Data Science, Data Mining, Data Wrangling, Data Preparation.
attributes. Real-world data is generally noisy, incomplete and inconsistent. It implies that raw data tends to be corrupt, have missing values or attributes, outliers or conflicting values. Data preparation stage resolves such kinds of data issues to ensure the dataset used for modeling stage is