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   Jakarta     Adara  9188 2018-10-07
## 54     54          Irene      3.6     Depok   Peronia 10411 2019-09-21
## 104   104          Irene      3.6    Bekasi     Arana 14044 2019-01-20
## 154   154          Irene      3.6    Bekasi  Alamanda 11762 2019-12-08
## 204   204          Irene      3.6     Depok     Asoka 13827 2018-06-12
## 254   254          Irene      3.6   Jakarta   Mutiara 13603 2019-04-24
## 304   304          Irene      3.6    Bekasi Teradamai  9074 2018-01-29
## 354   354          Irene      3.6    Bekasi     Asoka  7163 2020-02-02
## 404   404          Irene      3.6    Bekasi     Asera 13816 2018-07-11
## 454   454          Irene      3.6     Bogor Teradamai 12918 2019-09-07
## 504   504          Irene      3.6   Jakarta    Narada  8365 2020-09-11
## 554   554          Irene      3.6 Tengerang Teradamai 14902 2018-09-25
## 604   604          Irene      3.6     Depok     Arana 12190 2018-04-24
## 654   654          Irene      3.6     Bogor     Adara  9155 2018-06-14
## 704   704          Irene      3.6    Bekasi   Albasia 11558 2019-03-26
## 754   754          Irene      3.6   Jakarta    Narada 12181 2019-12-13
## 804   804          Irene      3.6   Jakarta     Arana  7203 2018-07-20
## 854   854          Irene      3.6     Bogor     Asoka 10741 2020-07-23
## 904   904          Irene      3.6     Bogor Sweethome  7678 2018-06-22
## 954   954          Irene      3.6     Depok    Winona 13314 2018-03-02
## 1004 1004          Irene      3.6 Tengerang   Albasia 12225 2018-03-28
## 1054 1054          Irene      3.6 Tengerang   Albasia 11273 2019-09-01
## 1104 1104          Irene      3.6 Tengerang   Permata 14579 2019-01-25
## 1154 1154          Irene      3.6 Tengerang     Adara 13782 2020-06-02
## 1204 1204          Irene      3.6 Tengerang    Lavesh 11476 2018-08-01
## 1254 1254          Irene      3.6     Bogor     Adara 11054 2018-08-26
## 1304 1304          Irene      3.6     Depok   Albasia 11651 2020-04-05
## 1354 1354          Irene      3.6     Bogor     Asoka 10230 2018-04-14
## 1404 1404          Irene      3.6   Jakarta Primadona 11482 2018-03-14
## 1454 1454          Irene      3.6     Bogor   Permata  7890 2019-12-10
## 1504 1504          Irene      3.6   Jakarta     Adara 13705 2019-07-22
## 1554 1554          Irene      3.6     Depok   Peronia  7233 2020-05-06
## 1604 1604          Irene      3.6 Tengerang     Tiara 13849 2020-06-08
## 1654 1654          Irene      3.6     Depok   Permata 11249 2018-07-25
## 1704 1704          Irene      3.6 Tengerang    Winona 14119 2019-08-10
## 1754 1754          Irene      3.6   Jakarta     Tiara 11038 2018-04-29
## 1804 1804          Irene      3.6     Bogor Sweethome 12470 2018-09-09
## 1854 1854          Irene      3.6   Jakarta     Arana 13848 2019-09-08
## 1904 1904          Irene      3.6     Bogor     Tiara 14019 2018-11-21
## 1954 1954          Irene      3.6     Depok      Neon 14537 2020-01-22
## 2004 2004          Irene      3.6 Tengerang     Asera 12382 2020-03-13
## 2054 2054          Irene      3.6     Depok   Albasia 14826 2019-11-15
## 2104 2104          Irene      3.6 Tengerang    Winona  9905 2018-04-01
## 2154 2154          Irene      3.6   Jakarta      Neon  8521 2020-08-23
## 2204 2204          Irene      3.6    Bekasi Primadona 12178 2018-04-26
## 2254 2254          Irene      3.6    Bekasi   Peronia 10396 2018-06-30
## 2304 2304          Irene      3.6   Jakarta     Asoka 10373 2018-02-21
## 2354 2354          Irene      3.6 Tengerang   Palmyra 13481 2018-12-19
## 2404 2404          Irene      3.6    Bekasi     Arana  9404 2020-02-24
## 2454 2454          Irene      3.6 Tengerang   Mutiara 13956 2018-10-21
## 2504 2504          Irene      3.6     Depok  Victoria 12189 2020-04-08
## 2554 2554          Irene      3.6     Depok Teradamai 