Prediction Model Setup Guide AI Prompt

Guide me through setting up a prediction model for my problem using a low-code or AutoML tool.

I want to predict: {{target_variable}}
Using data from: {{data_source}}
Tool I am using: {{tool_name}} (e.g. MLJAR Studio, DataRobot, Google AutoML, H2O.ai)

1. Before I build the model — data preparation:
   - What does my data need to look like before I feed it to the model?
   - Which columns should I include as inputs and which should I exclude? (e.g. exclude columns that would not be available at prediction time, exclude columns that directly reveal the answer)
   - How many rows do I need? Is my current dataset large enough?
   - Does my target variable (the thing I want to predict) need any preparation?

2. Common mistakes to avoid before pressing 'build':
   - Data leakage: including a column that tells the model the answer directly (e.g. using 'was refunded' to predict 'will churn' — if someone was refunded they already churned)
   - Using the future to predict the past: make sure all your input columns only use information that was available at the time you would have made the prediction
   - Predicting something that does not actually need prediction: if 95% of cases are one class, always predicting that class will look accurate but is useless

3. Setting up the model in {{tool_name}}:
   - Walk me through the key settings I need to configure: target column, problem type, training/test split, and the main metric to optimize
   - Which metric should I use to evaluate this model given my business goal?

4. Interpreting the first results:
   - What should I look at first in the results?
   - What does 'good enough' look like for my use case?
   - What are the most common reasons a first model underperforms?

5. If the model is not good enough:
   - What are my options? (more data, better features, different model type, different problem framing)