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Getting Started

1. Install

pip install cranalytics
# or: uv add cranalytics

Visualization (matplotlib, plotly, seaborn) and survival analysis (lifelines) are included in the base install.

If you choose an external modeling backend, install that backend directly:

uv add optbinning  # optimal WoE binning
uv add xgboost     # xgboost model families
uv add lightgbm    # lightgbm challenger workflows

2. Run the canonical first command

cranalytics quickstart

This is the recommended first-run experience. It shows the package workflows, walks through synthetic examples, and tells you what real-world data shape you need next. Your goal is one first win and one clear next step, not a full tour of every command.

3. Pick the right workflow

Use one of these pages next:

If you already know the right workflow, those pages also point to the direct demo command. quickstart is still the default recommendation for a new user.

4. Validate or prepare your own data

See what columns your data needs first:

cranalytics validate-data --show-schema

Then validate your file:

cranalytics validate-data your_data.csv

For non-portfolio shapes, use the workflow-specific references and tutorials, starting with Input Data Contracts.

5. Go deeper by workflow

6. Reference