.

Let practical constraints guide selection:

Need interpretability > simpler models

Tabular data and performance-driven > tree-based or boosting models

Small datasets > avoid overly complex models

Be cautious of extremely high accuracy. Iterate over time. As data, features, or business requirements evolve, the “best” model can change.
The key takeaway is that model selection is an iterative engineering process, not a one-time decision. In most cases, improvements come more from better data and features than from switching algorithms.

Hope this helps.
Thank you.