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Overview A complete tutorial on data exploration (EDA) We cover several data exploration aspects, including missing value imputation, outlier removal and the art of feature engineering   Introduction There are no shortcuts for data exploration. If you are in a state of mind, that machine learning can sail you away from every data storm, trust me, it won’t. After some point of time, you’ll realize that you are struggling at improving model’s accuracy. In such situation, data exploration techniques will come to your rescue. I can confidently say this, because I’ve been through such situations, a lot. I have been a Business Analytics professional for close to three years now. In my initial days, one of my mentor suggested me to spend significant time on exploration and analyzing data. Following his advice has served me well. I’ve created this tutorial to help you understand the underlying techniques of data exploration. As always, I’ve tried my best to explain these concepts in the simple

1. What do you mean by the Logistic Regression? It’s a classification algorithm that is used where the target variable is of categorical nature. The main objective behind Logistic Regression is to determine the relationship between features and the probability of a particular outcome. For Example,  when we need to predict whether a student passes or fails in an exam given the number of hours spent studying as a feature, the target variable comprises two values i.e. pass and fail. Therefore, we can solve classification problem statements which is a supervised machine learning technique using Logistic Regression. 2. What are the different types of Logistic Regression? Three different types of Logistic Regression are as follows: 1. Binary Logistic Regression:  In this, the target variable has only two 2 possible outcomes. For Example,  0 and 1, or pass and fail or true and false. 2. Multinomial Logistic Regression:  In this, the target variable can have three or more possible values witho