Supervised Learning - Classification Week 8 Challenge
Question 1: Apply knn to the “Surface defects in stainless steel plates” and identify the differences?
# "KNN Coding using Steel plate surface defect data set"
# "Importing the Required libraries"
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from math import gamma
from typing import Type
# "Importing the Steel Plate Dataset using Pandas"
Steel_df=pd.read_csv("faults.csv")
# "Describing the dataframe"
print(Steel_df.describe)
print(Steel_df.dtypes)
# Last seven columns are type of error classification
label_columns=Steel_df.columns.values[-7:]
# Assigning the error classification values to target variables
targets=(Steel_df.iloc[:,-7:]).idxmax(1)
print('n',label_columns)
print('n',targets)
# Dropping the 7 error classification columns and retaining only the targets
dataset=Steel_df.drop(label_columns,axis=1)
# creating a new value 'target' with all classification values
dataset['target']=targets
print('n',dataset)
# Printing count of each type of errors
print('n',dataset.target.value_counts())
#Countplot
sns.catplot(x ="X_Minimum", hue ="Pastry", kind="count", data = Steel_df)
# "Checking the Null Values & the count of the Columns"
print(Steel_df.isnull().sum())
print(Steel_df.count())
# Dropping features with high correlation to others
dataset=dataset.drop('TypeOfSteel_A400',axis=1)
dataset=dataset.drop('X_Minimum',axis=1)
dataset=dataset.drop('Y_Minimum',axis=1)
print(dataset.shape)
# Assigning feature column values to X
X=dataset.iloc[:,0:24]
# Assigning feature column values to Y
Y=dataset.iloc[:,24]
# "Splitting the Data set into Test & Training sets"
X_train,Y_train,X_test,Y_test=train_test_split(X,Y,test_size=0.2)
# Count of each target classification in test set
index=pd.Index(Y_test)
print(index.value_counts())
# "KNN Algorithm & Comparing the Accuracy with other algorithm"
# K-Fold Cross Validation
from sklearn.model_selection import KFold
kf = KFold(n_splits=5)
kf
KFold(n_splits=5, random_state=None, shuffle=False)
for train_index,test_index in kf.split(dataset):
print(train_index,test_index)
# comparing the Accuracy using the get_score function
def get_score(model,X_train,X_test,Y_train,Y_test):
model.fit(X_train,Y_train)
return model.score(X_test,Y_test)
print("SVC Accuracy = ",get_score(SVC(),X_train,X_test,Y_train,Y_test))
print("KNN Accuracy = ",get_score(KNeighborsClassifier(n_neighbors=4),X_train,X_test,Y_train,Y_test))
print("Random Forest Accuracy = ",get_score(RandomForestClassifier(n_estimators=40),X_train,X_test,Y_train,Y_test))
# Startified K-Fold
from sklearn.model_selection import cross_val_score
print("SVC Accuracy_Startified K-Fold = ",cross_val_score(SVC(),X,Y))
print("KNN Accuracy_Startified K-Fold = ",cross_val_score(KNeighborsClassifier(n_neighbors=4),X,Y))
print("Random Forest Accuracy_Startified K-Fold = ",cross_val_score(RandomForestClassifier(n_estimators=40),X,Y))Output:
1.# Printing the Describe function:
<bound method NDFrame.describe of X_Minimum X_Maximum Y_Minimum Y_Maximum Pixels_Areas X_Perimeter \
0 42 50 270900 270944 267 17
1 645 651 2538079 2538108 108 10
2 829 835 1553913 1553931 71 8
3 853 860 369370 369415 176 13
4 1289 1306 498078 498335 2409 60
... ... ... ... ... ... ...
1936 249 277 325780 325796 273 54
1937 144 175 340581 340598 287 44
1938 145 174 386779 386794 292 40
1939 137 170 422497 422528 419 97
1940 1261 1281 87951 87967 103 26
Y_Perimeter Sum_of_Luminosity Minimum_of_Luminosity \
0 44 24220 76
1 30 11397 84
2 19 7972 99
3 45 18996 99
4 260 246930 37
... ... ... ...
