One of the most famous and useful case studies of churn prediction is in the telecom industry. It is important for telecom companies to analyze all relevant customer data and develop a robust and accurate Churn Prediction model to retain customers and to form strategies for reducing customer attrition rates. In this project, Telco Customer Churn Dataset which is available at Kaggle is used. Two numerical columns: 1. MonthlyCharges: The amount charged to the customer monthly 2. TotalCharges: The total amount charged to the customer Eighteen categorical columns: 1. CustomerID: Customer ID unique for each customer 2. gender: Whether the customer is a male or a female 3. SeniorCitizen: Whether the customer is a senior citizen or not (1, 0) 4. Partner: Whether the customer has a partner or not (Yes, No) 5. Dependents: Whether the customer has dependents or not (Yes, No) 6. Tenure: Number of months the customer has stayed with the company 7. PhoneService: Whether the customer has a phone service or not (Yes, No) 8. MultipleLines: Whether the customer has multiple lines or not (Yes, No, No phone service) 9. InternetService: Customer's internet service provider (DSL, Fiber optic, No) 10. OnlineSecurity: Whether the customer has online security or not (Yes, No, No internet service) 11.
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.
Banks, telephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics (along with cash flow, EBITDA, etc.) because the cost of retaining an existing customer is far less than acquiring a new one (Wikipedia). According to this article, the probability of selling to a new customer is 60-70%, while the probability of selling to a new prospect is 5-20%. So knowing if a customer is at risk of leaving is one of the most important tasks a company has to perform in order to keep growing its business. The data can be found here on kaggle public datasets. We will predict if a customer will churn based on his informations. There are 7043 customers and 20 features.
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 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.