12372 2018-09-11
## 2604 2604          Irene      3.6 Tengerang      Neon  8145 2018-03-15
## 2654 2654          Irene      3.6     Depok   Permata 10473 2019-02-23
## 2704 2704          Irene      3.6 Tengerang   Alindra  7889 2018-02-27
## 2754 2754          Irene      3.6    Bekasi     Adara 13949 2019-07-03
## 2804 2804          Irene      3.6     Depok   Mutiara 12752 2018-05-22
## 2854 2854          Irene      3.6   Jakarta    Lavesh 13272 2019-12-19
## 2904 2904          Irene      3.6     Depok   Permata  8643 2019-01-06
## 2954 2954          Irene      3.6     Bogor   Palmyra 14491 2018-03-20
## 3004 3004          Irene      3.6     Bogor  Victoria  9148 2018-03-06
## 3054 3054          Irene      3.6     Depok   Alindra 10143 2019-10-22
## 3104 3104          Irene      3.6    Bekasi   Albasia 11148 2019-01-08
## 3154 3154          Irene      3.6   Jakarta   Albasia  7394 2019-07-08
## 3204 3204          Irene      3.6     Depok     Adara  9878 2019-04-13
## 3254 3254          Irene      3.6 Tengerang   Permata 14711 2018-02-25
## 3304 3304          Irene      3.6   Jakarta Primadona 11424 2020-04-01
## 3354 3354          Irene      3.6    Bekasi   Permata 11749 2020-09-11
## 3404 3404          Irene      3.6     Depok   Palmyra  7344 2018-02-04
## 3454 3454          Irene      3.6   Jakarta    Winona 11294 2018-07-06
## 3504 3504          Irene      3.6 Tengerang   Alindra 11284 2020-05-08
## 3554 3554          Irene      3.6 Tengerang   Albasia 13889 2019-04-22
## 3604 3604          Irene      3.6     Bogor   Mutiara 11393 2020-04-21
## 3654 3654          Irene      3.6     Bogor Sweethome  9716 2020-02-19
## 3704 3704          Irene      3.6   Jakarta     Asoka 13360 2019-08-20
## 3754 3754          Irene      3.6 Tengerang     Adara 14261 2019-09-06
## 3804 3804          Irene      3.6    Bekasi    Narada 12999 2019-05-22
## 3854 3854          Irene      3.6    Bekasi    Winona 12390 2018-09-14
## 3904 3904          Irene      3.6     Bogor   Peronia  9484 2019-03-25
## 3954 3954          Irene      3.6   Jakarta     Arana 13274 2020-05-19
## 4004 4004          Irene      3.6     Depok   Peronia 12849 2019-12-17
## 4054 4054          Irene      3.6     Depok     Tiara 12054 2020-09-26
## 4104 4104          Irene      3.6 Tengerang   Permata  8261 2018-08-28
## 4154 4154          Irene      3.6    Bekasi     Asoka 14057 2018-09-08
## 4204 4204          Irene      3.6     Depok   Permata  8610 2020-03-14
## 4254 4254          Irene      3.6     Bogor     Asoka 13041 2018-08-01
## 4304 4304          Irene      3.6     Depok     Adara 10352 2020-09-08
## 4354 4354          Irene      3.6     Depok Primadona 11541 2019-01-09
## 4404 4404          Irene      3.6    Bekasi Teradamai  9997 2018-03-29
## 4454 4454          Irene      3.6   Jakarta    Winona 10109 2019-08-30
## 4504 4504          Irene      3.6   Jakarta   Albasia 14703 2019-08-27
## 4554 4554          Irene      3.6    Bekasi Primadona 10562 2018-09-07
## 4604 4604          Irene      3.6     Depok Teradamai  9830 2020-08-04
## 4654 4654          Irene      3.6     Bogor    Winona  9425 2019-10-23
## 4704 4704          Irene      3.6   Jakarta   Palmyra 11036 2020-03-30
## 4754 4754          Irene      3.6     Bogor  Victoria 13339 2018-03-22
## 4804 4804          Irene      3.6     Bogor      Neon  8945 2018-11-18
## 4854 4854          Irene      3.6    Bekasi Primadona  8265 2019-01-26
## 4904 4904          Irene      3.6   Jakarta     Adara 14740 2018-09-07
## 4954 4954          Irene      3.6     Depok      Neon 10775 2020-07-12
## 5004 5004          Irene      3.6 Tengerang Primadona 11235 2018-03-24
## 5054 5054          Irene      3.6    Bekasi  Victoria 11085 2019-05-30
## 5104 5104          Irene      3.6 Tengerang    Narada  8294 2019-01-04
## 5154 5154          Irene      3.6     Depok  