R

# tuliskan koding R kalian disini
x= Data$Price
y = ifelse((x>12000),print('High'), ifelse((x>=10000 & x<=12000), print('Medium'), print('Low')))

## [1] "High"
## [1] "Medium"
## [1] "Low"

Data$Classes = y
datatable(Data)

R

k = Data$Price
l = ifelse((k<8000), 5/100*Data$Price, ifelse((k>=8000 & k<9000),6/100*Data$Price, ifelse((k>=9000 & k<10000), 7/100*Data$Price ,ifelse((k>= 10000 & k<11000), 8/100*Data$Price, ifelse ((k>=11000 & k<13000), 9/100*Data$Price,1/10*Data$Price))) ))

Data$BookingFee = l
Data

R

library(DT)

z=for(x  in "Irene" ){print(subset(Data,subset=(Marketing_Name== x)))}

##        Id Marketing_Name Work_Exp      City   Cluster Price Date_Sales
## 4       4          Irene      3.6 Tengerang   Permata  7323 2018-11-03
## 54     54          Irene      3.6    Bekasi     Asoka  7158 2018-12-27
## 104   104          Irene      3.6     Bogor Teradamai  7538 2020-03-07
## 154   154          Irene      3.6    Bekasi     Adara  7268 2018-06-13
## 204   204          Irene      3.6   Jakarta     Asera 14417 2018-07-01
## 254   254          Irene      3.6     Depok  Alamanda 13613 2018-08-01
## 304   304          Irene      3.6     Bogor    Lavesh 10605 2018-11-15
## 354   354          Irene      3.6   Jakarta    Narada 10786 2020-04-27
## 404   404          Irene      3.6 Tengerang   Alindra 11820 2018-02-08
## 454   454          Irene      3.6     Depok     Asoka  9672 2019-02-18
## 504   504          Irene      3.6     Depok  Alamanda 10247 2020-04-08
## 554   554          Irene      3.6     Bogor     Arana 14623 2020-07-05
## 604   604          Irene      3.6    Bekasi    Winona  9467 2018-06-22
## 654   654          Irene      3.6     Depok   Permata  9016 2019-07-09
## 704   704          Irene      3.6 Tengerang   Albasia  8105 2019-01-14
## 754   754          Irene      3.6   Jakarta   Peronia  8964 2018-05-20
## 804   804          Irene      3.6   Jakarta     Arana 13280 2018-07-03
## 854   854          Irene      3.6   Jakarta Primadona 13644 2019-03-06
## 904   904          Irene      3.6   Jakarta   Permata 11010 2018-04-06
## 954   954          Irene      3.6     Bogor Primadona 10587 2019-04-15
## 1004 1004          Irene      3.6   Jakarta    Narada 12511 2020-09-06
## 1054 1054          Irene      3.6    Bekasi   Mutiara  8049 2019-03-08
## 1104 1104          Irene      3.6     Depok    Narada 13829 2020-06-25
## 1154 1154          Irene      3.6     Bogor Sweethome 11032 2020-04-11
## 1204 1204          Irene      3.6   Jakarta     Tiara 13707 2019-11-02
## 1254 1254          Irene      3.6   Jakarta   Albasia 11023 2020-02-18
## 1304 1304          Irene      3.6     Depok    Narada 10159 2020-09-05
## 1354 1354          Irene      3.6 Tengerang      Neon 11180 2019-09-24
## 1404 1404          Irene      3.6 Tengerang     Asoka  7307 2020-09-03
## 1454 1454          Irene      3.6 Tengerang  Alamanda 13435 2018-04-12
## 1504 1504          Irene      3.6     Bogor     Arana 11532 2019-04-18
## 1554 1554          Irene      3.6     Depok    Winona 14982 2018-10-27
## 1604 1604          Irene      3.6    Bekasi     Asoka 11617 2019-05-15
## 1654 1654          Irene      3.6     Bogor Teradamai 10828 2020-03-26
## 1704 1704          Irene      3.6     Depok  Alamanda  8138 2020-01-07
## 1754 1754          Irene      3.6     Depok Sweethome  9632 2019-10-02
## 1804 1804          Irene      3.6     Bogor     Adara 13244 2020-05-22
## 1854 1854          Irene      3.6     Depok   Alindra 12778 2018-07-19
## 1904 1904          Irene      3.6    Bekasi Teradamai  7847 2018-06-07
## 1954 1954          Irene      3.6 Tengerang Teradamai  9282 2020-06-28
## 2004 2004          Irene      3.6   Jakarta Sweethome  9264 2019-11-08
## 2054 2054          Irene      3.6   Jakarta    Lavesh 12223 2018-10-24
## 2104 2104          Irene      3.6    Bekasi    Lavesh 13444 2018-03-27
## 2154 2154          Irene      3.6    Bekasi    Winona 10138 2020-08-01
## 2204 2204          Irene      3.6   Jakarta   Albasia  7012 2018-09-03
## 2254 2254          Irene      3.6    Bekasi   Mutiara  8021 2018-08-02
## 2304 2304          Irene      3.6 Tengerang Primadona  7580 2019-04-25
## 2354 2354          Irene      3.6     Depok   Palmyra 10905 2019-12-13
## 2404 2404          Irene      3.6 Tengerang     Asoka 14141 2019-08-21
## 2454 2454          Irene      3.6   Jakarta     Adara 10168 2020-06-08
## 2504 2504          Irene      3.6   Jakarta     Asoka 10303 2020-07-07
## 2554 2554          Irene      3.6     Bogor Sweethome 12546 2018-12-03
## 2604 2604          Irene      3.6     Depok     Asera  9602 2018-04-22
## 2654 2654          Irene      3.6     Bogor     Arana  9728 2018-08-17
## 2704 2704          Irene      3.6     Depok Primadona  9521 