Telco Customer Churn Prediction

Owned by Vidya Patil
Last updated: Mar 11, 2022 by Subhajit Sengupta

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Problem Scenario

Customer churn is a big problem for service providers because losing customers results in losing revenue and could indicate service deficiencies. There are many reasons why customers decide to leave services. With data analytics and machine learning, we can identify the important factors of churning, create a retention plan, and predict which customers are likely to churn.

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Dataset

We use a dataset of telecommunication company which can be found here. This dataset contains 21 fields (columns). Each document (row) represents a customer record. We have data about customer's demographic, service subscriptions, and the field $Churn which indicates whether the customer has already left or not. There are 7043 customer records with 1869 churns. The following image shows a preview of this dataset:

Objectives

  1. Profiling: Use Profile Snap from ML Analytics Snap Pack to compute statistics of this dataset.
  2. Data Preparation: Perform data preparation on this dataset using Snaps from ML Data Preparation Snap Pack.
  3. Cross Validation: Use Cross Validator (Classification) Snap from ML Core Snap Pack to perform 10-fold cross validation on various Machine Learning algorithms. The result lets us know the accuracy of each algorithm in the churn prediction.
  4. Model Building: Use Trainer (Classification) Snap from ML Core Snap Pack to build the logistic regression model based on this dataset; then serialize and store.
  5. Model Hosting: Use Predictor (Classification) Snap from ML Core Snap Pack to host the model and build the API using Ultra Task.
  6. API Testing: Use REST Post Snap to send a sample request to the Ultra Task to make sure the API is working as expected.
  7. Visualization API: Use Remote Python Script Snap from ML Core Snap Pack to host API that can provide data visualization of the selected field in the dataset.

We are going to build 8 pipelines: one pipeline for each objective except for Cross Validation where we have 2 pipelines to automate the process of trying multiple algorithms. Each of these pipelines is described in the Pipelines section below.

Pipelines

Profiling

The File Reader Snap reads the dataset which is in CSV format. Then, the CSV Parser Snap parses the dataset into documents. In SnapLogic, there are two types of input/output view. The diamond shape is the binary view while the circle shape is the document view. Parser Snaps convert binary into document while Formatter Snaps convert document to binary.

In most cases, the output documents of CSV Parser Snap are represented as string data type even though some of them are supposed to be numeric. The Profile Snap gives different sets of statistics for numeric and categorical fields, so, we use the Type Converter Snap to automatically convert the data types. Then, we use JSON Formatter and File Writer Snaps to write the data statistics as a file on SnapLogic File System (SLFS). The Profile Snap also has a second output which is an interactive profile of the dataset. We use Document to Binary and File Writer Snaps to save the interactive profile in HTML format.

In this dataset, there is a field $SeniorCitizen which is categorical but contains 1 and 0 instead of Yes and No. The auto mode of the Type Converter Snap thinks that this field is numeric and converts those 0 and 1 into an integer. So, we need to add Policy that tells the Snap to convert values in $SeniorCitizen to string data type which represents categorical data.

Let's take a look at the data statistics computed by the Profile Snap.

Below shows the profile of $Churn field. As you can see, 1869 of customers have already churned. There are 7043 - 1869 = 5174 who are stilling using the services.

The picture below shows the histogram of $tenure field. There are 1612 customers (including churn) who use the services for less then 7.2 months. There are also large amount of customers who use the services for 64.8 - 73 months. The significant drops from 0 - 7.2 compared to 7.2 - 14.4 could indicate that customers churn a lot in the early stage.

Data Preparation

As mentioned in the previous pipeline section, $customerID field should be removed from the dataset and the missing values in $TotalCharges should be handled.

The Mapper Snap removes $customerID field from the input documents. In the Snap configuration, Pass through is selected to output all the fields. However, $customerID is removed according to the transformation we specify. Leaving the Target path property empty means removing the input field.

The Clean Missing Values Snap imputes missing values in $TotalCharges with the average value. When using Impute with Average, the data statistics are required. We use File Reader1 and JSON Parser Snaps to read the data statistics generated by the Profile Snap in the previous pipeline.

Cross Validation

In this step, we perform a 10-fold cross validation with various classification algorithms. Cross validation is a technique to evaluate how well a specific machine learning algorithm performs on a dataset. To keep things simple, we use accuracy to indicate the quality of the predictions. The baseline accuracy is 50% because we have two possible labels: churn and not churn. However, based on the data statistics of this dataset, there are 5174 customers who are not churning out of 7043 total customers. The baseline accuracy should be 5174 / 7043 = 73.5% in case we predict that all customers are not churning.

We have 2 pipelines: parent and child. The parent pipeline uses the Pipeline Execute Snap to execute the child pipeline with different parameters. The parameter is the algorithm name which will be used by the child pipeline to perform the 10-fold cross validation. The overall accuracy and other statistics will be sent back to the parent pipeline which will write the results to SLFS and also use the Aggregate Snap to find the best accuracy.

Child Pipeline

The File Reader Snap reads the processed dataset generated by the data preparation pipeline and feeds the dataset into the Cross Validator (Classification) Snap. The output of this pipeline is sent to the parent pipeline.

In the Cross Validator (Classification) Snap, Label field is set to $Churn which is the one we want to predict. Normally, you can select Algorithm from the suggested list. However, we will automate trying multiple algorithms with Pipeline Execute Snap so we set the Algorithm to the pipeline parameter. The default pipeline parameter can be set in the pipeline setting.

Parent Pipeline

This pipeline uses the Pipeline Execute Snap to execute the child pipeline multiple times with different classification algorithms. The goal is to find the algorithm that performs the best in this dataset.

Below is the content of the CSV Generator Snap. It contains a list of algorithms we want to try.