names x_values y_values 697 114516 10000.205 7860 154584 8888.918889 22RV1 228312 12000.064 42MGBA 140076 6666.674444 639V 157752 6000.107 A172 144936 12000.137 A204 208656 40000.12 A3KAWAKAMI 83808 10000.135 A498 188748 7500.17125 A4FUK 256284 15000.07 A549 82440 5714.298571 ACHN 113112 15000.2675 AN3CA 246096 7500.00875 BC1 126252 10000.11 BFTC909 157860 5000.039167 BHY 284580 4000.002 BL41 156348 6000.158 BPH1 232812 13333.4 BV173 132156 20000.185 BXPC3 215784 2857.143571 CA46 176184 8000.046 CAKI1 164556 13333.38556 CAL120 158940 3636.376364 CAL27 171108 15000.0175 CAL29 157896 6000.037 CAL51 110844 20000.295 CAL72 200052 8889.018889 CAL851 213444 11428.56714 CALU6 202284 11428.61 CAR1 372672 10000.16167 CCK81 157968 40000.21 CHP134 467100 13333.56167 CL11 259596 4000.088 CMLT1 175320 6666.793333 DANG 163872 13333.36167 DAOY 156996 6666.73 DB 142596 5000.0675 DBTRG05MG 470700 7500.09625 DEL 102168 10000.1525 EFM19 1256004 6000.127 EFO21 371448 10000.01167 EFO27 196596 6666.696667 EN 516708 40000.035 EPLC272H 169596 10000.01167 ESS1 156852 6000.167 FADU 141768 7500.05875 FLO1 160596 8571.638571 FU97 350208 6666.878333 FUOV1 473616 6666.663333 GAMG 184824 5454.551818 GB1 221868 8571.595714 H358 181440 5714.322857 H460 90684 4444.446667 H82 134820 13333.44 H929 265140 20000.855 HCC366 250884 5714.295714 HCC44 152712 8000.178 HCC827 237744 8000.025 HCT116 101376 15000.0375 HCT15 92232 8888.905556 HDQP1 290592 15000.3375 HEL 128052 7500.00375 HELA 213156 20000.19 HL60 198432 3636.463636 HOP62 170568 8000.108 HS578T 190188 15000.1125 HSC2 120528 30000.525 HSC3 122688 15000.4375 HT 134100 11111.27 HT1080 281916 6666.705556 HT29 102060 15000.1125 HUH1 177336 10000.04167 HUH7 485676 8571.607143 HUTU80 91584 30000.125 IGROV1 162828 20000.01667 IM95 187560 12000.05 IPC298 144144 10000.04167 JAR 133308 20000.05 JEKO1 142128 8571.547143 JHH2 204048 8888.95 JHH4 217044 8571.435714 JHH6 155844 12000.07 JHH7 182520 13333.44667 JVM3 142092 20000.14333 K562 153684 8000.17 KALS1 221796 20000.05 KARPAS299 136836 12000.154 KASUMI1 257616 12000.086 KCL22 123804 12000.006 KE37 183816 10000.13833 KM12 120060 5454.622727 KMRC20 171684 7500.11875 KMS11 136332 7499.99625 KMS12BM 198252 5000.00875 KON 103248 10000.09167 KOPN8 160380 13333.43833 KP3 226260 30000.025 KU1919 120672 5454.577273 KU812 291636 15000.0825 KYM1 131652 10000.00833 KYSE150 101196 7500.00625 KYSE180 140616 5714.495714 KYSE410 159012 5714.321429 KYSE450 150120 7500.00625 KYSE510 152172 4444.473333 KYSE70 254916 7500.08125 L428 145584 11428.71714 L540 302436 20000.17333 LAMA84 156276 11428.76143 LCLC103H 221724 5714.404286 LCLC97TM1 166968 7500.01875 LK2 299628 8571.478571 LNCAPCLONEFGC 146520 40000.96 LOVO 137124 10000.0275 LOXIMVI 112644 10000.1525 LS411N 69228 10000.09833 LU65 135900 11428.59857 LXF289 148068 10000.24833 MC116 115092 12000.17 MCF7 119484 6666.865556 MDAMB231 134244 8571.427143 MDAMB453 219276 8000.156 MDAMB468 260892 11428.64143 MESSA 130644 20000.118 MFE280 271872 7500.02375 MFE319 378360 11428.72714 MHHNB11 349884 16000.038 MIAPACA2 140004 6666.71 MM1S 292788 40000.65 MOLM16 253080 11111.13556 MOLT16 142308 8000.144 MSTO211H 153288 10000.11125 MV411 120888 10000.045 NALM6 140256 12000.01 NB4 136800 5454.547273 NUDUL1 166032 16000.01 NUGC3 132048 6666.865556 OACM51C 184716 12000.038 OCIAML2 168372 15000.38 OCIAML3 171036 8000.326 OCIM1 112464 11111.15778 OE19 225504 6666.876667 ONS76 104436 6666.732222 OSC19 165672 4444.452222 OSC20 225612 15000.3225 OVCAR5 137916 15000.2225 OVCAR8 127512 8333.4825 P12ICHIKAWA 140004 4444.493333 PATU8988T 118620 12000.138 PC3 231120 11428.67 QGP1 206748 5000.08875 RAJI 131256 12000.002 RCHACV 141732 12000.082 RCK8 172440 20000.43667 RERFLCKJ 412416 6666.704444 RERFLCMS 113508 7500.0175 RPMI8226 180792 6666.7 RPMI8402 137124 5714.384286 RS411 178632 10000.035 RT112 173340 10000.25667 RT4 173556 11428.79143 SAOS2 356868 7500.13 SAS 110340 10000.14 SCC4 223812 10000.22333 SF126 94212 12500.0175 SF268 159408 8888.948889 SF539 128484 15000.03 SIGM5 235260 20000.0975 SIMA 301788 15000.01 SKES1 153432 8571.434286 SKHEP1 140220 11111.14889 SKM1 164448 8571.498571 SKMEL28 175176 14285.86286 SKMEL3 165348 5454.549091 SKMEL30 154008 14285.72 SKMES1 219456 7500.0175 SKOV3 216216 20000.08 SNU1 116100 16000.048 SNU16 149472 7500.055 SR 126036 6666.678333 SUDHL1 119448 8000.041 SUDHL10 104436 15000.4775 SUDHL4 111096 11428.70143 SUDHL5 126972 40000.805 SUPM2 152136 16000.082 SUPT1 144396 8571.527143 SW620 116532 10000.024 T24 273492 6000.004 TALL1 231156 12000.018 TC71 136764 8888.993333 TCCSUP 304524 17143.13429 TGW 140724 6666.803333 U2OS 258768 8571.462857 UCH2 6106248 8571.491429 UO31 136908 13333.40667 UWB1289 178020 11111.12667 VAL 192492 20000.00333 WSUNHL 132912 11428.80143 Using the following dataset please suggest any ML method which can be used to predict the values for future data. x_values is the input data and y_values is the predicted value. Range of x_values can be much higher than what is present in the dataset. Please suggest. Continue reading...