2018-08-19
## 2754 2754          Irene      3.6     Bogor   Mutiara 14280 2020-04-26
## 2804 2804          Irene      3.6   Jakarta    Winona 12260 2019-09-03
## 2854 2854          Irene      3.6     Depok   Peronia  7044 2020-03-14
## 2904 2904          Irene      3.6 Tengerang   Alindra 12053 2020-02-21
## 2954 2954          Irene      3.6     Depok     Adara 11571 2018-01-21
## 3004 3004          Irene      3.6     Bogor  Alamanda 11444 2019-10-26
## 3054 3054          Irene      3.6 Tengerang Teradamai 13860 2019-11-05
## 3104 3104          Irene      3.6   Jakarta     Arana  8472 2020-07-26
## 3154 3154          Irene      3.6   Jakarta     Asoka 13919 2019-05-26
## 3204 3204          Irene      3.6    Bekasi   Palmyra 14174 2020-05-27
## 3254 3254          Irene      3.6    Bekasi Teradamai 10083 2020-01-27
## 3304 3304          Irene      3.6     Depok     Asera 13998 2018-02-07
## 3354 3354          Irene      3.6   Jakarta   Palmyra  9622 2018-10-18
## 3404 3404          Irene      3.6     Depok   Permata  8133 2018-04-14
## 3454 3454          Irene      3.6    Bekasi Primadona 13569 2018-09-23
## 3504 3504          Irene      3.6   Jakarta  Alamanda 10723 2018-03-16
## 3554 3554          Irene      3.6     Bogor     Adara  8542 2018-08-03
## 3604 3604          Irene      3.6     Depok  Victoria 12879 2019-03-30
## 3654 3654          Irene      3.6     Bogor Primadona 14179 2020-08-17
## 3704 3704          Irene      3.6    Bekasi  Alamanda 10191 2019-01-10
## 3754 3754          Irene      3.6 Tengerang Teradamai  7365 2019-08-25
## 3804 3804          Irene      3.6    Bekasi   Albasia 11796 2018-07-04
## 3854 3854          Irene      3.6   Jakarta     Asera 14666 2019-06-19
## 3904 3904          Irene      3.6 Tengerang Primadona 11587 2020-09-01
## 3954 3954          Irene      3.6     Depok   Mutiara 12995 2018-04-24
## 4004 4004          Irene      3.6     Bogor     Tiara  8421 2020-09-24
## 4054 4054          Irene      3.6    Bekasi  Victoria 14494 2018-03-03
## 4104 4104          Irene      3.6 Tengerang Primadona 12004 2018-10-31
## 4154 4154          Irene      3.6     Depok Teradamai  7799 2018-11-01
## 4204 4204          Irene      3.6 Tengerang     Asera 11524 2018-02-19
## 4254 4254          Irene      3.6 Tengerang     Asoka 11976 2019-07-10
## 4304 4304          Irene      3.6 Tengerang     Adara 12811 2020-08-16
## 4354 4354          Irene      3.6     Depok   Mutiara 11728 2018-06-21
## 4404 4404          Irene      3.6     Bogor Primadona  7646 2019-08-30
## 4454 4454          Irene      3.6     Depok     Arana 14657 2018-10-16
## 4504 4504          Irene      3.6   Jakarta   Mutiara 13785 2018-07-21
## 4554 4554          Irene      3.6   Jakarta  Victoria 13686 2019-08-17
## 4604 4604          Irene      3.6    Bekasi    Lavesh 11347 2020-06-30
## 4654 4654          Irene      3.6   Jakarta     Asoka 12531 2020-08-20
## 4704 4704          Irene      3.6 Tengerang    Narada  8384 2019-03-09
## 4754 4754          Irene      3.6     Depok    Narada  9098 2018-10-24
## 4804 4804          Irene      3.6 Tengerang  Alamanda  9029 2020-02-05
## 4854 4854          Irene      3.6   Jakarta Sweethome  9109 2019-04-22
## 4904 4904          Irene      3.6     Depok     Arana 11097 2019-01-27
## 4954 4954          Irene      3.6     Bogor   Mutiara  8455 2020-09-12
## 5004 5004          Irene      3.6     Depok Primadona  8175 2018-02-28
## 5054 5054          Irene      3.6 Tengerang      Neon 12526 2019-06-25
## 5104 5104          Irene      3.6     Bogor   Alindra  8050 2018-05-29
## 5154 5154          Irene      3.6     Depok    Lavesh 10702 2019-04-02
## 5204 5204          Irene      3.6     Depok   Albasia 11470 2020-04-24
## 5254 5254          Irene      3.6    Bekasi   Peronia 11261 2018-10-25
## 5304 5304          Irene      3.6   Jakarta Teradamai  7829 2019-08-13
## 5354 5354          Irene      3.6    Bekasi    Lavesh  7362 2018-12-15
## 5404 5404          Irene      3.6   Jakarta Primadona  8841 2019-11-17
## 5454 5454          Irene      3.6     Depok     Asoka  9609 2019-03-25
## 5504 5504          Irene      3.6     Bogor   Palmyra 11582 2020-03-03
## 5554 5554          Irene      3.6    Bekasi   Albasia  7183 2019-12-16
## 5604 5604          Irene      3.6     Bogor    Winona 13655 2020-01-24
## 5654 5654          Irene      3.6     Depok   Permata  7885 2019-04-02
## 5704 5704          Irene      3.6     Bogor   Peronia  9197 2020-06-03
## 5754 5754          Irene      3.6     Depok   Palmyra 14645 2019-07-08
## 5804 5804          Irene      3.6     Bogor  Alamanda 13653 2019-01-26
## 5854 5854          Irene      3.6     Bogor    Lavesh  7232 2018-02-27
## 5904 5904          Irene      3.6 Tengerang Primadona  8533 2019-02-28
## 