Code and links to a telecom project did for a company in India Data Preparation The following data preparation steps are crucial for this problem:
This is one of the most important parts of data preparation since good features are often the differentiators between good and bad models. Use your business understanding to derive features you think could be important indicators of churn.
As mentioned above, you need to predict churn only for the high�value customers. Define high-value customers as follows: Those who have recharged with an amount more than or equal to X, where X is the 70th percentile of the average recharge amount in the first two months (the good phase).
Now tag the churned customers (churn=1, else 0) based on the fourth month as follows: Those who have not made any calls (either incoming or outgoing) AND have not used mobile internet even once in the churn phase. The attributes you need to use to tag churners are:
total_ic_mou_9
total_og_mou_9
vol_2g_mb_9
vol_3g_mb_9Techvantage Confidential
Build models to predict churn. The predictive model that you’re going to build will serve two purposes: It will be used to predict whether a high-value customer will churn or not, in near future (i.e. churn phase). By knowing this, the company can take action steps such as providing special plans, discounts on recharge etc.
It will be used to identify important variables that are strong predictors of churn. These variables may also indicate why customers choose to switch to other networks. In some cases, both of the above-stated goals can be achieved by a single machine learning model. But here, you have a large number of attributes, and thus you should try using a dimensionality reduction technique such as PCA and then build a predictive model. After PCA, you can use any classification model. Also, since the rate of churn is typically low (about 5-10%, this is called class-imbalance) - try using techniques to handle class imbalance.