IndexIntroductionResearch and Investigation TechniquesTechniques UsedB. Logistic RegressionConclusionCustomer satisfaction has a huge impact on the service delivery of any company. A simple word of mouth structures the corporate environment to improve productivity and results. With such impact from customers, it is essential to keep them on track, to know the value of the product and service. The approach used for this project is to analyze digital media users, to see if they could continue to do business with the organization, if not, make them do business with the help of increased service provision. For this analysis, a sample of digital media user data was considered to see if it might change in the future. This prediction was made with the help of machine learning techniques. The tool used for this analysis was Rapidminer. The output was shown with accurate results in statistical representation. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay IntroductionIn general, CRM (Customer Relationship Management) is a tool that helps the organization to maintain the relationship between buyers and customer interaction, keep track of their records and accounts. It helps them improve customer satisfaction. For the analysis, sample data from digital media was considered for churn prediction. This analysis is used to predict whether a customer would choose to stay with the organization even after the contractual period. This is similar to the friction model. Customer loyalty is an important aspect in any organization, where it shows the performance level of the company from low to high. Friction is also one of the main uses of data mining. In the current era, everything is going digital. The use of digital media is becoming a necessity for survival in the business environment. This helps organizations and customers to keep up to date with the trend for their own purposes. There are different forms of digital media in various formats such as audio, video, images and graphical representations. Considering the attrition model, there are three types: voluntary attrition, involuntary attrition, and intended attrition. If a customer wanted to switch to another company, it would be a voluntary abandonment. Involuntary attrition, also known as forced attrition, happens when the customer is fired from the company for any reason, some of the common reasons are unpaid invoices. Expected attrition occurs when the customer is no longer available in the target area, such as when a customer moves to another location. There are several methods to predict the outcome of this project. The main background of this project is to look at survival analysis. In this analysis, machine learning techniques are used to test the variation between them. They are deep learning and logistic regression. With the help of such techniques, it will be possible to know the most accurate method and take it into account. To perform this analysis, a tool called "Rapidminer" was used. Research and Investigate Techniques There are various techniques available to implement and get results from predicting customer churn analytics in digital media. The techniques can be machine learning techniques such as Bayesian network, deep learning or decision trees. In other way, it can also be a statistical method of prediction through logistic regression, which is mainly performed between dependent variable andother variable when the dependent variable is dichotomous. There were some previous works that were done on this project with certain techniques. All these techniques gave only the expected result. The dataset used for this project is very balanced. This helps ML techniques perform analysis and deliver effective results. In case of imbalance, the techniques will not work and will not give effective results will not be available. However, for imbalanced datasets, there is a technique called oversampling technique, which deals with classification problems, of two types. They are the synthetic minority oversampling technique and the adaptive synthetic sampling technique. This technique helps balance the data sets, which helps in performing the analysis. Another popular technique used for churn analysis is CART, which is the classification and regression tree model. This is the branch of the decision tree model. This technique mainly deals with classification and misclassification issues in the dataset. The other popular model used for this analysis was the Support Vector Machine (SVM) model. This model also works mainly on linearity classification problems. It is effective in working on linear and non-linear cases. The above-mentioned models are not limited, but they are worth mentioning about using for this churn analysis. It has a special way of applying to certain hypotheses to be more effective. Techniques Used As discussed above, there are many important techniques available in use. But in this project only two techniques are used to find churn analytics in digital media. These techniques are so popular and widely used for this type of project in churn analysis. This technique helps us not only predict the outcome, but also helps us statistically with all the factors that lead a customer to stay or choose another network. The dataset used for this project has 21 columns. The "Churn" column is the dependent variable. It is a dichotomous variable with yes or no. The independent variables are Senior, Gender, Months of stay, Telephone service, Multiple lines, Internet service, Online security, Online backup, Device protection, Technical support, TV and movie streaming, Contract length, Paper billing, Payment method, Monthly Charges and Total Expenses.A. Neural networks (Deep Learning) This is one of the most popular algorithms in the field of forecasting analysis. It is one of the branches of machine learning techniques. This big data processing is capable of analyzing large amounts of data at a given time, however it may also take some time to run through the dataset if the volume of data is very large. This technique is more flexible and scalable. The analysis was performed using the Rapidminer tool. In this test, accuracy is calculated with the overall variables. The metric type for this test is binomial. Confusion matrix algorithm is used for statistical classification of the dataset. With the help of simulation, an in-depth understanding of the type of customer who prefers the services with the invoices he receives is analyzed. To analyze the performance, tests such as precision, AUC, sensitivity, specificity, recall, f and accuracy were performed.B. Logistic RegressionThis is also one of the methods of machine learning techniques. This is the statistical method of forecasting. This method may be the best technique for this project as it deals with customer attrition cases. This analysis is meaningful when the dependent variable is dichotomous. The output is encoded as 0 or 1. In..