5954 5954          Irene      3.6     Bogor Primadona 10785 2018-05-20
## 6004 6004          Irene      3.6 Tengerang     Asera 10630 2018-04-14
## 6054 6054          Irene      3.6 Tengerang Sweethome  8251 2019-05-17
## 6104 6104          Irene      3.6     Bogor    Narada 10997 2018-12-30
## 6154 6154          Irene      3.6 Tengerang   Albasia  7952 2020-06-17
## 6204 6204          Irene      3.6     Bogor     Arana  9750 2019-08-07
## 6254 6254          Irene      3.6     Depok  Alamanda 11580 2018-12-15
## 6304 6304          Irene      3.6     Depok   Albasia 12963 2020-02-13
## 6354 6354          Irene      3.6     Depok   Albasia  7889 2019-01-20
## 6404 6404          Irene      3.6   Jakarta      Neon 12499 2019-10-16
## 6454 6454          Irene      3.6    Bekasi   Mutiara  7446 2020-08-13
## 6504 6504          Irene      3.6 Tengerang     Adara  8824 2018-05-29
## 6554 6554          Irene      3.6   Jakarta    Lavesh  9558 2018-09-17
## 6604 6604          Irene      3.6 Tengerang     Asera  9583 2020-02-07
## 6654 6654          Irene      3.6     Depok   Palmyra 10336 2019-05-03
## 6704 6704          Irene      3.6   Jakarta     Arana  7869 2020-06-04
## 6754 6754          Irene      3.6     Bogor Sweethome  9979 2019-04-07
## 6804 6804          Irene      3.6 Tengerang   Permata  7879 2018-01-03
## 6854 6854          Irene      3.6 Tengerang     Tiara 10737 2019-11-28
## 6904 6904          Irene      3.6 Tengerang   Albasia  8943 2020-04-07
## 6954 6954          Irene      3.6    Bekasi  Victoria  7056 2018-03-11
## 7004 7004          Irene      3.6    Bekasi   Alindra  8801 2020-04-27
## 7054 7054          Irene      3.6     Bogor    Lavesh 14966 2020-01-22
## 7104 7104          Irene      3.6   Jakarta     Tiara 11991 2019-08-10
## 7154 7154          Irene      3.6     Bogor Sweethome 13470 2020-01-25
## 7204 7204          Irene      3.6 Tengerang     Asera  7585 2020-09-21
## 7254 7254          Irene      3.6     Depok   Palmyra  8530 2018-07-18
## 7304 7304          Irene      3.6 Tengerang     Arana  7490 2020-08-18
## 7354 7354          Irene      3.6   Jakarta    Narada 11842 2020-03-03
## 7404 7404          Irene      3.6     Depok Teradamai 11666 2019-11-13
## 7454 7454          Irene      3.6    Bekasi    Winona  7759 2020-06-13
## 7504 7504          Irene      3.6    Bekasi  Alamanda 14370 2019-02-14
## 7554 7554          Irene      3.6     Depok   Peronia 12640 2018-03-23
## 7604 7604          Irene      3.6     Depok  Alamanda 10464 2019-05-26
## 7654 7654          Irene      3.6     Bogor  Alamanda  9828 2020-07-20
## 7704 7704          Irene      3.6 Tengerang     Adara  8151 2018-04-22
## 7754 7754          Irene      3.6     Depok   Alindra 12789 2020-09-21
## 7804 7804          Irene      3.6     Depok      Neon 12656 2019-07-21
## 7854 7854          Irene      3.6     Bogor   Permata 13199 2018-10-03
## 7904 7904          Irene      3.6    Bekasi Teradamai  9554 2019-10-22
## 7954 7954          Irene      3.6     Depok      Neon 10942 2020-06-21
## 8004 8004          Irene      3.6   Jakarta  Victoria 12550 2020-08-28
## 8054 8054          Irene      3.6    Bekasi   Alindra  7408 2019-12-06
## 8104 8104          Irene      3.6 Tengerang     Tiara 11336 2018-03-01
## 8154 8154          Irene      3.6     Bogor  Victoria  8825 2018-01-11
## 8204 8204          Irene      3.6   Jakarta    Lavesh 13554 2019-12-27
## 8254 8254          Irene      3.6   Jakarta Teradamai  8080 2018-03-22
## 8304 8304          Irene      3.6     Bogor   Permata  9420 2018-08-04
## 8354 8354          Irene      3.6     Bogor Primadona 12265 2018-05-20
## 8404 8404          Irene      3.6     Depok     Tiara 13549 2019-03-31
## 8454 8454          Irene      3.6    Bekasi   Mutiara 10903 2019-06-03
## 8504 8504          Irene      3.6     Bogor    Winona 14837 2018-12-01
## 8554 8554          Irene      3.6   Jakarta Sweethome  8101 2020-09-10
## 8604 8604          Irene      3.6 Tengerang     Asera  9620 2019-11-19
## 8654 8654          Irene      3.6     Depok Sweethome 12536 2018-08-10
## 8704 8704          Irene      3.6     Depok    Lavesh 12569 2018-06-23
## 8754 8754          Irene      3.6     Depok    Winona 12452 2018-09-19
## 8804 8804          Irene      3.6   Jakarta Teradamai 14589 2018-12-25
## 8854 8854          Irene      3.6     Depok    Lavesh 11861 2019-03-16
## 8904 8904          Irene      3.6    Bekasi   Albasia 12401 2018-09-12
## 8954 8954          Irene      3.6     Depok     Arana 13500 2019-07-22
## 9004 9004          Irene      3.6 Tengerang Primadona  9487 2019-12-07
## 9054 9054          Irene      3.6    Bekasi    Lavesh 14905 2020-06-08
## 9104 9104          Irene      3.6    Bekasi   Permata  7309 2020-03-11
## 9154 