what is Data Processing? Data processing in machine learning refers to the steps taken to prepare, clean, and transform the data before it is used to train a machine learning model. It is a critical step in the machine learning pipeline as the quality of the data can greatly impact the performance of the model. What are the step involve in Data Processing? Data processing can include a variety of tasks such as: Data cleaning: Removing missing or duplicate data, handling outliers, and dealing with inconsistent or inaccurate data. Data integration: Combining data from multiple sources to create a single data set. Data transformation: Scaling, normalizing, or encoding data to make it more consistent and suitable for the machine learning algorithm. Data reduction: Selecting a subset of the data to use for training and testing, or reducing the dimensionality of the data. Data splitting: Dividing the data into training and test set EDA (Exploratory Data Analysis) : EDA (Exploratory Data...
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