Data preprocessing, a process used in Data Mining, transforms raw information to a format suitable for use. Data found in the real world is often insufficient, inconsistent, and sometimes missing specific behaviors or patterns, with various inaccuracies thrown into the mix. Preprocessing data has proven an effective way to address these issues. In short, it's a stage in Data Mining that equips with tools for deriving knowledge from data simultaneously.

Techniques involved in data preprocessing

  1. Data integration- This technique, similar to data warehousing, combines data from various sources into a unified data store for data analytic tasks. The sources can be multiple databases, data cubes, or flat files. Schema integration is a crucial factor in Data Integration dealing with the complex issue of how real-world entities from different data sources are matched. The answer lies in the metadata, which is essentially data about data available in databases and data warehouses. It aids in preventing schema integration difficulties. Another critical aspect is redundancy. It can arise due to attribute inheritance from another table or inconsistencies in attribute or dimension names.
  2. Data transformation- This technique alters data into mining formats that suit the requirements. The procedures involved in data transformation comprise Normalisation where attribute data is scaled to fit within a specified range, such as -1.0 to 1.0 or 0 to 1.0; Smoothing irons out the data noise using techniques like binning, clustering, and regression; Aggregation is the process of applying summary procedures on data, often used for constructing data cubes for multi-level data analysis; Generalizing data replaces low-level or primitive/raw data with high-level abstractions using concept hierarchies.
  3. Data cleaning- The goal of data cleaning processes is to fill missing values, smoothen noise by identifying outliers, and rectify inconsistencies in data. Noisy and flawed attribute values can render data erroneous. This can occur due to imperfect data-gathering instruments, mistakes during data input, or data transfer errors. "Dirty" data can disrupt the mining process. So, applying multiple data cleansing algorithms can prove beneficial for reliable Data Preprocessing.
  4. Data reduction- Data reduction methods serve useful when complex data analysis on large datasets turn time-consuming or unfeasible. They focus on analyzing a reduced form of a data set that maintains the original data's integrity and still produces quality knowledge. Some data reduction strategies include Data Cube Aggregation, which applies the process of summarizing data, Dimension Reduction for identifying and trimming irrelevant or redundant features, and Data Compression using encoding approaches to decrease the data volume.
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