Results explanation

After we passed our model-evaluation stage and decided to select the estimated and evaluated model as our final models, our next task is to interpret the results to the telco company and their clients.

In terms of explaining the machine learning results, the telco company is particularly interested in understanding what influences the Call Center call volume as well as what impacts the subscriber churn. Of course, they are also open to other special insights.

We will work on these tasks, with our focus on big influencing features and some special insights.

Descriptive statistics and visualizations

With R or SPSS on Spark, as well as MLlib in place, one advantage is to obtain analytical results fast. So, quickly, we have obtained the following insights as summarized by the following tables.

For subscriber churn, we have the following two tables that summarize the subscriber churn ratios, per their phone manufactures and per our market segments of six main categories. Producing...