Victoria  7033 2019-03-25
## 5204 5204          Irene      3.6    Bekasi   Palmyra  8582 2018-09-09
## 5254 5254          Irene      3.6 Tengerang     Asoka 10734 2018-06-21
## 5304 5304          Irene      3.6    Bekasi    Lavesh  8363 2018-11-18
## 5354 5354          Irene      3.6     Depok     Tiara 14456 2018-09-19
## 5404 5404          Irene      3.6     Bogor   Permata  7286 2018-12-19
## 5454 5454          Irene      3.6   Jakarta     Tiara  9500 2019-03-07
## 5504 5504          Irene      3.6     Bogor  Victoria 14113 2019-06-12
## 5554 5554          Irene      3.6 Tengerang   Permata  9847 2019-09-02
## 5604 5604          Irene      3.6   Jakarta    Winona 11138 2019-06-13
## 5654 5654          Irene      3.6     Bogor Teradamai 11394 2019-06-27
## 5704 5704          Irene      3.6     Depok  Victoria 11451 2019-03-08
## 5754 5754          Irene      3.6   Jakarta   Peronia 10013 2020-09-19
## 5804 5804          Irene      3.6     Bogor   Albasia  8668 2020-06-05
## 5854 5854          Irene      3.6   Jakarta   Albasia 14492 2018-04-17
## 5904 5904          Irene      3.6     Depok    Winona  8306 2018-05-10
## 5954 5954          Irene      3.6     Depok  Victoria 11708 2020-05-12
## 6004 6004          Irene      3.6   Jakarta   Peronia 10149 2019-11-11
## 6054 6054          Irene      3.6     Depok  Victoria 12246 2019-01-09
## 6104 6104          Irene      3.6 Tengerang    Winona 10838 2020-04-13
## 6154 6154          Irene      3.6     Bogor  Victoria 13740 2019-07-20
## 6204 6204          Irene      3.6     Bogor Sweethome 14399 2018-09-04
## 6254 6254          Irene      3.6   Jakarta Sweethome  8862 2019-04-07
## 6304 6304          Irene      3.6   Jakarta    Lavesh 12395 2020-01-22
## 6354 6354          Irene      3.6   Jakarta   Alindra 10454 2018-05-28
## 6404 6404          Irene      3.6     Bogor     Tiara 11398 2018-03-17
## 6454 6454          Irene      3.6   Jakarta   Peronia  9325 2020-01-13
## 6504 6504          Irene      3.6     Bogor   Permata 11853 2018-11-18
## 6554 6554          Irene      3.6   Jakarta    Winona 14758 2018-02-05
## 6604 6604          Irene      3.6     Bogor  Alamanda 13859 2018-07-03
## 6654 6654          Irene      3.6     Depok   Albasia 12744 2020-02-05
## 6704 6704          Irene      3.6     Bogor   Mutiara 14902 2020-08-15
## 6754 6754          Irene      3.6     Bogor   Alindra 10253 2018-01-18
## 6804 6804          Irene      3.6   Jakarta Sweethome  7333 2019-09-26
## 6854 6854          Irene      3.6     Depok     Asera  9223 2018-02-19
## 6904 6904          Irene      3.6 Tengerang     Arana 13158 2020-02-13
## 6954 6954          Irene      3.6   Jakarta   Palmyra 11973 2018-07-28
## 7004 7004          Irene      3.6   Jakarta    Winona 13364 2018-05-03
## 7054 7054          Irene      3.6 Tengerang      Neon  8781 2019-12-06
## 7104 7104          Irene      3.6     Depok   Albasia 14767 2018-01-10
## 7154 7154          Irene      3.6     Bogor   Albasia 13621 2019-12-13
## 7204 7204          Irene      3.6   Jakarta  Victoria 12168 2020-02-07
## 7254 7254          Irene      3.6    Bekasi   Peronia 13988 2019-06-30
## 7304 7304          Irene      3.6     Depok Primadona  9249 2018-04-20
## 7354 7354          Irene      3.6     Bogor    Narada  9539 2018-03-23
## 7404 7404          Irene      3.6   Jakarta   Permata 11117 2020-01-21
## 7454 7454          Irene      3.6    Bekasi Primadona  8316 2019-10-09
## 7504 7504          Irene      3.6   Jakarta    Lavesh 13010 2019-12-05
## 7554 7554          Irene      3.6   Jakarta    Lavesh 14844 2019-12-12
## 7604 7604          Irene      3.6   Jakarta    Winona 13688 2019-07-09
## 7654 7654          Irene      3.6     Bogor     Asoka  7569 2018-08-08
## 7704 7704          Irene      3.6     Depok     Asera  9488 2018-12-02
## 7754 7754          Irene      3.6   