9154          Irene      3.6     Bogor Teradamai 12805 2018-03-01
## 9204 9204          Irene      3.6     Bogor     Asera 12483 2018-05-12
## 9254 9254          Irene      3.6     Depok   Peronia 13585 2018-07-29
## 9304 9304          Irene      3.6   Jakarta  Alamanda 12653 2018-05-06
## 9354 9354          Irene      3.6     Bogor   Permata  9742 2019-10-17
## 9404 9404          Irene      3.6 Tengerang      Neon 10927 2020-09-07
## 9454 9454          Irene      3.6     Bogor   Mutiara 13325 2020-02-25
## 9504 9504          Irene      3.6     Bogor  Victoria 14994 2018-07-25
## 9554 9554          Irene      3.6    Bekasi Teradamai 10562 2019-04-04
## 9604 9604          Irene      3.6   Jakarta     Asoka 13954 2020-04-18
## 9654 9654          Irene      3.6 Tengerang     Arana  7756 2019-02-03
## 9704 9704          Irene      3.6    Bekasi     Asera  9858 2019-05-03
## 9754 9754          Irene      3.6     Bogor    Narada  9274 2018-11-30
## 9804 9804          Irene      3.6    Bekasi      Neon 13211 2018-11-02
## 9854 9854          Irene      3.6     Depok  Alamanda  9577 2020-04-27
## 9904 9904          Irene      3.6    Bekasi     Arana 12328 2019-04-17
## 9954 9954          Irene      3.6     Depok    Winona 13207 2019-11-29
##      Advertisement Classes BookingFee
## 4                5     Low     366.15
## 54              14     Low     357.90
## 104             15     Low     376.90
## 154             13     Low     363.40
## 204             18    High    1441.70
## 254              3    High    1361.30
## 304              1  Medium     848.40
## 354             13  Medium     862.88
## 404             18  Medium    1063.80
## 454              2     Low     677.04
## 504             15  Medium     819.76
## 554             20    High    1462.30
## 604             13     Low     662.69
## 654             14     Low     631.12
## 704              1     Low     486.30
## 754             10     Low     537.84
## 804              4    High    1328.00
## 854             13    High    1364.40
## 904              9  Medium     990.90
## 954              9  Medium     846.96
## 1004            19    High    1125.99
## 1054            19     Low     482.94
## 1104            13    High    1382.90
## 1154            19  Medium     992.88
## 1204            15    High    1370.70
## 1254            11  Medium     992.07
## 1304             3  Medium     812.72
## 1354             9  Medium    1006.20
## 1404            14     Low     365.35
## 1454            15    High    1343.50
## 1504             2  Medium    1037.88
## 1554            16    High    1498.20
## 1604            12  Medium    1045.53
## 1654            12  Medium     866.24
## 1704            11     Low     488.28
## 1754            15     Low     674.24
## 1804             3    High    1324.40
## 1854             7    High    1150.02
## 1904            10     Low     392.35
## 1954            16     Low     649.74
## 2004            19     Low     648.48
## 2054            16    High    1100.07
## 2104             6    High    1344.40
## 2154             4  Medium     811.04
## 2204             6     Low     350.60
## 2254            15     Low     481.26
## 2304             6     Low     379.00
## 2354            18  Medium     872.40
## 2404            12    High    1414.10
## 2454            14  Medium     813.44
## 2504             5  Medium     824.24
## 2554             4    High    1129.14
## 2604            14     Low     672.14
## 2654            12     Low     680.96
## 2704             8     Low     666.47
## 2754            19    High    1428.00
## 2804            16    High    1103.40
## 2854            19     Low     352.20
## 2904             9    High    1084.77
## 2954             2  Medium    1041.39
## 3004            18  Medium    1029.96
## 3054             9    High    1386.00
## 3104            18     Low     508.32
## 3154            13    High    1391.90
## 3204            11    High    1417.40
## 3254            17  Medium     806.64
## 3304            11    High    1399.80
## 3354            14     Low     673.54
## 3404            17     Low     487.98
## 3454             4    High    1356.90
## 3504            20  Medium     857.84
## 3554             3     Low     512.52
## 3604            19    High    1159.11
## 3654             7    High    1417.90
## 3704             9  Medium     815.28
## 3754             3     Low     368.25
## 3804             9  Medium    1061.64
## 3854            13    High    1466.60
## 3904            13  Medium    1042.83
## 3954             3    High    1169.55
## 4004            15     Low     505.26
## 4054            15    High    1449.40
## 4104            13    High    1080.36
## 4154            10     Low     389.95
## 4204             