This study begins with an overview of data preprocessing, focusing on real-world data challenges. Before any data analysis method begins, these are the first problems that have got to be understood and resolved. In this work, the author discusses data preprocessing, like standardization and normalization including feature scaling to more readily accomplish the data classification. Finding the most informative collection of features is the goal of preprocessing to boost the classifier’s performance. Include standardization is for the most part expected to take out the impact of a few quantitative highlights estimated on various scales. Besides, feature scaling is used to normalize all different numeric numbers to properly scaled numbers. The point of this part is to help analysts in picking a fitting preprocessing procedure for information investigation. The basic preprocessing methods used for the characterization of information are then addressed in this section. Fitting Python features to various information applications will be shown as concrete examples at the end of each session.