Jakarta  Victoria 11816 2019-09-20
## 7804 7804          Irene      3.6    Bekasi    Winona 12367 2018-02-09
## 7854 7854          Irene      3.6    Bekasi   Palmyra  9825 2019-04-17
## 7904 7904          Irene      3.6 Tengerang    Narada 11825 2018-12-12
## 7954 7954          Irene      3.6 Tengerang   Mutiara 12989 2020-05-25
## 8004 8004          Irene      3.6     Depok   Peronia 12088 2018-01-20
## 8054 8054          Irene      3.6   Jakarta     Arana 13393 2019-06-14
## 8104 8104          Irene      3.6   Jakarta     Asera 10476 2018-08-29
## 8154 8154          Irene      3.6 Tengerang     Adara 13083 2018-12-02
## 8204 8204          Irene      3.6   Jakarta  Alamanda 13712 2020-03-08
## 8254 8254          Irene      3.6     Bogor Primadona 13494 2018-01-26
## 8304 8304          Irene      3.6   Jakarta Primadona  8598 2020-03-23
## 8354 8354          Irene      3.6    Bekasi  Victoria 10622 2019-04-02
## 8404 8404          Irene      3.6     Depok  Alamanda 13436 2018-04-24
## 8454 8454          Irene      3.6 Tengerang   Mutiara  8358 2019-11-02
## 8504 8504          Irene      3.6     Depok    Winona  8153 2019-02-22
## 8554 8554          Irene      3.6     Bogor     Asera 11694 2019-06-04
## 8604 8604          Irene      3.6   Jakarta  Alamanda 14861 2020-06-07
## 8654 8654          Irene      3.6   Jakarta     Asera 13668 2019-03-25
## 8704 8704          Irene      3.6    Bekasi   Peronia  7743 2018-09-05
## 8754 8754          Irene      3.6     Bogor   Alindra  7389 2018-11-23
## 8804 8804          Irene      3.6     Bogor     Adara  8612 2020-04-25
## 8854 8854          Irene      3.6     Bogor Primadona 13852 2018-03-07
## 8904 8904          Irene      3.6   Jakarta Sweethome  9930 2018-02-19
## 8954 8954          Irene      3.6     Bogor   Alindra 14328 2018-02-21
## 9004 9004          Irene      3.6     Depok    Lavesh  8170 2018-02-21
## 9054 9054          Irene      3.6 Tengerang   Permata  9806 2020-06-05
## 9104 9104          Irene      3.6 Tengerang     Arana 13882 2020-07-31
## 9154 9154          Irene      3.6 Tengerang     Arana 14126 2020-03-10
## 9204 9204          Irene      3.6     Bogor Teradamai 14652 2019-06-08
## 9254 9254          Irene      3.6     Depok     Asera 14955 2019-08-31
## 9304 9304          Irene      3.6     Depok     Adara 12672 2020-07-09
## 9354 9354          Irene      3.6 Tengerang   Permata  7219 2018-04-06
## 9404 9404          Irene      3.6    Bekasi     Asoka 11678 2019-04-10
## 9454 9454          Irene      3.6     Bogor   Peronia 11555 2019-07-02
## 9504 9504          Irene      3.6    Bekasi     Adara 14757 2018-05-25
## 9554 9554          Irene      3.6     Bogor   Palmyra 13129 2018-04-30
## 9604 9604          Irene      3.6    Bekasi     Asera  7928 2020-06-07
## 9654 9654          Irene      3.6 Tengerang  Victoria 10190 2019-02-11
## 9704 9704          Irene      3.6 Tengerang  Alamanda  8102 2018-07-18
## 9754 9754          Irene      3.6   Jakarta Sweethome 14224 2019-10-21
## 9804 9804          Irene      3.6   Jakarta    Winona  9124 2018-06-30
## 9854 9854          Irene      3.6   Jakarta      Neon 10619 2019-07-26
## 9904 9904          Irene      3.6     Depok     Tiara  7888 2019-08-14
## 9954 9954          Irene      3.6     Depok     Tiara 13760 2018-01-05
##      Advertisement Classes BookingFee
## 4               12     Low     643.16
## 54              20  Medium     832.88
## 104             10    High    1404.40
## 154              5  Medium    1058.58
## 204             12    High    1382.70
## 254              4    High    1360.30
## 304             19     Low     635.18
## 354              3     Low     358.15
## 404              4    High    1381.60
## 454              3    High    1162.62
## 504             10     Low     501.90
## 554              9    High    1490.20
## 604             12    