Machine Learning Project Starter Kit - Checklist Workbook

This workbook guides you through the critical phases of starting a machine learning project, from problem definition to initial model deployment. Each item includes prompts and space for notes.

Checklist

• Define the business problem and success metrics (e.g., precision/recall targets).
• Collect and audit available data sources; check for missing values and class imbalance.
• Perform exploratory data analysis (EDA) using pandas profiling and visualizations.
• Preprocess data: handle outliers, encode categorical features, scale numerical features.
• Split data into training, validation, and test sets (e.g., 70/15/15).
• Select baseline model (e.g., logistic regression) and establish performance benchmark.
• Iterate on feature engineering and model tuning using cross-validation (e.g., 5-fold).
• Document results, finalize model, and plan deployment with monitoring strategy.

Payment

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Frequently Asked Questions