The bank wants to improve their services. For instance, the bank managers have only vague idea, who is a good client (whom to offer some additional services) and who is a bad client (whom to watch carefully to minimize the bank loses). Fortunately, the bank stores data about their clients, the accounts (transactions within several months), the loans already granted, the credit cards issued. The bank managers hope to improve their understanding of customers and seek specific actions to improve services. A mere application of a discovery tool will not be convincing for them.
To test a data mining approach to help the bank managers, it was decided to address two problems, a descriptive and a predictive one. While the descriptive problem was left open, the predictive problem is the prediction of whether a loan will end successfuly.
The data about the clients and their accounts consist of following relations:
- relation account (4500 objects - each record describes static characteristics of an account,
- relation client (5369 objects) - each record describes characteristics of a client,
- relation disposition (5369 objects) - each record relates together a client with an account i.e. this relation describes the rights of clients to operate accounts,
- relation permanent order (6471 objects) - each record describes characteristics of a payment order,
- relation transaction (1056320 objects) - each record describes one transaction on an account,
- relation loan (682 objects) - each record describes a loan granted for a given account,
- relation credit card (892 objects) - each record describes a credit card issued to an account,
- relation demographic data (77 objects) - each record describes demographic characteristics of a district.
Each account has both static characteristics (e.g. date of creation, address of the branch) given in relation "account" and dynamic characteristics (e.g. payments debited or credited, balances) given in relations "permanent order" and "transaction". Relation "client" describes characteristics of persons who can manipulate with the accounts. One client can have more accounts, more clients can manipulate with single account; clients and accounts are related together in relation "disposition". Relations "loan" and "credit card" describe some services which the bank offers to its clients; more credit cards can be issued to an account, at most one loan can be granted for an account. Relation "demographic data" gives some publicly available information about the districts (e.g. the unemployment rate); additional information about the clients can be deduced from this.
- In R, develop a function that shows how many values are missing or N/A from every sheet.
- Develop a function that calculates the client age based on the integer in the column birth_number. The initial date format was YYMMDD so we needed to parse it. We did a strsplit of the first two digits and added 1900 to match the YYYY pattern. With that, we created a new column "age" and deleted the old one (birth_number). Using the same approach we did the same for the other dimensions
- district correlation - to see if there is any correlation between attributes. We could conclude that districts with more inhabitants and ratio_urban have an higher average_salary.
- loan correlation - Loans amount is highly related with its duration. Higher the amount, longer duration. (as expected)
Did some improvements on data with R:
- Merged client with districts
- Merged with disposition (pipe between account and client)
- Merged with account
- Merged with a new table meansTemp that calculates the mean of the amount and balance of each account.
- Merged with Loan
- converted columns unemploymant.rate..95 and no..of.commited.crimes..95 to numeric
- changed the name of the column code, relation District to district_id to be easier to join with client.
- NAs districts values changed to the median of all the other attributes values'.