Preprocessing is a vital step in data analysis to ensure data quality and reliability. Common preprocessing steps include:
Data cleaning: Removing noise and outliers from the dataset. Normalization: Scaling data to a standard range to facilitate comparison. Transformation: Applying mathematical functions to stabilize variance and make data more normally distributed. Feature selection: Identifying the most relevant variables for analysis.