crosstherubicon / churn_prediction

Churn prediction using Random Forest and Decision Tree Classifiers.

Churn Prediction USA TODAY (GANNETT)'s Coding Challenge Problem

Churning occurs when customers leave and stop buying your product or using your service during a defined time frame.

Random Forest and Decision Tree models were used for predicting churning. The dataset contains 10000 samples and 10 columns as:

• CPL_wrt_BC=change in cost per lead with respect to business category
• client_state=client's location
• duration=how long the client has been running advertising campaigns in months
• num_prods=how many distinct advertising products the client has bought
• calls=number of calls received
• CPL_wrt_self=change in client's cost per lead in the past three months
• churn=target column (0=retention | 1=churn)
• avg_budget=average monthly budget spent on advertising campaigns
• BC=client's business category
• clicks=number of clicks received

There were missing data on column 5th column which were imputed using mean values.

The class imbalance was about

0: 8000 1: 2000

Normally, it is advised to either undersample or collect more data. But for this analysis, I moved ahead to come back after model performance analysis if necessary. The Random Forest ROC (shown first) and Decision Tree (shown second) gave reasonable results.