BITE302P : DATABASE SYSTEMS LAB BANKING SYSTEM Bank_Master(Code, Name, Address \{doomo, street, landmark, city, state, pincode\} Customer (CID, Aadhamo, Name, Address \{doorno, street, landmarks, city, state, pincode\}, DoB, anyremarks) Account(AID type, balance, bank_code, CID) Loan (LNO, type, Amount_sanctioned, balance,bank_code,CID) Online_deposits(Refno. Type, startdate, period, CID, Int_percen,bank_code) Transactions_withinbank(TID, type, ID, amount, date_time, status) I. Create all the tables specified above. Make underlined columns as primary key. (use number, number(m,n), varchar(n), date, time, timestamp datatypes appropriately) Insert atleast 5 rows to each table. Use Interactive insertion for inserting rows to the table. [code in Bank_Master should be like SBI_VLR, SBI_KPD for State Bank for India and IND_VIT, IND_CHN for Indian Bank etc. Country need not be added in address, considering bank and customer only in India. CID of customer is a unique number generated instead of using the aadhar number in other tables. Anyremarks of customer is a description of the customer like School Teacher or Farmer. Account type should be savings/ current/ or any relevant. Loan type should be vehicle/house/education/personal. Online deposits type is RD/VRD/PPF/FD and refno is a unique number. In Transactions_withinbank TID is a unique number of each transaction, and ID will be like AID,LNO,Refno meaning that it is related to that account/loan/deposits and type will be credit or debit(C/D). And status will be declined or failed or success.] 2. Write simple DDL/DML Queries to 1. Remove all the rows from Loan table permanently. 2. Change the name of the Loan table to Loan_Details. 3. List all Customer details. 4. List all Account details of a particular customer. 5. Give a list of Customers in ascending order of Aadhar Number. 6. List account details of particular bank (like SBI). 7. List the Customer names who are senior citizen. 8. List the transaction details within a range of numbers. 9. List the transaction details which is not suecess. 10. Find the transaction type whose amount exceeds 50000 and done during peak hours (10.00am to 2.00pm). 11. Find the transaction details where status is not known. 12. Find the customers whose landmark (like 'near VIT' or 'VIT') and month of birth are same