How can you handle data duplication in data cleaning for specific domains?

Data duplication, where multiple entries represent the same information in a dataset, can result from various factors like human errors or inconsistent data formats. This duplication can harm machine learning models by reducing accuracy and analysis validity, increasing data complexity and size (impacting storage and processing), introducing bias and noise, and violating algorithm assumptions and constraints, such as in linear regression or clustering.

When several records or entries in a dataset represent the same thing or piece of information, this is known as data duplication. Human error, uneven formatting, data merging or appending, and web page scraping are some of the possible causes of this. Your machine learning models may suffer from data duplication in a number of ways, including decreased validity and accuracy of analysis and results, increased complexity and size of data that may impact processing, storage, and memory needs, introduction of bias and noise into your data that may compromise the generalization and robustness of your models, and breaking the rules and assumptions of some algorithms, such as clustering or linear regression.

To detect data duplication, start by identifying it within your dataset. Depending on your data type and format, various methods and tools can be employed. These include built-in functions or libraries in your programming language or framework, hashing or checksum techniques, fuzzy matching or similarity measures, and domain-specific tools or algorithms. For instance, you can use pandas in Python or dplyr in R to find and count duplicate rows or columns. Natural language processing techniques are useful for detecting duplicates in text data, while image processing methods can be applied to find duplicates in image data.

First define the scope of what level of duplication you wish to remove. For example, for a key value pair, do you consider one value repeating as a duplicate or all values repeating as duplicates. This in turn would help you choose the right tool to remove duplicates. In python, df.drop_duplicates(subset=None, keep='first') will help you drop all duplicates it finds after the first value of the subset, where subset is the values' scope you defined. In R, one would use dplyr. Alternatively, there are other deduplication software in market as well and of course, for a smaller dataset one can rely on manual inspection and validation

Utilize unique identifiers to identify and merge duplicates. For text-based data, employ techniques like fuzzy matching and string similarity metrics to identify similar records. In the healthcare domain, consider additional matching criteria like patient ID.