EECS738-Lab 7 Midterm Solved

  Acme Telephonica (AT) is a mobile phone operator that  has customers across every state of the U.S.A.
 

 AT struggles with customer churn prediction—customers  leaving AT for other mobile phone operators.

 AT hired us to take a new approach to reducing customer  churn.

This case study is to develop a machine learning solution to this business  problem.


  AT did not approach us with a well-specified machine learning problem. Instead, the company approached us with a business problem—reducing customer churn.
 

Our first goal is to convert this business problem into a machine learning problem and develop a concrete solution.

To evaluate the available data, we have the data definitions.

Feature
Description
BILLAMOUNTCHANGEPCT
The percent by which the customer’s bill has changed from  last
CALLMINUTESCHANGEPCT

AVGBILL  

AVGRECURRINGCHARGE  

AVGDROPPEDCALLS  

PEAKRATIOCHANGEPCT

AVGRECEIVEDMINS

AVGMINS  

AVGOVERBUNDLEMINS

AVGROAMCALLS

PEAKOFFPEAKRATIO
month to thismonth

The percent by which the call minutes used by the customer has  changed from last month to this month

The average monthly bill amount

The average monthly recurring charge paid by the customer  The average number of customer calls dropped each month

The percent by which the customer’s peak calls to off-peak calls ratio has changed from last month to this month

The average number of calls received each month by the customer

The average number of call minutes used by the customer each month

The average number of out-of-bundle minutes used by the customer eachmonth

The average number of roamingcallsmade by the customer each month

The ratio between peak and off peak calls made by the customer  thismonth
NEWFREQUENTNUMBERS
How many new numbers the customer is frequently calling   this month?
 

Feature
Description
CUSTOMERCARECALLS
The number of customer care calls made by the customer last
NUMRETENTIONCALLS

NUMRETENTIONOFFERS  

AGE

CREDITRATING  

INCOME  

LIFETIME  

OCCUPATION  

REGIONTYPE  

HANDSETPRICE  

HANDSETAGE  

NUMHANDSETS  

SMARTPHONE  

CHURN
month

The number of times the customer has been called by the retentionteam

The number of retention offers the customer has accepted

The customer’sage

The customer’s credit rating  

The customer’s incomelevel

The number of months the customer has been with AT

The customer’soccupation

The type of region the customer lives in  

The price of the customer’s current handset  

The age of the customer’s current handset

The number of handsets the customer has had in the past 3 years

Is the customer’s current handset a smart phone?  

The targetfeature