9  Medium    1037.16
## 4254            15  Medium    1077.84
## 4304            14    High    1152.99
## 4354             8  Medium    1055.52
## 4404            17     Low     382.30
## 4454             9    High    1465.70
## 4504            13    High    1378.50
## 4554            17    High    1368.60
## 4604            10  Medium    1021.23
## 4654            15    High    1127.79
## 4704             6     Low     503.04
## 4754            18     Low     636.86
## 4804             4     Low     632.03
## 4854             6     Low     637.63
## 4904            20  Medium     998.73
## 4954            13     Low     507.30
## 5004             8     Low     490.50
## 5054             7    High    1127.34
## 5104            18     Low     483.00
## 5154            12  Medium     856.16
## 5204            14  Medium    1032.30
## 5254             8  Medium    1013.49
## 5304             6     Low     391.45
## 5354             7     Low     368.10
## 5404             8     Low     530.46
## 5454             9     Low     672.63
## 5504            18  Medium    1042.38
## 5554             8     Low     359.15
## 5604            14    High    1365.50
## 5654             7     Low     394.25
## 5704             6     Low     643.79
## 5754            18    High    1464.50
## 5804            20    High    1365.30
## 5854            18     Low     361.60
## 5904            20     Low     511.98
## 5954            18  Medium     862.80
## 6004             7  Medium     850.40
## 6054            13     Low     495.06
## 6104             1  Medium     879.76
## 6154             9     Low     397.60
## 6204             7     Low     682.50
## 6254            19  Medium    1042.20
## 6304            16    High    1166.67
## 6354             4     Low     394.45
## 6404             3    High    1124.91
## 6454             9     Low     372.30
## 6504             9     Low     529.44
## 6554            10     Low     669.06
## 6604            20     Low     670.81
## 6654            13  Medium     826.88
## 6704             2     Low     393.45
## 6754             4     Low     698.53
## 6804            14     Low     393.95
## 6854             8  Medium     858.96
## 6904             3     Low     536.58
## 6954             1     Low     352.80
## 7004            12     Low     528.06
## 7054            16    High    1496.60
## 7104            18  Medium    1079.19
## 7154             3    High    1347.00
## 7204            19     Low     379.25
## 7254             2     Low     511.80
## 7304             7     Low     374.50
## 7354             8  Medium    1065.78
## 7404             6  Medium    1049.94
## 7454             9     Low     387.95
## 7504             6    High    1437.00
## 7554            16    High    1137.60
## 7604             8  Medium     837.12
## 7654             2     Low     687.96
## 7704            12     Low     489.06
## 7754             9    High    1151.01
## 7804            10    High    1139.04
## 7854            15    High    1319.90
## 7904            13     Low     668.78
## 7954            12  Medium     875.36
## 8004             9    High    1129.50
## 8054            14     Low     370.40
## 8104             8  Medium    1020.24
## 8154             6     Low     529.50
## 8204             2    High    1355.40
## 8254            14     Low     484.80
## 8304            13     Low     659.40
## 8354            18    High    1103.85
## 8404            10    High    1354.90
## 8454            17  Medium     872.24
## 8504            11    High    1483.70
## 8554            10     Low     486.06
## 8604             5     Low     673.40
## 8654             4    High    1128.24
## 8704            14    High    1131.21
## 8754            16    High    1120.68
## 8804             9    High    1458.90
## 8854             5  Medium    1067.49
## 8904            13    High    1116.09
## 8954             5    High    1350.00
## 9004             7     Low     664.09
## 9054            15    High    1490.50
## 9104            17     Low     365.45
## 9154             9    High    1152.45
## 9204            12    High    1123.47
## 9254            15    High    1358.50
## 9304             5    High    1138.77
## 9354            20     Low     681.94
## 9404             1  Medium     874.16
## 9454            15    High    1332.50
## 9504             5    High    1499.40
## 9554            11  Medium     844.96
## 9604            15    High    1395.40
## 9654            14     Low     387.80
## 9704            12     Low     690.06
## 9754            11     Low     649.18
## 9804             8    High    1321.10
## 9854             2     Low     670.39
## 9904            15    High    1109.52
## 9954            15    High    1320.70

