aniyus / customer-churn-prediction

Decision Tree in Python and RapidMiner

Customer churn prediction

Key words:

Machine Learning/Data Mining (Classification Technique)

• Applied Model: Decision Tree
• Tool: Python, RapidMiner

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Data Description:

This dataset is about consumers and their decisions to terminate a contract (i.e., consumer churn problem).

Data Size: 31891 records

Col.	Var. Name	Var. Description
1	revenue	Mean monthly revenue in dollars
2	outcalls	Mean number of outbound voice calls
3	incalls	Mean number of inbound voice calls
4	months	Months in Service
5	eqpdays	Number of days the customer has had his/her current equipment
6	webcap	Handset is web capable
7	marryyes	Married (1=Yes; 0=No)
8	travel	Has traveled to non-US country (1=Yes; 0=No)
9	pcown	Owns a personal computer (1=Yes; 0=No)
10	creditcd	Possesses a credit card (1=Yes; 0=No)
11	retcalls	Number of calls previously made to retention team
12	churndep	Did the customer churn (1=Yes; 0=No)

Modeling Process:

1. Data Cleasing
2. Modeling
3. Model Evaluation
4. Model Interpretation