Techniques such as data cleaning, normalization, encoding categorical variables, handling missing data, and feature scaling.
Data Preprocessing for Machine Learning Data preprocessing is a crucial step in the machine learning pipeline that involves cleaning and transforming raw data into a format suitable for training a model. 1. Data Cleaning: Handling missing values: Impute missing values using techniques like mean, median, or mode imputation. Outlier detection: Identify and handle outliers that can skew the model's...…
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