1936 22 35033 119
1937 24 34599 112
1938 22 37572 120
1939 47 52715 117
1940 22 11682 101
Maximum_of_Luminosity ... Orientation_Index Luminosity_Index \
0 108 ... 0.8182 -0.2913
1 123 ... 0.7931 -0.1756
2 125 ... 0.6667 -0.1228
3 126 ... 0.8444 -0.1568
4 126 ... 0.9338 -0.1992
... ... ... ... ...
1936 141 ... -0.4286 0.0026
1937 133 ... -0.4516 -0.0582
1938 140 ... -0.4828 0.0052
1939 140 ... -0.0606 -0.0171
1940 133 ... -0.2000 -0.1139
SigmoidOfAreas Pastry Z_Scratch K_Scatch Stains Dirtiness Bumps \
0 0.5822 1 0 0 0 0 0
1 0.2984 1 0 0 0 0 0
2 0.2150 1 0 0 0 0 0
3 0.5212 1 0 0 0 0 0
4 1.0000 1 0 0 0 0 0
... ... ... ... ... ... ... ...
1936 0.7254 0 0 0 0 0 0
1937 0.8173 0 0 0 0 0 0
1938 0.7079 0 0 0 0 0 0
1939 0.9919 0 0 0 0 0 0
1940 0.5296 0 0 0 0 0 0
Other_Faults
0 0
1 0
2 0
3 0
4 0
... ...
1936 1
1937 1
1938 1
1939 1
1940 1
2.#Printing the Data type:
X_Minimum int64
X_Maximum int64
Y_Minimum int64
Y_Maximum int64
Pixels_Areas int64
X_Perimeter int64
Y_Perimeter int64
Sum_of_Luminosity int64
Minimum_of_Luminosity int64
Maximum_of_Luminosity int64
Length_of_Conveyer int64
TypeOfSteel_A300 int64
TypeOfSteel_A400 int64
Steel_Plate_Thickness int64
Edges_Index float64
Empty_Index float64
Square_Index float64
Outside_X_Index float64
Edges_X_Index float64
Edges_Y_Index float64
Outside_Global_Index float64
LogOfAreas float64
Log_X_Index float64
Log_Y_Index float64
Orientation_Index float64
Luminosity_Index float64
SigmoidOfAreas float64
Pastry int64
Z_Scratch int64
K_Scatch int64
Stains int64
Dirtiness int64
Bumps int64
Other_Faults int64
dtype: object
3.#Printing the Output (Y) Combined Column:
n ['Pastry' 'Z_Scratch' 'K_Scatch' 'Stains' 'Dirtiness' 'Bumps'
'Other_Faults']
n 0 Pastry
1 Pastry
2 Pastry
3 Pastry
4 Pastry
...
1936 Other_Faults
1937 Other_Faults
1938 Other_Faults
1939 Other_Faults
1940 Other_Faults
Length: 1941, dtype: object
4.#Printing the final dataset by removing the high corelation data & its count:
n X_Minimum X_Maximum Y_Minimum Y_Maximum Pixels_Areas X_Perimeter \
0 42 50 270900 270944 267 17
1 645 651 2538079 2538108 108 10
2 829 835 1553913 1553931 71 8
3 853 860 369370 369415 176 13
4 1289 1306 498078 498335 2409 60
... ... ... ... ... ... ...
1936 249 277 325780 325796 273 54
1937 144 175 340581 340598 287 44
1938 145 174 386779 386794 292 40
1939 137 170 422497 422528 419 97
1940 1261 1281 87951 87967 103 26
Y_Perimeter Sum_of_Luminosity Minimum_of_Luminosity \
0 44 24220 76
1 30 11397 84
2 19 7972 99
3 45 18996 99
4 260 246930 37
... ... ... ...
1936 22 35033 119
1937 24 34599 112
1938 22 37572 120
1939 47 52715 117
1940 22 11682 101
Maximum_of_Luminosity ... Edges_X_Index Edges_Y_Index \
0 108 ... 0.4706 1.0000
1 123 ... 0.6000 0.9667
2 125 ... 0.7500 0.9474
3 126 ... 0.5385 1.0000
4 126 ... 0.2833 0.9885
... ... ... ... ...
