Hello guys, i need to create a java OOP program and i would really appreciate if you guys could help me out

This program needs do to some operations as SUM, COUNT, AVG, MIN and MAX

Here are some input and output examples

Generic ex:

Input:

F // File name

Column1Name,Column2Name

SUM //Operation

Column3Name // Column where are the values we want to do the operation

Here are some examples of input and output for this CSV file :

Ex.1:

Input:

Customer_Data.csv

Geography,Gender

SUM

Tenure

Output: {[Germany, Male]=6646, [Germany, Female]=5924, [France, Male]=13901, [France, Female]=11192, [Spain, Male]=7020, [Spain, Female]=5445.}

Basiclly we need to sum all the values of tenure where the gender is female and from france, all the values of tenure where the gender is male and from france, all the values of tenure where the gender is male and from germany, all the values where the gender is female and from german and soo on

Note that the order of columns in the output does not matter.

Ex. 2 Input:

Customer_Data.csv

Geography,Gender

AVG

Age

Output:

{[Germany, Male]=39.42477203647417, [Germany, Female]=40.154233025984915 [France, Male]=38.2964039229931, [France, Female]=38.77399380804953, [Spain, Male]=38.64913544668588, [Spain, Female]=39.19926538108356}

Ex. 3:

Input:

Customer_Data.csv

Geography,Exited

SUM

CreditScore

Output: {[Germany, 1]=527219, [Germany, 0]=1107278, [France, 1]=519951, [France, 0]=2737486, [Spain, 0]=1345943, [Spain, 1]=267411}

I will leave here the first 26 rows of the csv, the csv has 10 000 rows and thats why the output may look different

RowNumber,CustomerId,Surname,CreditScore,Geography,Gender,Age,Tenure,Balance,NumOfProducts,HasCrCard,IsActiveMember,EstimatedSalary,Exited

1,15634602,Hargrave,619,France,Female,42,2,0,1,1,1,101348.88,1

2,15647311,Hill,608,Spain,Female,41,1,83807.86,1,0,1,112542.58,0

3,15619304,Onio,502,France,Female,42,8,159660.8,3,1,0,113931.57,1

4,15701354,Boni,699,France,Female,39,1,0,2,0,0,93826.63,0

5,15737888,Mitchell,850,Spain,Female,43,2,125510.82,1,1,1,79084.1,0

6,15574012,Chu,645,Spain,Male,44,8,113755.78,2,1,0,149756.71,1

7,15592531,Bartlett,822,France,Male,50,7,0,2,1,1,10062.8,0

8,15656148,Obinna,376,Germany,Female,29,4,115046.74,4,1,0,119346.88,1

9,15792365,He,501,France,Male,44,4,142051.07,2,0,1,74940.5,0

10,15592389,H?,684,France,Male,27,2,134603.88,1,1,1,71725.73,0

11,15767821,Bearce,528,France,Male,31,6,102016.72,2,0,0,80181.12,0

12,15737173,Andrews,497,Spain,Male,24,3,0,2,1,0,76390.01,0

13,15632264,Kay,476,France,Female,34,10,0,2,1,0,26260.98,0

14,15691483,Chin,549,France,Female,25,5,0,2,0,0,190857.79,0

15,15600882,Scott,635,Spain,Female,35,7,0,2,1,1,65951.65,0

16,15643966,Goforth,616,Germany,Male,45,3,143129.41,2,0,1,64327.26,0

17,15737452,Romeo,653,Germany,Male,58,1,132602.88,1,1,0,5097.67,1

18,15788218,Henderson,549,Spain,Female,24,9,0,2,1,1,14406.41,0

19,15661507,Muldrow,587,Spain,Male,45,6,0,1,0,0,158684.81,0

20,15568982,Hao,726,France,Female,24,6,0,2,1,1,54724.03,0

21,15577657,McDonald,732,France,Male,41,8,0,2,1,1,170886.17,0

22,15597945,Dellucci,636,Spain,Female,32,8,0,2,1,0,138555.46,0

23,15699309,Gerasimov,510,Spain,Female,38,4,0,1,1,0,118913.53,1

24,15725737,Mosman,669,France,Male,46,3,0,2,0,1,8487.75,0

25,15625047,Yen,846,France,Female,38,5,0,1,1,1,187616.16,0

26,15738191,Maclean,577,France,Male,25,3,0,2,0,1,124508.29,0