Data preprocessing is the process of cleaning and organizing raw data before it is used for analysis or modeling. This step is crucial because real-world data often contains errors, missing values, or irrelevant information that can lead to inaccurate results. By addressing these issues, data preprocessing helps ensure that the data is in a suitable format for further analysis. Common techniques in data preprocessing include data cleaning, data transformation, and data normalization. Data cleaning involves removing duplicates and correcting errors, while data transformation may include converting data types or aggregating data. Data normalization ensures that different scales of data do not skew the analysis, making the dataset more consistent and reliable.