1936 141 ... 0.5185 0.7273
1937 133 ... 0.7046 0.7083
1938 140 ... 0.7250 0.6818
1939 140 ... 0.3402 0.6596
1940 133 ... 0.7692 0.7273
Outside_Global_Index LogOfAreas Log_X_Index Log_Y_Index \
0 1.0 2.4265 0.9031 1.6435
1 1.0 2.0334 0.7782 1.4624
2 1.0 1.8513 0.7782 1.2553
3 1.0 2.2455 0.8451 1.6532
4 1.0 3.3818 1.2305 2.4099
... ... ... ... ...
1936 0.0 2.4362 1.4472 1.2041
1937 0.0 2.4579 1.4914 1.2305
1938 0.0 2.4654 1.4624 1.1761
1939 0.0 2.6222 1.5185 1.4914
1940 0.0 2.0128 1.3010 1.2041
Orientation_Index Luminosity_Index SigmoidOfAreas target
0 0.8182 -0.2913 0.5822 Pastry
1 0.7931 -0.1756 0.2984 Pastry
2 0.6667 -0.1228 0.2150 Pastry
3 0.8444 -0.1568 0.5212 Pastry
4 0.9338 -0.1992 1.0000 Pastry
... ... ... ... ...
1936 -0.4286 0.0026 0.7254 Other_Faults
1937 -0.4516 -0.0582 0.8173 Other_Faults
1938 -0.4828 0.0052 0.7079 Other_Faults
1939 -0.0606 -0.0171 0.9919 Other_Faults
1940 -0.2000 -0.1139 0.5296 Other_Faults
[1941 rows x 28 columns]
n Other_Faults 673
Bumps 402
K_Scatch 391
Z_Scratch 190
Pastry 158
Stains 72
Dirtiness 55
Name: target, dtype: int64
5.#Printing Null value function:
X_Minimum 0
X_Maximum 0
Y_Minimum 0
Y_Maximum 0
Pixels_Areas 0
X_Perimeter 0
Y_Perimeter 0
Sum_of_Luminosity 0
Minimum_of_Luminosity 0
Maximum_of_Luminosity 0
Length_of_Conveyer 0
TypeOfSteel_A300 0
TypeOfSteel_A400 0
Steel_Plate_Thickness 0
Edges_Index 0
Empty_Index 0
Square_Index 0
Outside_X_Index 0
Edges_X_Index 0
Edges_Y_Index 0
Outside_Global_Index 0
LogOfAreas 0
Log_X_Index 0
Log_Y_Index 0
Orientation_Index 0
Luminosity_Index 0
SigmoidOfAreas 0
Pastry 0
Z_Scratch 0
K_Scatch 0
Stains 0
Dirtiness 0
Bumps 0
Other_Faults 0
dtype: int64
6.#Printing the Count function:
X_Minimum 1941
X_Maximum 1941
Y_Minimum 1941
Y_Maximum 1941
Pixels_Areas 1941
X_Perimeter 1941
Y_Perimeter 1941
Sum_of_Luminosity 1941
Minimum_of_Luminosity 1941
Maximum_of_Luminosity 1941
Length_of_Conveyer 1941
TypeOfSteel_A300 1941
TypeOfSteel_A400 1941
Steel_Plate_Thickness 1941
Edges_Index 1941
Empty_Index 1941
Square_Index 1941
Outside_X_Index 1941
Edges_X_Index 1941
Edges_Y_Index 1941
Outside_Global_Index 1941
LogOfAreas 1941
Log_X_Index 1941
Log_Y_Index 1941
Orientation_Index 1941
Luminosity_Index 1941
SigmoidOfAreas 1941
Pastry 1941
Z_Scratch 1941
K_Scatch 1941
Stains 1941
Dirtiness 1941
Bumps 1941
Other_Faults 1941
dtype: int64
7.#Count of each target classification in test set
(1941, 25)
Other_Faults 138
Bumps 82
K_Scatch 65
Z_Scratch 40
Pastry 39
Stains 18
Dirtiness 7
Name: target, dtype: int64
8.#printing the(rain_index,test_index:
[ 389 390 391 ... 1938 1939 1940] [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89
90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125
126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143
144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161
162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179
180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197
198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215
216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233
234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251
252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269
270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287
288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305
306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323
324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341
342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359
360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377
378 379 380 381 382 383 384 385 386 387 388]
[ 0 1 2 ... 1938 1939 1940] [389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406
407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424
425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442
443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460
461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478
479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514
515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532
533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550
551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568
569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586
587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604
605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622
623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658
659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676
677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694
695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712
713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730
731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748
749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766
767 768 769 770 771 772 773 774 775 776]
[ 0 1 2 ... 1938 1939 1940] [ 777 778 779 780 781 782 783 784 785 786 787 788 789 790
791 792 793 794 795 796 797 798 799 800 801 802 803 804
805 806 807 808 809 810 811 812 813 814 815 816 817 818
819 820 821 822 823 824 825 826 827 828 829 830 831 832
833 834 835 836 837 838 839 840 841 842 843 844 845 846
847 848 849 850 851 852 853 854 855 856 857 858 859 860
861 862 863 864 865 866 867 868 869 870 871 872 873 874
875 876 877 878 879 880 881 882 883 884 885 886 887 888
889 890 891 892 893 894 895 896 897 898 899 900 901 902
903 904 905 906 907 908 909 910 911 912 913 914 915 916
