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Many customers are leaving a bank all over Europe. A sample of 10K customers is given to perofrm data wrangling, modeling and mining to deliver some insights and help the bank solve the problem.
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Problem Statement : Your client is a retail banking institution. Term deposits are a major source of income for a bank. A term deposit is a cash investment held at a financial institution. Your money is invested for an agreed rate of interest over a fixed amount of time, or term. The bank has various outreach plans to sell term deposits to their customers such as email marketing, advertisements, telephonic marketing and digital marketing. Telephonic marketing campaigns still remain one of the most effective way to reach out to people. However, they require huge investment as large call centers are hired to actually execute these campaigns. Hence, it is crucial to identify the customers most likely to convert beforehand so that they can be specifically targeted via call. You are provided with the client data such as : age of the client, their job type, their marital status, etc. Along with the client data, you are also provided with the information of the call such as the duration of the call, dayand month of the call, etc. Given this information, your task is to predict if the client will subscribe to term deposit.
This repository contains Data science projects
Moringa_Data_Science_Core_W1_Independent_Project_2019_08_Josephine_Wanjiku_Python_Notebook
This was a data science challenge made for KBC bank based in Belgium, Leuven. Machine learning algorithms were used to select clients with a high propensity to own loan, credit card and mutual fund. These clients are expected to maximize the revenue of the bank.
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