The process of selecting, altering, or developing new features (variables) from raw data to enhance the performance of machine learning models is known as feature engineering. It involves identifying the data's most important information and presenting it in a way that will assist the model see patterns and make more precise predictions. Scaling, one-hot encoding, imputing missing values, and creating additional features using mathematical operations or domain expertise are some examples of feature engineering jobs. In order to maximise the prediction ability of machine learning algorithms, it is essential.It involves a set of techniques that enable us to create new features by combining or transforming the existing ones. These techniques help to highlight the most important patterns and relationships in the data, which in turn helps the machine learning model to learn from the data more effectively