credit-card-lead-prediction

CREDIT CARD LEAD PREDICTION

The main aim of our project was to find out customers that would show higher intent towards a recommended credit card or not.

1. Having a predefined set of customers that are eligible for taking these credit cards, we had to deal with a classification problem.
2. This way the bank would be able to better allocate its marketing efforts, leading in more efficient results.

Key Conclusions

1. If we were about to choose for a well defined model, based on key metrics (F1 - Score & Cohen’s Kapa), we would most probably choose the Gradient Boosted Trees Algorithm, after applying oversampling (F1: 68,5%, Cohen’s: 58,3%).
2. It is indeed a quite efficient algorithm even when we check our results using the KPIs (Net Profit: 834.000)
3. But, taking into account the main objective of all the companies, which is the profit, we end-up selecting the Logistic Regression after applying under-sampling, which offers higher net profit (Net Profit: 1.061.000)

The dataset was obtained through Kaggle (https://www.kaggle.com/code/ankitabanerji/credit-card-lead-prediction-eda/data?select=train.csv) and the analysis was made using the KNIME Software.