Deduplication
Deduplication is the process of identifying and removing duplicate records from a dataset. In data science, deduplication is an important step in data preprocessing, as it helps to ensure data quality and accuracy. Deduplication can be performed using various techniques, such as rule-based matching, probabilistic matching, and machine learning-based matching. Rule-based matching involves defining a set of rules to identify duplicates based on specific criteria, such as name, address, and phone number. Probabilistic matching uses statistical algorithms to calculate the probability of two records being a match. Machine learning-based matching involves training a model to identify duplicates based on a set of features extracted from the data. Deduplication is commonly used in various applications, such as customer relationship management, fraud detection, and healthcare analytics.
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