asmitabhardwaj/Logistic_Regression

Performing EDA and creating logistic regression model on Algerian forest fire dataset # Logistic_Regression Performing EDA and creating logistic regression model on Algerian forest fire dataset Application Flow Logistic Regression is one of the most fundamental algorithms for classification in the Machine Learning world. But before proceeding with the algorithm, let’s first discuss the lifecycle of any machine learning model. This diagram explains the creation of a Machine Learning model from scratch and then taking the same model further with hyperparameter tuning to increase its accuracy, deciding the deployment strategies for that model and once deployed setting up the logging and monitoring frameworks to generate reports and dashboards based on the client requirements. A typical lifecycle diagram for a machine learning model looks like: Introduction In linear regression, the type of data we deal with is quantitative, whereas we use classification models to deal with qualitative data or categorical data. The algorithms used for solving a classification problem first predict the probability of each of the categories of the qualitative variables, as the basis for making the classification. And, as the probabilities are continuous numbers, classification using probabilities also behave like regression methods. Logistic regression is one such type of classification model which is used to classify the dependent variable into two or more classes or categories. Why don’t we use Linear regression for classification problems? Let’s suppose you took a survey and noted the response of each person as satisfied, neutral or Not satisfied. Let’s map each category: Satisfied – 2 Neutral – 1 Not Satisfied – 0 But this doesn’t mean that the gap between Not satisfied and Neutral is same as Neutral and satisfied. There is no mathematical significance of these mapping. We can also map the categories like: Satisfied – 0 Neutral – 1 Not Satisfied – 2 It’s completely fine to choose the above mapping. If we apply linear regression to both the type of mappings, …