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Welcome to theSplink Workflow Guide!

This repository provides a structured approach to entity resolution usingSplink, enabling efficient record linkage and deduplication at scale.

Splink is a Python package for probabilistic record linkage (entity resolution) that allows you to deduplicate and link records from datasets without unique identifiers.

This workflow demonstrates best practices to:

• Clean and preprocess data for entity resolution.
• Configure Splink for optimal matching.
• Perform scalable and efficient record linkage.
• Evaluate and fine-tune results for accuracy.

To get started, install the required dependencies with my chosen backend - spark:

!pip install'splink[spark]'

Follow these steps to use this workflow:

1. Prepare your data – Ensure datasets are cleaned and formatted properly.
2. Define linkage rules – Configure comparison and scoring functions.
3. Run Splink – Execute entity resolution with your chosen backend (Spark, DuckDB, etc.).
4. Evaluate results – Analyze and refine matches for accuracy.

To get a basic Splink model up and running, use the following code. It demonstrates how to:

Estimate the parameters of a deduplication modelUse the parameter estimates to identify duplicate recordsUse clustering to generate an estimated unique person ID.

importsplink.comparison_libraryasclfromsplinkimportDuckDBAPI,Linker,SettingsCreator,block_on,splink_datasetsdb_api=DuckDBAPI()df=splink_datasets.fake_1000settings=SettingsCreator(link_type="dedupe_only",comparisons=[cl.NameComparison("first_name"),cl.JaroAtThresholds("surname"),cl.DateOfBirthComparison("dob",input_is_string=True,        ),cl.ExactMatch("city").configure(term_frequency_adjustments=True),cl.EmailComparison("email"),    ],blocking_rules_to_generate_predictions=[block_on("first_name","dob"),block_on("surname"),    ])linker=Linker(df,settings,db_api)linker.training.estimate_probability_two_random_records_match(    [block_on("first_name","surname")],recall=0.7,)linker.training.estimate_u_using_random_sampling(max_pairs=1e6)linker.training.estimate_parameters_using_expectation_maximisation(block_on("first_name","surname"))linker.training.estimate_parameters_using_expectation_maximisation(block_on("email"))pairwise_predictions=linker.inference.predict(threshold_match_weight=-5)clusters=linker.clustering.cluster_pairwise_predictions_at_threshold(pairwise_predictions,0.95)df_clusters=clusters.as_pandas_dataframe(limit=10)

You can tweak the following settings for better results:

• Blocking rules: Define rules to limit the number of comparisons.
• Scoring functions: Adjust parameters for similarity calculations.
• Threshold tuning: Optimize match acceptance criteria.

📁 splink/├── 📜 README.md├── 📦 requirements.txt├── 📁 data/         # Sample datasets├── 📁 notebooks/    # Jupyter notebooks  │   ├── 📁 tutorials/  # Step-by-step guides and explanations  │   ├── 📁 examples/   # Practical use cases and sample workflows  ├── 📁 scripts/      # Python scripts for automation  └── 📁 results/      # Output match results

This workflow is useful for:

• Customer data deduplication
• Fraud detection through identity matching
• Merging datasets across different sources
• etc. imagination is the limit

Contributions are welcome! Feel free to submit issues, suggestions, or pull requests.

Happy Linking! 🚀