The core business of a financial institution can be broadly classified as lending and borrowing. Lending generates revenue to the bank in the form of interest from customers with some level of default risk involved. Borrowing, or rather attracting public’s savings into the bank is another source of revenue generation, which can be less risky than the former. A bank usually invests the customer’s long-term deposits into riskier financial assets which can earn the better return than what they pay to their customer. The customer, on the other hand, is assured a risk-free return on his/her deposit. However, the return on the fixed-term deposit is better than the savings account as the customer is deprived off the rights to use the fund prior to the maturity unless one is ready to compensate the bank as per the pre-specified agreements on the particular term deposit scheme.
There is a stiff competition among the financial institutions/banks in increasing the customer base in their retail banking segment. Along with offering innovative products to the public, a huge amount of money is spent on marketing their products. The term deposit is very important among the diverse range of products and services offered by banks in retail banking segment. With advancement in data science and machine learning and availability of data, most banks are adapting to a data-driven decision. The dataset here consists of direct marketing by contacting the clients and assessing the success rate of sales made.
In this project, we apply machine learning algorithms to build a predictive model of the data set to provide a necessary suggestion for marketing campaign team. The goal is to predict whether a client will subscribe a term deposit (variable y) with the help of a given set of dependent variables. This is a real dataset collected from a Portuguese bank that used its own contact-center to do direct marketing campaigns to motivate and attract the clients for their term deposit scheme to enhance the business.
For the notebook, all you need is to upload the csv file to your working environment and execute the cells.
For the python files:
- clone the repo.
- Pip install the requirements file.
- On the terminal, run the following command to execute and get the predictions into a csv file: python main.py 'file path'