High    1097.10
## 654             19     Low     640.85
## 704             12  Medium    1040.22
## 754              9    High    1096.29
## 804              9     Low     360.15
## 854             10  Medium     859.28
## 904             14     Low     383.90
## 954              3    High    1331.40
## 1004             3    High    1100.25
## 1054             6  Medium    1014.57
## 1104             5    High    1457.90
## 1154            16    High    1378.20
## 1204             6  Medium    1032.84
## 1254            13  Medium     994.86
## 1304            14  Medium    1048.59
## 1354             9  Medium     818.40
## 1404            11  Medium    1033.38
## 1454             3     Low     394.50
## 1504             3    High    1370.50
## 1554             4     Low     361.65
## 1604            20    High    1384.90
## 1654            20  Medium    1012.41
## 1704            17    High    1411.90
## 1754            10  Medium     993.42
## 1804            11    High    1122.30
## 1854            12    High    1384.80
## 1904             2    High    1401.90
## 1954             1    High    1453.70
## 2004             1    High    1114.38
## 2054             8    High    1482.60
## 2104            16     Low     693.35
## 2154             4     Low     511.26
## 2204             1    High    1096.02
## 2254             4  Medium     831.68
## 2304             6  Medium     829.84
## 2354            19    High    1348.10
## 2404             9     Low     658.28
## 2454            14    High    1395.60
## 2504             8    High    1097.01
## 2554            12    High    1113.48
## 2604            11     Low     488.70
## 2654             4  Medium     837.84
## 2704             1     Low     394.45
## 2754             6    High    1394.90
## 2804             4    High    1147.68
## 2854            15    High    1327.20
## 2904             5     Low     518.58
## 2954            14    High    1449.10
## 3004             5     Low     640.36
## 3054            19  Medium     811.44
## 3104            14  Medium    1003.32
## 3154            14     Low     369.70
## 3204             8     Low     691.46
## 3254             5    High    1471.10
## 3304             5  Medium    1028.16
## 3354             1  Medium    1057.41
## 3404            11     Low     367.20
## 3454            10  Medium    1016.46
## 3504            13  Medium    1015.56
## 3554            13    High    1388.90
## 3604            13  Medium    1025.37
## 3654            14     Low     680.12
## 3704            20    High    1336.00
## 3754             7    High    1426.10
## 3804             3    High    1169.91
## 3854             8    High    1115.10
## 3904            17     Low     663.88
## 3954             7    High    1327.40
## 4004             7    High    1156.41
## 4054             8    High    1084.86
## 4104            19     Low     495.66
## 4154            10    High    1405.70
## 4204             5     Low     516.60
## 4254            10    High    1304.10
## 4304             7  Medium     828.16
## 4354             9  Medium    1038.69
## 4404            10     Low     699.79
## 4454            12  Medium     808.72
## 4504            15    High    1470.30
## 4554            12  Medium     844.96
## 4604             6     Low     688.10
## 4654            10     Low     659.75
## 4704            20  Medium     993.24
## 4754            12    High    1333.90
## 4804             3     Low     536.70
## 4854            16     Low     495.90
## 4904            19    High    1474.00
## 4954            16  Medium     862.00
## 5004            18  Medium    1011.15
## 5054             4  Medium     997.65
## 5104             1     Low     497.64
## 5154             4     Low     351.65
## 5204            10     Low     514.92
## 5254            11  Medium     858.72
## 5304             