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Arun Velu


Logistic regression is a technique that uses statistics to develop a prediction model on any occurrence that is binary in itself and its nature (Ahmad et al., 2021). When it comes to a binary event, it may either occur or not occur. A binary nature has only two results which are represented in form of 0 (non-occurrence) and 1 (occurrence). Another application for logistic regression is where there are more than two classifications on the dependent variable. Logistic regression can be binary when the classification of the dependent variable is in two groups while it could be multinomial when the dependent variable is two groups or more. Predictive modeling is a technique where the known results are taken and develop a model that will help in predicting the later activities and occurrence (Midi et al., 2010). It uses ancient data to predict events that will occur in the future. Predictive modeling comes in different types which are ANOVA, logistic regression, decision trees, time series, neutral networks, linear regression and ridge regression. It is very critical in selecting the right model for regression to save on time in a project. Selecting incorrect modeling may result in the synthesis of a wrong prediction and non constant mean and varying variances (Hosmer et al., 2013) Variances should be constant and not varying. Consequently, regression analysis predicts continuous variance target from more than one independent variable. Regression analysis utilizes the natural variance and not a variance that have gone through experiments since they are manipulated and cannot produce the correct result. Predictive churn model is used to explain customer churn or a customer stepping down on a product or a service. This model provides quantifiable matrices and alertness to fight the retention effort (Harrell 2015). The probable monthly churn; the number of active users who churned in divided by total number of active user days, this will provide the number of churns in every user day. I hope this study will educate practitioners in the estimation of the independent variable and determining the risk factors. It’s helpful because probability produces results for independent variable and result variable in multiple models and/or binary events. When the result of two or more variable is established, it becomes easy to understand and organize a business examination for decision-making.

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Arun Velu. (2021). APPLICATION OF LOGISTIC REGRESSION MODELS IN RISK MANAGEMENT. International Journal of Innovations in Engineering Research and Technology, 8(04), 251–260. Retrieved from