R

DataIrene = subset(Data, subset=(Marketing_Name == "Irene"))


b = ifelse((DataIrene$Work_Exp>3), (DataIrene$BookingFee*0.03),(DataIrene$BookingFee*0.02))

DataIrene$Bonus = b

DataIrene

Totalbonus=sum(DataIrene$Bonus)
Totalbonus

## [1] 5351.693

R

 Angel = subset (Data, subset=(Marketing_Name == "Angel")) 
Sherly=subset (Data, subset=(Marketing_Name == "Sherly")) 
Vanessa=subset(Data, subset=(Marketing_Name == "Vanessa"  ))
Irene=subset(Data, subset=(Marketing_Name == "Irene"))
Julian=subset (Data, subset=(Marketing_Name == "Julian"))
Jeffry =subset (Data, subset=(Marketing_Name == "Jeffry")) 
Nikita =subset (Data, subset=(Marketing_Name =="Nikita")) 
Kefas =subset(Data, subset=(Marketing_Name == "Kefas"))
Siana =subset (Data, subset=(Marketing_Name == "Siana")) 
Lala=subset (Data, subset=(Marketing_Name == "Lala")) 
Fallen =subset (Data, subset=(Marketing_Name == "Fallen")) 
Ardifo =subset (Data, subset=(Marketing_Name == "Ardifo"))
Kevin =subset (Data, subset=(Marketing_Name == "Kevin"))
Juen =subset (Data, subset=(Marketing_Name == "Juen")) 
Jerrel =subset (Data, subset=(Marketing_Name == "Jerrel"))
Imelda =subset (Data, subset=(Marketing_Name == "Imelda")) 
Widi =subset(Data, subset=(Marketing_Name == "Widi")) 
Theodor = subset (Data, subset=(Marketing_Name == "Theodor")) 
Elvani =subset (Data, subset=(Marketing_Name == "Elvani")) 
Jonathan = subset (Data, subset=(Marketing_Name == "Jonathan")) 
Sofia =subset (Data, subset=(Marketing_Name == "Sofia")) 
Abraham = subset (Data, subset=(Marketing_Name == "Abraham")) 
Siti =subset (Data, subset=(Marketing_Name == "Siti")) 
Niko =subset(Data, subset=(Marketing_Name == "Niko")) 
Sefli =subset (Data, subset=(Marketing_Name == "Selfi")) 
Bene =subset(Data, subset=(Marketing_Name == "Bene")) 
Diana =subset (Data, subset=(Marketing_Name == "Diana"))
Pupe =subset(Data, subset=(Marketing_Name == "Pupe")) 
Andi =subset (Data, subset=(Marketing_Name == "Andi")) 
Tatha =subset (Data, subset=(Marketing_Name == "Tatha"))
Endri=subset (Data, subset=(Marketing_Name == "Endri")) 
Monika= subset(Data, subset=(Marketing_Name == "Monika")) 
Hans =subset(Data, subset=(Marketing_Name == "Hans")) 
Debora= subset (Data, subset=(Marketing_Name == "Debora")) 
Hanifa= subset (Data, subset=(Marketing_Name == "Hanifa"))
James =subset (Data, subset=(Marketing_Name == "James")) 
Jihan =subset (Data, subset=(Marketing_Name == "Jihan"))
Friska =subset (Data, subset=(Marketing_Name == "Friska")) 
Ardiwan = subset (Data, subset=(Marketing_Name == "Ardiwan")) 
Bakti =subset (Data, subset=(Marketing_Name == "Bakti")) 
Anthon =subset (Data, subset=(Marketing_Name == "Anthon")) 
Amry =subset (Data, subset=(Marketing_Name == "Amry")) 
Wiwik =subset (Data, subset=(Marketing_Name == "Wiwik")) 
Bastian = subset (Data, subset=(Marketing_Name == "Bastian")) 
Budi = subset (Data, subset=(Marketing_Name == "Budi")) 
Leo = subset (Data, subset=(Marketing_Name == "Leo")) 
Simon = subset (Data, subset=(Marketing_Name == "Simon")) 
Matius = subset (Data, subset=(Marketing_Name == "Matius")) 
Arry = subset (Data, subset=(Marketing_Name == "Arry")) 
Eliando = subset (Data, subset=(Marketing_Name == "Eliando")) 
  
Nama_Sales = c("Angel","Sherly","Vanessa","Irene","Julian",
                        "Jeffry","Nikita","Kefas","Siana","Lala",
                        "Fallen","Ardifo","Kevin","Juen","Jerrel",
                        "Imelda","Widi","Theodora","Elvani","Jonathan",
                        "Sofia","Abraham","Siti","Niko","Sefli",
                        "Bene", "Diana", "Pupe", "Andi", "Tatha",
                        "Endri", "Monika", "Hans", "Debora","Hanifa",
                        "James", "Jihan", "Friska","Ardiwan", "Bakti",
                        "Anthon","Amry", "Wiwik", "Bastian", "Budi",
                        "Leo","Simon","Matius","Arry", "Eliando")
propertysold = c(sum(Angel$Price), sum(Sherly$Price), sum(Vanessa$Price), sum(Irene$Price),
sum(Julian$Price), sum (Jeffry$Price), sum(Nikita$Price), sum(Kefas$Price), sum(Siana$Price), sum(Lala$Price), sum(Fallen$Price), sum(Ardifo$Price), sum(Kevin$Price), sum (Juen$Price), sum(Jerrel$Price), sum(Imelda$Price), sum(Widi$Price), sum(Theodor$Price), sum(Elvani$Price), sum(Jonathan$Price), sum(Sofia$Price), sum(Abraham$Price), sum(Siti$Price), sum(Niko$Price), sum(Sefli$Price), sum(Bene$Price), sum( Diana$Price), sum (Pupe$Price), sum(Andi$Price), sum( Tatha$Price), sum(Endri$Price), sum( Monika$Price), sum( Hans$Price), sum( Debora$Price), sum(Hanifa$Price), sum (James$Price), sum( Jihan$Price), sum( Friska$Price), sum(Ardiwan$Price), sum(Bakti$Price), sum(Anthon$Price), sum(Amry$Price), sum( Wiwik$Price), sum( Bastian$Price), sum( Budi$Price), sum (Leo$Price), sum(Simon$Price), sum(Matius$Price), sum(Arry$Price), sum( Eliando$Price))

datamarketing= data.frame(Nama_Sales, propertysold)

datamarketing

# Best Marketing
datamarketing[which.max(datamarketing$propertysold),]

#Kota dan Cluster Paling Menguntungkan
profitable= Data[,c("City","Cluster","Price")]

profitable[which.max(profitable$Price),]