5     Low     501.78
## 5354             4    High    1445.60
## 5404             4     Low     364.30
## 5454            11     Low     665.00
## 5504            15    High    1411.30
## 5554            16     Low     689.29
## 5604             8  Medium    1002.42
## 5654            16  Medium    1025.46
## 5704             8  Medium    1030.59
## 5754             5  Medium     801.04
## 5804            15     Low     520.08
## 5854            10    High    1449.20
## 5904            16     Low     498.36
## 5954             3  Medium    1053.72
## 6004            16  Medium     811.92
## 6054            10    High    1102.14
## 6104             9  Medium     867.04
## 6154            18    High    1374.00
## 6204            17    High    1439.90
## 6254            15     Low     531.72
## 6304             6    High    1115.55
## 6354            17  Medium     836.32
## 6404             6  Medium    1025.82
## 6454             3     Low     652.75
## 6504            16  Medium    1066.77
## 6554            20    High    1475.80
## 6604             5    High    1385.90
## 6654             6    High    1146.96
## 6704            18    High    1490.20
## 6754             2  Medium     820.24
## 6804            18     Low     366.65
## 6854             6     Low     645.61
## 6904            20    High    1315.80
## 6954            18  Medium    1077.57
## 7004             1    High    1336.40
## 7054             5     Low     526.86
## 7104             5    High    1476.70
## 7154            18    High    1362.10
## 7204             3    High    1095.12
## 7254            19    High    1398.80
## 7304             9     Low     647.43
## 7354            14     Low     667.73
## 7404            19  Medium    1000.53
## 7454            12     Low     498.96
## 7504            15    High    1301.00
## 7554            20    High    1484.40
## 7604            12    High    1368.80
## 7654            16     Low     378.45
## 7704             3     Low     664.16
## 7754            13  Medium    1063.44
## 7804            11    High    1113.03
## 7854            12     Low     687.75
## 7904             4  Medium    1064.25
## 7954             5    High    1169.01
## 8004             4    High    1087.92
## 8054            18    High    1339.30
## 8104             2  Medium     838.08
## 8154             1    High    1308.30
## 8204             3    High    1371.20
## 8254            14    High    1349.40
## 8304            10     Low     515.88
## 8354            17  Medium     849.76
## 8404             3    High    1343.60
## 8454            15     Low     501.48
## 8504             6     Low     489.18
## 8554             1  Medium    1052.46
## 8604             5    High    1486.10
## 8654             8    High    1366.80
## 8704             8     Low     387.15
## 8754             2     Low     369.45
## 8804             4     Low     516.72
## 8854             3    High    1385.20
## 8904            19     Low     695.10
## 8954             2    High    1432.80
## 9004            18     Low     490.20
## 9054            20     Low     686.42
## 9104            10    High    1388.20
## 9154             8    High    1412.60
## 9204            16    High    1465.20
## 9254            14    High    1495.50
## 9304             7    High    1140.48
## 9354             5     Low     360.95
## 9404            19  Medium    1051.02
## 9454            18  Medium    1039.95
## 9504            18    High    1475.70
## 9554            18    High    1312.90
## 9604             2     Low     396.40
## 9654             2  Medium     815.20
## 9704            14     Low     486.12
## 9754            12    High    1422.40
## 9804             9     Low     638.68
## 9854             4  Medium     849.52
## 9904             4     Low     394.40
## 9954             9    High    1376.00

