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Every company wants to increase its revenue and profitability. To do that, while they acquire new customers, they also want to make sure that the existing ones stay with them for a long term. Also, its strategically important to know beforehand whether a set of customers are planning to stop using their services (especially recurring ones like internet, cable, phone etc.). To do that, every company or business creates and tracks customer metrics which are then used to predict their likelihood of churn.
Customer Churn for a company occurs when a customer decides to stop using the services of that company. In this project, we will be using the customer data of a telecom sector company based in the US to predict the probability of churn for each of the customer. We will look at the standard practices that are followed in the industry to solve these problems and also go beyond just those techniques. We have chosen the telecom company data for the churn problem as it is a major area of concern for companies in that sector.
Once we have built a model, the churn model output can also be used as a warning indicator that some customers are likely to churn. The key drivers that are making the customer more likely to churn can be alleviated and ensure that the customers are actually retained.
In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.