#Total Biaya Sdvertisment irene
costforad = subset(Data, subset=(Marketing_Name == "Irene"))

adscost = ( costforad$Advertisement * 4)

Totalcost=print(sum(adscost))

## [1] 8796

# Rata-Rata cost iklan
Namasales = c("Angel","Sherly","Vanessa","Irene","Julian",
                        "Jeffry","Nikita","Kefas","Siana","Lala",
                        "Fallen","Ardifo","Kevin","Juen","Jerrel",
                        "Imelda","Widi","Theodora","Elvani","Jonathan",
                        "Sofia","Abraham","Siti","Niko","Sefli",
                        "Bene", "Diana", "Pupe", "Andi", "Tatha",
                        "Endri", "Monika", "Hans", "Debora","Hanifa",
                        "James", "Jihan", "Friska","Ardiwan", "Bakti",
                        "Anthon","Amry", "Wiwik", "Bastian", "Budi",
                        "Leo","Simon","Matius","Arry", "Eliando")
ratacost = for (x in Namasales) {
  f= subset ( Data, subset=(Marketing_Name == x ))
  c=sum(f$Advertisement * 4)
  print (cat(sum(c)/length(f$Id), (cat(x, 'Rata-rata pengeluaran untuk iklan'))))}

## Angel Rata-rata pengeluaran untuk iklan39.64NULL
## Sherly Rata-rata pengeluaran untuk iklan42.2NULL
## Vanessa Rata-rata pengeluaran untuk iklan43.44NULL
## Irene Rata-rata pengeluaran untuk iklan43.98NULL
## Julian Rata-rata pengeluaran untuk iklan42.04NULL
## Jeffry Rata-rata pengeluaran untuk iklan39.68NULL
## Nikita Rata-rata pengeluaran untuk iklan43.6NULL
## Kefas Rata-rata pengeluaran untuk iklan41.28NULL
## Siana Rata-rata pengeluaran untuk iklan43.4NULL
## Lala Rata-rata pengeluaran untuk iklan44.04NULL
## Fallen Rata-rata pengeluaran untuk iklan42.8NULL
## Ardifo Rata-rata pengeluaran untuk iklan42.2NULL
## Kevin Rata-rata pengeluaran untuk iklan42.24NULL
## Juen Rata-rata pengeluaran untuk iklan40.88NULL
## Jerrel Rata-rata pengeluaran untuk iklan41.76NULL
## Imelda Rata-rata pengeluaran untuk iklan41.44NULL
## Widi Rata-rata pengeluaran untuk iklan41.78NULL
## Theodora Rata-rata pengeluaran untuk iklan41.54NULL
## Elvani Rata-rata pengeluaran untuk iklan41NULL
## Jonathan Rata-rata pengeluaran untuk iklan42.3NULL
## Sofia Rata-rata pengeluaran untuk iklan43.74NULL
## Abraham Rata-rata pengeluaran untuk iklan40.22NULL
## Siti Rata-rata pengeluaran untuk iklan39.58NULL
## Niko Rata-rata pengeluaran untuk iklan38.06NULL
## Sefli Rata-rata pengeluaran untuk iklan43.38NULL
## Bene Rata-rata pengeluaran untuk iklan43.88NULL
## Diana Rata-rata pengeluaran untuk iklan42.56NULL
## Pupe Rata-rata pengeluaran untuk iklan42.76NULL
## Andi Rata-rata pengeluaran untuk iklan40.14NULL
## Tatha Rata-rata pengeluaran untuk iklan43.56NULL
## Endri Rata-rata pengeluaran untuk iklan44.1NULL
## Monika Rata-rata pengeluaran untuk iklan43.16NULL
## Hans Rata-rata pengeluaran untuk iklan41.22NULL
## Debora Rata-rata pengeluaran untuk iklan41.08NULL
## Hanifa Rata-rata pengeluaran untuk iklan39.1NULL
## James Rata-rata pengeluaran untuk iklan38.96NULL
## Jihan Rata-rata pengeluaran untuk iklan39.02NULL
## Friska Rata-rata pengeluaran untuk iklan41.28NULL
## Ardiwan Rata-rata pengeluaran untuk iklan43.8NULL
## Bakti Rata-rata pengeluaran untuk iklan41.76NULL
## Anthon Rata-rata pengeluaran untuk iklan40.54NULL
## Amry Rata-rata pengeluaran untuk iklan41.36NULL
## Wiwik Rata-rata pengeluaran untuk iklan41.02NULL
## Bastian Rata-rata pengeluaran untuk iklan40.94NULL
## Budi Rata-rata pengeluaran untuk iklan42.04NULL
## Leo Rata-rata pengeluaran untuk iklan39.96NULL
## Simon Rata-rata pengeluaran untuk iklan43.66NULL
## Matius Rata-rata pengeluaran untuk iklan39.12NULL
## Arry Rata-rata pengeluaran untuk iklan41.52NULL
## Eliando Rata-rata pengeluaran untuk iklan43.06NULL

