1. Refer to the tables below and write a SQL statement to prepare a list with salesman name, customer name and their cities for the salesmen and customer who belongs to the same city. Table: Salesman salesman_id 5001 5002 5005 5006 5007 5003 Table: Customer | name | city | commission James Hoog | New York Nail knite Paris Pit Alex London Mc Lyon Paris Paul Adam Rome Lauson Hen | San Jose customer_id | cust_name 3002 | Nick Rimando 3007 Brad Davis 3005 Graham Zusi 3008 Julian Green Fabian Johnson 3004 3009 Geoff Cameron 3003 Jozy Altidor city New York New York California London Paris Berlin Moscow 0.15 0.13 0.11 0.14 0.13 0.12 | grade salesman_id 100 200 200 300 300 100 200 5001 5001 5002 5002 5006 5003 5007 2. Refer to the tables below and write a SQL statement to make a list with order no, purchase amount, customer name and their cities for those orders which order amount between 500 and 2000 ord_no 70001 70009 70002 70004 70007 70005 70008 Table: customer 3002 3007 3005 3008 3004 3009 3003 purch_amt ord_date customer_id salesman_id 2012-10-05 3005 2012-09-10 3001 2012-10-05 3002 2012-08-17 3009 2012-09-10 3005 2012-07-27 3007 2012-09-10 3002 customer_id | cust_name Nick Rimando Brad Davis 150.5 270.65 65.26 110.5 948.5 2400.6 5760 +234567890 10 3. Given the following car theft data set: Example No. Color Type Origin 1 Red Sports Domestic Red Sports Domestic Red Sports Domestic Yellow Sports Yellow Sports Yellow SUV Yellow SUV Yellow SUV Red Red Sports SUV Graham Zusi Julian Green Fabian Johnson | Paris Geoff Cameron. | Berlin Jozy Altidor I Moscow 1 city | grade salesman_id | New York New York | California London Domestic Imported Imported Imported Domestic No Imported Imported Stolen? Yes No Yes No Yes No Yes 5002 5005 5001 5003 5002 No Yes 5001 5001 100 200 200 300 300 100 | 200 I Classify the following data: {Color: Red, Type: SUV, Origin: Domestic} using Nave Bayes 5001 5001 5002 5002 5006 5003 5007