codehax41 / prediction-of-customer-churn

Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs." [IBM Sample Data Sets]

Prediction-of-Customer-Churn

Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs." [IBM Sample Data Sets]

Context "Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs." [IBM Sample Data Sets] Content Each row represents a customer, each column contains customer’s attributes described on the column Metadata. The data set includes information about: • Customers who left within the last month – the column is called Churn • Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies • Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges • Demographic info about customers – gender, age range, and if they have partners and dependents

Business Context: This case requires trainees to develop a model for predicting customer churn at a fictitious wireless telecom company and use insights from the model to develop an incentive plan for enticing would-be churners to remain with company. Data for the case are available in csv format. The data are a scaled down version of the full database generously donated by an anonymous wireless telephone company. There are still 7043 customers in the database, and 20 potential predictors. Candidates can use whatever method they wish to develop their machine learning model. The data are available in one data file with 7043 rows that combines the calibration and validation customers. “calibration” database consisting of 4000 customers and a “validation” database consisting of 3043 customers. Each database contained (1) a “churn” variable signifying whether the customer had left the company two months after observation, and (2) a set of 20 potential predictor variables that could be used in a predictive churn model. Following usual model development procedures, the model would be estimated on the calibration data and tested on the validation data. This case requires both statistical analysis and creativity/judgment. I recommend you spend much time on both fine-tuning and interpreting results of your machine learning model.