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Step by Step process of Feature Engineering for Machine Learning Algorithms in Data Science

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  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 Feature En
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Basic Ensemble Techniques in Machine Learning

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  Introduction Ensemble models in machine learning operate on a similar idea. They combine the decisions from multiple models to improve the overall performance. This can be achieved in various ways, which you will discover in this article. Ensembling is nothing but the technique to combine several individual predictive models to come up with the final predictive model. And in this article, we’re going to look at some of the ensembling techniques for both Classification and Regression problems such as Maximum voting, Averaging, Weighted Averaging, and Rank Averaging. Table of Contents Introduction to Ensemble Learning Basic Ensemble Techniques 2.1 Max Voting 2.2 Averaging 2.3 Weighted Average Advanced Ensemble Techniques 3.1 Stacking 3.2 Boosting 3.3 Bagging Algorithms based on Bagging and Boosting 4.1 Bagging meta-estimator 4.2 Random Forest 4.3 AdaBoost   1. Introduction to Ensemble Learning Let’s understand the concept of ensemble learning with an example. Suppose you are a movie di
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