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Bank churn prediction dataset

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Jan 11, 2017 · Customer churn impacts the cost to the business, for example, lost revenue and the marketing costs involved with replacing those customers with new ones. Reducing customer churn is a key goal for every business. In this challenge, A Bank is looking for help from data scientists like you to help them provide insights using their past data. churn from bank credit cards. They introdu ced a hybrid approach to extract rules from SVM for customer relationship management purposes. The approach is composed of three phases where: 1) SVM-recursive feature elimin ation is applied to reduce the feature set; 2) the obtained dataset is used to build the SVM model; and 3) using NB, tree rules areAssignment 1: Predicting Customer Churn The objective of this assignment is to do a quick refresh on machine learning, to set up your favorite development environment, to process a simple dataset, apply machine learning algorithms that were studied in CS5785, and write up the results in the form of a presentation. Bank Customer Churn Prediction. Comments (12) Run. 2582.9 s. history Version 24 of 24. Exploratory Data Analysis. Classification. Model Comparison. Cell link copied. Assumptions • Due to limitation in our dataset we performed CLTV analysis on the basis of the following assumptions: - Given data contains one year of transaction details - Unit of amount is dollars - following are the margins that company is getting from their customer • 5% of day charge • 10% of evening hours • 20% of night and ...Now, this dataset consists of 10,000 customers mentioning their age, salary, marital_status, credit card limit, credit card category, etc. There are nearly 18 features. We have only 16.07% of customers who have churned. Thus, it's a bit difficult to train our model to predict churning customers. We use cookies on Kaggle to deliver our services ...