R

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


DataIrene$Bonus= DataIrene$BookingFee*3/100

DataIrene

Totalbonus=sum(DataIrene$Bonus)
Totalbonus

## [1] 5884.546

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] 7992

# 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 iklan42.42NULL
## Sherly Rata-rata pengeluaran untuk iklan40.08NULL
## Vanessa Rata-rata pengeluaran untuk iklan42.78NULL
## Irene Rata-rata pengeluaran untuk iklan39.96NULL
## Julian Rata-rata pengeluaran untuk iklan41.64NULL
## Jeffry Rata-rata pengeluaran untuk iklan41.76NULL
## Nikita Rata-rata pengeluaran untuk iklan40.24NULL
## Kefas Rata-rata pengeluaran untuk iklan42.06NULL
## Siana Rata-rata pengeluaran untuk iklan40.86NULL
## Lala Rata-rata pengeluaran untuk iklan44.38NULL
## Fallen Rata-rata pengeluaran untuk iklan41.42NULL
## Ardifo Rata-rata pengeluaran untuk iklan43.5NULL
## Kevin Rata-rata pengeluaran untuk iklan41.28NULL
## Juen Rata-rata pengeluaran untuk iklan43.34NULL
## Jerrel Rata-rata pengeluaran untuk iklan42.8NULL
## Imelda Rata-rata pengeluaran untuk iklan41.22NULL
## Widi Rata-rata pengeluaran untuk iklan40.04NULL
## Theodora Rata-rata pengeluaran untuk iklan41.88NULL
## Elvani Rata-rata pengeluaran untuk iklan43.18NULL
## Jonathan Rata-rata pengeluaran untuk iklan43.54NULL
## Sofia Rata-rata pengeluaran untuk iklan41.32NULL
## Abraham Rata-rata pengeluaran untuk iklan41.38NULL
## Siti Rata-rata pengeluaran untuk iklan41.68NULL
## Niko Rata-rata pengeluaran untuk iklan43.78NULL
## Sefli Rata-rata pengeluaran untuk iklan41.9NULL
## Bene Rata-rata pengeluaran untuk iklan42.22NULL
## Diana Rata-rata pengeluaran untuk iklan42.82NULL
## Pupe Rata-rata pengeluaran untuk iklan42.36NULL
## Andi Rata-rata pengeluaran untuk iklan41.82NULL
## Tatha Rata-rata pengeluaran untuk iklan43.04NULL
## Endri Rata-rata pengeluaran untuk iklan42.74NULL
## Monika Rata-rata pengeluaran untuk iklan44.18NULL
## Hans Rata-rata pengeluaran untuk iklan42.96NULL
## Debora Rata-rata pengeluaran untuk iklan41.9NULL
## Hanifa Rata-rata pengeluaran untuk iklan38.72NULL
## James Rata-rata pengeluaran untuk iklan40.42NULL
## Jihan Rata-rata pengeluaran untuk iklan42.46NULL
## Friska Rata-rata pengeluaran untuk iklan41.94NULL
## Ardiwan Rata-rata pengeluaran untuk iklan41.42NULL
## Bakti Rata-rata pengeluaran untuk iklan41.88NULL
## Anthon Rata-rata pengeluaran untuk iklan42.5NULL
## Amry Rata-rata pengeluaran untuk iklan41.18NULL
## Wiwik Rata-rata pengeluaran untuk iklan42.28NULL
## Bastian Rata-rata pengeluaran untuk iklan42.3NULL
## Budi Rata-rata pengeluaran untuk iklan42.4NULL
## Leo Rata-rata pengeluaran untuk iklan42.36NULL
## Simon Rata-rata pengeluaran untuk iklan41.68NULL
## Matius Rata-rata pengeluaran untuk iklan39.46NULL
## Arry Rata-rata pengeluaran untuk iklan44.76NULL
## Eliando Rata-rata pengeluaran untuk iklan43.94NULL

# Pendapatan

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

revenue

## [1] 919810.5

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   :38.98  
##                                                           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.: 6224   1st Qu.: 4619  
##  Mode  :character   Mode  :character   Median : 9869   Median : 6398  
##                                        Mean   :10067   Mean   : 6455  
##                                        3rd Qu.:13091   3rd Qu.: 7968  
##                                        Max.   :20000   Max.   :11000

`

R

Datasoal2$NetWorth = Datasoal2$Income-Datasoal2$Spending

Datasoal2

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