Customer churn classification using predictive machine learning models - WebSystemer.no

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Metis Data Science Bootcamp has been rigorous, and this is my third project. The goal is to predict customer churn in a Telecommunication company. Customer attrition, customer turnover, or customer defection -- they all refer to the loss of clients or customers, ie, churn. This can be due to voluntary reasons (by choice) or involuntary reasons (for example relocation). In this article, we will explore 8 predictive analytic models to assess customers' propensity or risk to churn.


Customer churn prediction using Neural Networks with TensorFlow.js Deep Learning for JavaScript Hackers (Part IV) - Adventures in Artificial Intelligence

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TL;DR Learn about Deep Learning and create Deep Neural Network model to predict customer churn using TensorFlow.js. First day! You've landed this Data Scientist intern job at a large telecom company. You can't stop dreaming about the Lambos and designer clothes you're going to get once you're a Senior Data Scientist. Even your mom is calling to remind you to put your Ph.D. in Statistics diploma on the wall. This is the life, who cares about that you're in your mid-30s and this is your first job ever.


Customer churn prediction with Keras and Pandas – GDG SRILANKA – Medium

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Customer churn or customer attrition is the loss of existing customers from a service or a company and that is a vital part of many businesses to understand in order to provide more relevant and quality services and retain the valuable customers to increase their profitability. In this post we will try to predict customer churn for a telco operator. We will be using a dataset from IBM Watson analytics community. We'll then read the csv file in to a pandas dataframe. In this post we are using a relatively small dataset which can be easily stored in the memory but if you are using a bigger file(s) it's highly recommended to look in to Tensorflow Dataset API which is beyond the scope of this post.


Customer Analytics: Using Deep Learning With Keras To Predict Customer Churn

@machinelearnbot

Customer churn is a problem that all companies need to monitor, especially those that depend on subscription-based revenue streams. The simple fact is that most organizations have data that can be used to target these individuals and to understand the key drivers of churn, and we now have Keras for Deep Learning available in R (Yes, in R!!), which predicted customer churn with 82% accuracy. We're super excited for this article because we are using the new keras package to produce an Artificial Neural Network (ANN) model on the IBM Watson Telco Customer Churn Data Set! As for most business problems, it's equally important to explain what features drive the model, which is why we'll use the lime package for explainability. In addition, we use three new packages to assist with Machine Learning (ML): recipes for preprocessing, rsample for sampling data and yardstick for model metrics. These are relatively new additions to CRAN developed by Max Kuhn at RStudio (creator of the caret package). It seems that R is quickly developing ML tools that rival Python. Good news if you're interested in applying Deep Learning in R! We are so let's get going!! Customer churn refers to the situation when a customer ends their relationship with a company, and it's a costly problem. Customers are the fuel that powers a business. Further, it's much more difficult and costly to gain new customers than it is to retain existing customers. As a result, organizations need to focus on reducing customer churn. The good news is that machine learning can help.


Customer Analytics: Using Deep Learning With Keras To Predict Customer Churn

@machinelearnbot

Customer churn is a problem that all companies need to monitor, especially those that depend on subscription-based revenue streams. The simple fact is that most organizations have data that can be used to target these individuals and to understand the key drivers of churn, and we now have Keras for Deep Learning available in R (Yes, in R!!), which predicted customer churn with 82% accuracy. We're super excited for this article because we are using the new keras package to produce an Artificial Neural Network (ANN) model on the IBM Watson Telco Customer Churn Data Set! As for most business problems, it's equally important to explain what features drive the model, which is why we'll use the lime package for explainability. In addition, we use three new packages to assist with Machine Learning (ML): recipes for preprocessing, rsample for sampling data and yardstick for model metrics. These are relatively new additions to CRAN developed by Max Kuhn at RStudio (creator of the caret package). It seems that R is quickly developing ML tools that rival Python. Good news if you're interested in applying Deep Learning in R! We are so let's get going!!