Step by Step process of Feature Engineering for Machine Learning Algorithms in Data Science
Introduction Data Science is not a field where theoretical understanding helps you to start a carrier. It totally depends on the projects you do and the practice you have done that determines your probability of success. Feature engineering is a very important aspect of machine learning and data science and should never be ignored. The main goal of Feature engineering is to get the best results from the algorithms. Table of Contents Why should we use Feature Engineering in data science? Feature Selection Handling missing values Handling imbalanced data Handling outliers Binning Encoding Feature Scaling 1. Why should we use Feature Engineering in data science? In Data Science, the performance of the model is depending on data preprocessing and data handling. Suppose if we build a model without Handling data, we got an accuracy of around 70%. By applying the Feature engineering on the same model there is a chance to increase the performance from 70% to more. Simply, by using Featur...