Why is data preprocessing important in ML?

Turning raw, messy data into clean, usable data. Clean — fix missing values, remove duplicates Transform — scale numbers, encode categories Select — keep only useful features Split — divide into train/test sets

Data preprocessing is an important step in Machine Learning because the raw data collected from different sources is often incomplete, inconsistent, noisy, or contains errors. If such data is directly used for training a model, it can reduce the accuracy and performance of the system. Data preprocessing helps in cleaning the data by removing missing values, duplicates, and irrelevant information. It also transforms the data into a suitable format through techniques like normalization, scaling, and encoding categorical values. Proper preprocessing improves the quality of the dataset, helps the model learn patterns more effectively, reduces training time, and increases the overall accuracy and reliability of Machine Learning models.