The growth of supermarkets in most populated cities are increasing and market competitions are also high. The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data. Predictive data analytics methods are easy to apply with this dataset. please find attachment of data setSHEET1 Apply the concepts covered during the week( text function( Trim, left, right, clean, find, search ) to a use case with real-world data for a business or organization.Provide a brief explanation of your data, the business or organization, and the use case. 

Provide an overview of your      data, business/organization, and use case.
Articulate what Excel functions      you have used and apply it to the dataset to demonstrate it

Invoice ID Branch City Customer type Gender Product line Unit price Quantity Tax 5% Total Date Time Payment cogs gross margin percentage gross income Rating

750-67-8428 A Yangon Member Female Health and beauty 74.69 7 26.1415 548.9715 1/5/2019 13:08 Ewallet 522.83 4.761904762 26.1415 9.1

226-31-3081 C Naypyitaw Normal Female Electronic accessories 15.28 5 3.82 80.22 3/8/2019 10:29 Cash 76.4 4.761904762 3.82 9.6

631-41-3108 A Yangon Normal Male Home and lifestyle 46.33 7 16.2155 340.5255 3/3/2019 13:23 Credit card 324.31 4.761904762 16.2155 7.4

123-19-1176 A Yangon Member Male Health and beauty 58.22 8 23.288 489.048 1/27/2019 20:33 Ewallet 465.76 4.761904762 23.288 8.4

373-73-7910 A Yangon Normal Male Sports and travel 86.31 7 30.2085 634.3785 2/8/2019 10:37 Ewallet 604.17 4.761904762 30.2085 5.3

699-14-3026 C Naypyitaw Normal Male Electronic accessories 85.39 7 29.8865 627.6165 3/25/2019 18:30 Ewallet 597.73 4.761904762 29.8865 4.1

355-53-5943 A Yangon Member Female Electronic accessories 68.84 6 20.652 433.692 2/25/2019 14:36 Ewallet 413.04 4.761904762 20.652 5.8

315-22-5665 C Naypyitaw Normal Female Home and lifestyle 73.56 10 36.78 772.38 2/24/2019 11:38 Ewallet 735.6 4.761904762 36.78 8

665-32-9167 A Yangon Member Female Health and beauty 36.26 2 3.626 76.146 1/10/2019 17:15 Credit card 72.52 4.761904762 3.626 7.2

692-92-5582 B Mandalay Member Female Food and beverages 54.84 3 8.226 172.746 2/20/2019 13:27 Credit card 164.52 4.761904762 8.226 5.9

351-62-0822 B Mandalay Member Female Fashion accessories 14.48 4 2.896 60.816 2/6/2019 18:07 Ewallet 57.92 4.761904762 2.896 4.5

529-56-3974 B Mandalay Member Male Electronic accessories 25.51 4 5.102 107.142 3/9/2019 17:03 Cash 102.04 4.761904762 5.102 6.8

365-64-0515 A Yangon Normal Female Electronic accessories 46.95 5 11.7375 246.4875 2/12/2019 10:25 Ewallet 234.75 4.761904762 11.7375 7.1

252-56-2699 A Yangon Normal Male Food and beverages 43.19 10 21.595 453.495 2/7/2019 16:48 Ewallet 431.9 4.761904762 21.595 8.2

829-34-3910 A Yangon Normal Female Health and beauty 71.38 10 35.69 749.49 3/29/2019 19:21 Cash 713.8 4.761904762 35.69 5.7

299-46-1805 B Mandalay Member Female Sports and travel 93.72 6 28.116 590.436 1/15/2019 16:19 Cash 562.32 4.761904762 28.116 4.5

656-95-9349 A Yangon Member Female Health and beauty 68.93 7 24.1255 506.6355 3/11/2019 11:03 Credit card 482.51 4.761904762 24.1255 4.6

765-26-6951 A Yangon Normal Male Sports and travel 72.61 6 21.783 457.443 1/1/2019 10:39 Credit card 435.66 4.761904762 21.783 6.9

329-62-1586 A Yangon Normal Male Food and beverages 54.67 3 8.2005 172.2105 1/21/2019 18:00 Credit card 164.01 4.761904762 8.2005 8.6

319-50-3348 B Mandalay Normal Female Home and lifestyle 40.3 2 4.03 84.63 3/11/2019 15:30 Ewallet 80.6 4.761904762 4.03 4.4

300-71-4605 C Naypyitaw Member Male Electronic accessories 86.04 5 21.51 451.71 2/25/2019 11:24 Ewallet 430.2 4.761904762 21.51 4.8

371-85-5789 B Mandalay Normal Male Health




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