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I'm a Graduate student and came into the job market and found a university degree wasn't sufficient to get a good paying job. I aimed at hottest technology in the market Big Data but the word BigData... Read More
I have extensive experience in data management and data processing. Over the past few years I saw the data management technology transition into the Big Data ecosystem and I needed to follow suit. I... Read More
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.
Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.
In this machine learning project, you will build predictive models to identify wine preferences of people using physiochemical properties of wines and help restaurants recommend the right quality of wine to a customer.
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.