#!/usr/bin/env python
# coding: utf-8

# # Introductory tutorial
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# This is the introduction to a five part tutorial which demonstrates how to de-duplicate a small dataset using simple settings.
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# The aim of the tutorial is to demonstarate core Splink functionality succinctly, rather that comprehensively document all configuration options.
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# The five parts are:
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# - [1. Exploratory analysis](https://moj-analytical-services.github.io/splink/demos/01_Exploratory_analysis.html)
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# - [2. Choosing blocking rules to optimise runtimes](https://moj-analytical-services.github.io/splink/demos/02_Blocking.html)
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# - [3. Estimating model parameters](https://moj-analytical-services.github.io/splink/demos/03_Estimating_model_parameters.html)
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# - [4. Predicting results](https://moj-analytical-services.github.io/splink/demos/04_Predicting_results.html)
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# - [5. Visualising predictions](https://moj-analytical-services.github.io/splink/demos/05_Visualising_predictions.html)
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# - [6. Quality assurance](https://moj-analytical-services.github.io/splink/demos/06_Quality_assurance.html)
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# Throughout the tutorial, we use the duckdb backend, which is the recommended option for smaller datasets of up to around 1 million records on a normal laptop.
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# You can find these tutorial notebooks in the `splink_demos` repo, and you can run them live in your web browser by clicking the following link:
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# [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/moj-analytical-services/splink_demos/splink3_demos?urlpath=lab)
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