Data preprocessing is the first step in any data analysis or machine learning pipeline. It involves cleaning, transforming and organizing raw data to ensure it is accurate, consistent and ready for modeling. It has a big impact on model building such as:
- Clean and well-structured data allows models to learn meaningful patterns rather than noise.
- Properly processed data prevents misleading inputs, leading to more reliable predictions.
- Organized data makes it simpler to create useful inputs for the model, enhancing model performance.
- Organized data supports better Exploratory Data Analysis (EDA), making patterns and trends more interpretable.