# Pendapatan

revenue= (sum(Data$Price)-(sum(Data$Advertisment) * 4))/((max(Data$Work_Exp))*12)

revenue

## [1] 917232.2

R

marital_status <- sample(c("Yes", "No"), 50000, replace = T) 
Address <-sample(c("Jakarta", "Bogor", "Depok", "Tangerang", "Bekasi"),50000, replace=T)
Work_Location <-sample(c("Jakarta", "Bogor", "Depok", "Tangerang", "Bekasi"),50000, replace=T)
Age <-floor (runif(50000,19,60))
Academic <-sample(c("J.School", "H.School", "Bachelor Degree", "Master",
"Phd"), 50000, replace = T) 





Grade <-sample(c("S+", "S", "A+", "A",
"B"), 50000, replace=T) 

Number_of_children <- ifelse(marital_status =="Yes", sample((c(0:10)), length(marital_status =="Yes"), replace=T), "0")
Private_vehicle <- sample(c("Car", "Motorcycle", "Public"),
50000, replace=T)
Home <- sample(c("Rent", "Own", "Credit"), 50000, replace=T) 

Datasoal2 <-data.frame (marital_status, Address, Work_Location, Age, Academic, Grade, Number_of_children, Private_vehicle, Home)

library(data.table)
data.table(Datasoal2)

Job <- ifelse (Datasoal2$Academic == "J.School", sample(c("Cleaning Service", "Security Guard", "Waiter")),
ifelse(Datasoal2$Academic == "H.School", sample(c("Waiter", "Security Guard")), ifelse(Datasoal2$Academic == "Bachelor Degree", sample(c("CEO", "Teacher")), ifelse(Datasoal2$Academic == "Master",sample(c("Board Director", "Actuarist", "Software Enginering")), sample(c("Head of Research", "Teacher"))
))))

Datasoal2 = data.frame(marital_status, Address, Work_Location, Age, Academic,Job, Grade, Number_of_children, Private_vehicle, Home)

Income <- ifelse (Datasoal2$Job == "Cleaning Service", sample(c(5000:6000)),
ifelse(Datasoal2$Job == "Clerk", sample(c(4500:5500)), ifelse(Datasoal2$Job == "Waiter", sample(c(4500:6000)), ifelse(Datasoal2$Job == "Teacher",sample(c(6000:9000)), ifelse (Datasoal2$Job == "Actuarist", sample(c(8500:13000)), ifelse(Datasoal2$ Job == "Software Enginering", sample(c(8000:12000)), ifelse (Datasoal2$Job =="Security Guard", sample(c(5000:5500)), ifelse (Datasoal2$Job== "Head of Research", sample(c(9000:14000)), ifelse(Datasoal2$Job == "CEO", sample(c(13000:20000)), sample(c(11000:16000))
)))))))))

Spending <- ifelse (Datasoal2$Job == "Cleaning Service", sample(c(3000:4500)),
ifelse(Datasoal2$Job == "Clerk", sample(c(2200:5100)), ifelse(Datasoal2$Job == "Waiter", sample(c(3000:5000)), ifelse(Datasoal2$Job == "Teacher",sample(c(4000:7500)), ifelse (Datasoal2$Job == "Actuarist", sample(c(3500:9000)), ifelse(Datasoal2$ Job == "Software Enginering", sample(c(5000:7500)), ifelse (Datasoal2$Job =="Security Guard", sample(c(4000:4500)), ifelse (Datasoal2$Job== "Head of Research", sample(c(6000:10500)), ifelse(Datasoal2$Job == "CEO", sample(c(6000:10000)), sample(c(5500:11000))
)))))))))

Datasoal2$Job=Job
Datasoal2$Income = Income
Datasoal2$Spending = Spending

Datasoal2

R

summary(Datasoal2)

##  marital_status       Address          Work_Location           Age       
##  Length:50000       Length:50000       Length:50000       Min.   :19.00  
##  Class :character   Class :character   Class :character   1st Qu.:29.00  
##  Mode  :character   Mode  :character   Mode  :character   Median :39.00  
##                                                           Mean   :38.92  
##                                                           3rd Qu.:49.00  
##                                                           Max.   :59.00  
##    Academic             Job               Grade           Number_of_children
##  Length:50000       Length:50000       Length:50000       Length:50000      
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##  Private_vehicle        Home               Income         Spending    
##  Length:50000       Length:50000       Min.   : 4500   Min.   : 3000  
##  Class :character   Class :character   1st Qu.: 5381   1st Qu.: 4216  
##  Mode  :character   Mode  :character   Median : 7505   Median : 5247  
##                                        Mean   : 8704   Mean   : 5783  
##                                        3rd Qu.:11377   3rd Qu.: 7115  
##                                        Max.   :20000   Max.   :11000

`

R

Datasoal2$NetWorth = Datasoal2$Income-Datasoal2$Spending

Datasoal2

#Potential untuk dipertahankan
subset(Datasoal2, NetWorth>= 7000)