Happy Customer Bank is a mid-sized private bank that deals in all kinds of banking products, like Savings accounts, Current accounts, investment products, credit products, among other offerings.
The bank also cross-sells products to its existing customers and to do so they use different kinds of communication like tele-calling, e-mails, recommendations on net banking, mobile banking, etc.
In this case, the Happy Customer Bank wants to cross sell its credit cards to its existing customers. The bank has identified a set of customers that are eligible for taking these credit cards.
Now, the bank is looking for your help in identifying customers that could show higher intent towards a recommended credit card, given:
Customer details (gender, age, region etc.) Details of his/her relationship with the bank (ChannelCode,Vintage, 'AvgAsset_Value etc.)
- Null values in Credit_Product feature was filled with 'no_cp'. As these ID were not having credit product.
- Plotted probability density in seaborn for numerical features i.e, Age, Vinatge, Avg_Account_Balance. (a) Avg_Account_Balance feature was highly skewed. (b) Boxplot: Avg_Account_Balance vs target categorical variable gave information that Avg_Account_Balance does not have much affect on target variable. (c) Tried encoding into bins, transformation of Avg_Account_Balance but removing this feature helped.
- All other categorical features were encoded.
- Gender feature was removed based on experiments which also helped.
- Tried adding many new combination of features, but result was not improved.
- Tried Random Forest, LightGBM and CatBoost.
- Catboost gave best result on validation set.
- Class weight was also tried seeing nearly 3:1 of data, but result was not improved.
