1. Data prep prerequisites - Splink
Splink performs optimally with cleaned and standardized data. Here is a non-exhaustive list of suggestions for data cleaning rules to enhance matching accuracy:
Data Prerequisites ¶ Splink requires that you clean your data and assign unique IDs to rows before linking. This section outlines the additional data cleaning steps needed before loading data into Splink. Unique IDs ¶ Each input dataset must have a unique ID column, which is unique within the dataset. By default, Splink assumes this column will be called unique_id , but this can be changed with the unique_id_column_name key in your Splink settings. The unique id is essential because it enables Splink to keep track each row correctly. Conformant input datasets ¶ Input datasets must be conforman
Explore this link on the map →related reading
- 聚合、联接或合并数据 - Tableauhelp.tableau.com
- 2. Exploratory analysis - Splinkmoj-analytical-services.github.io
- SMOTE and Tomek Links for imbalanced data | Kagglekaggle.com
- Defining and customising comparisons - Splinkmoj-analytical-services.github.io
- Link type - linking vs deduping - Splinkmoj-analytical-services.github.io
- 12 Tidy data | Python for Data Sciencebyuidatascience.github.io
- Backends overview - Splinkmoj-analytical-services.github.io
- DataCamp - Cleaning Data in Python | Joannajoannaoyzl.github.io
- LendingClub Loan Data Prediction | Kagglekaggle.com
- Making sure you're not a bot!hal.science
- The Great Data Integration Schlep — LessWronglesswrong.com
- Best Practices for Organizing Data - Honeycomb Docsdocs.honeycomb.io