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 help in identifying customers that could show higher intent towards a recommended credit card, given:
- Customer details (Gender, Age, Region_Code etc.)
- Details of his/her relationship with the bank (Channel_Code, Vintage, Avg_Account_Balance etc.)
- Jupyter Notebook
- Python
- Numpy
- Pandas
- Pandas_Profiling
- Matplotlib
- Seaborn
- Sklearn
- LightGBM
- XGBoost
- CatBoost
- Notebook File
- EDA File
- Approach File
- Final Submission File
- Sample Submission File
- Test File
- Train File
By using this analysis, we can predict whether the Credit Card Lead will be converted for a customer from future. So, that we can group those customers into segments and give them some surprise offers as the lead conversion rate will improve, which will increase the profit of the bank.