Professional ML Algorithm Decision Assistant

🎯 1. Problem Definition

Primary ML Task

What is your main objective with this data?

Dataset Size

How much training data do you have?

Number of Features

How many input variables/columns do you have?

50 features

Data Quality & Characteristics

Select all that apply to your dataset:

Non-linear relationships

Many categorical features

Contains outliers

Missing values present

Imbalanced classes

Needs feature engineering

Interpretability vs. Performance Trade-off

How important is it to explain model decisions?

Diagnosed Performance Issue

If you've already tried a model, what's the main issue?

Configure your ML problem characteristics to receive comprehensive, professional-grade algorithm recommendations with implementation guidance! 🚀

High Match (70-100%)
Excellent fit for your problem

Medium Match (40-69%)
Good option with tuning

Low Match (0-39%)
Consider other options first