Here is a sample data set that captures the credit card transaction details for a few users. Transaction Transaction Units IP Address User ID Account Age Shipping Address Transaction Date Product Category Number Time Value Purchased 3.56.123.0 johnp 25671147 32 1542, Orchid Lane, WA 98706, US 15-5-20 15:00:05 $121.58 Clothing 1 3.56.123.0 johnp 25671147 32 1542, Orchid Lane, WA 98706, US 10-6-20 10.23:10 $79.23 Electronics 2 3.56.123.0 Johnp 25671147 32 1542, Orchid Lane, WA 98706, US 1-6-20 07:12:45 Home Decor 1 1.186.52.7 johnp 25671147 In-store 3-6-20 01:11:10 $2,009.99 Electronics 10 johnp 25671147 32 In-store 2020-06-03 01:15:12 $4,131.00 Electronics 15 1 186.52.7 johnp 25671147 32 PJO. Box 1049 05-06-2020 01:22:24 $5,010.50 Tools 20 1 58.167.2 davide 51422789 47 90 Robinson Blyd, Alberta, 97602, Canada 15 May 2020 17:02:08 $234.20 Furniture 1 1.58.167.2 davide 51422789 47 90 Robinson Blyd, Alberta, 97602, Canada 18 May 2020 19:12:45 $141.00 Kithcen Supplies 3 davide 51422789 47 90 Robinson Blyd, Alberta, 97602, Canada 01 June 2020 17:34:15 $157.25 Car Spares 1 58.167.2 davidg 51422789 47 90 Robinson Blvd, Alberta, 97602, Canada 15 June 2020 18:02:10 $59.99 Kithcen Supplies H 172.165.10.1 ellend 11568528 P.O. Box 1322 07 June 2020 15:53:12 $99.99 Clothing 172.165.10.1 ellend 11568528 P.O. Box 1322 08 June 2020 17:15:30 $53.15 Minead 1 167.255.10 ellend 11568528 P O. Box 5401 02 July 2020 00:05:10 $4,895,00 Laptopsample data set snarea earlier on in the case study. Transaction Value per Transaction 6000 5000 4000 Transaction Value in USD 3000 2000 1000 0 Trn#1 Trn#2 Trn#3 Trn#4 Trn#5 -johnp davidg -ellendIn the next section you will be asked to answer the following 5 (five) questions based on this case study: 1. List at least 5 (five) data points that are required for the analysis and detection of a credit card fraud. (3 marks) 2. ldentify 3 (three) errors/issues that could impact the accuracy of your findings, based on a data table provided. (3 marks) 3. Identify 2 (two) anomalies, or unexpected behaviors, that would lead you to believe the transaction may be suspect, based on a data table provided. (2 marks) 4. Briefly explain your key take-away from the provided data visualization chart. (1 mark) 5. Identify the type of analysis that you are performing when you are analyzing historical credit card data to understand what a fraudulent transaction looks like. [Hint: The four types of Analytics include: Descriptive, Diagnostic, Predictive, Prescriptive] (1 mark)