917 918 919 920 921 922 923 924 925 926 927 928 929 930
931 932 933 934 935 936 937 938 939 940 941 942 943 944
945 946 947 948 949 950 951 952 953 954 955 956 957 958
959 960 961 962 963 964 965 966 967 968 969 970 971 972
973 974 975 976 977 978 979 980 981 982 983 984 985 986
987 988 989 990 991 992 993 994 995 996 997 998 999 1000
1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014
1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028
1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042
1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070
1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084
1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098
1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112
1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126
1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140
1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154
1155 1156 1157 1158 1159 1160 1161 1162 1163 1164]
[ 0 1 2 ... 1938 1939 1940] [1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178
1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192
1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206
1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220
1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234
1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262
1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276
1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290
1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304
1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318
1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332
1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346
1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374
1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388
1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402
1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416
1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430
1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444
1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458
1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486
1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500
1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514
1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528
1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542
1543 1544 1545 1546 1547 1548 1549 1550 1551 1552]
[ 0 1 2 ... 1550 1551 1552] [1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566
1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580
1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594
1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608
1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622
1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636
1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650
1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678
1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692
1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706
1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720
1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734
1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748
1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762
1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790
1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804
1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818
1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832
1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846
1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860
1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874
1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902
1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916
1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930
1931 1932 1933 1934 1935 1936 1937 1938 1939 1940]
9.#Printing the Startified K-FOld Accuracy:
SVC Accuracy_Startified K-Fold = [0.41131105 0.55154639 0.41494845 0.49742268 0.45360825]
KNN Accuracy_Startified K-Fold = [0.36760925 0.45618557 0.32989691 0.34793814 0.32989691]
Random Forest Accuracy_Startified K-Fold = [0.57583548 0.6185567 0.6185567 0.73453608 0.59278351]10.#Countplot:
Question 2: What are the pros and cons of knn?
Pros:
1. No Training period: KNN is called lazy leaner algorithm(instance based learning). It does not learn anything in the training period. It does not derive any discriminative function from the training data. This makes the kNN algorithm much faster than other algorithm.
2. Since KNN algorithm requires no training before making predictions,new data can be added seamlessly which will not impact the accuracy of the algorithm.
3.KNN is easy to implement. There are only two parameters required to implement KNN ie., the value of k and the disatnce function(eg Euclidian or Manhanttan).
Cons:
1. Does not work well with large data set. In large datsets, the cost of calculating the distance between the new point & each existing points is huge which degrades the performance of the algorithm.
2. Does not work well with high dimensions: The KNN algorithm doesn't work well with high dimensional data because with large number of dimensions, it becomes difficult to the agorithm to calculate the distances in each dimensions.
3.Need feature scaling: we need to do feature scaling (standardization and normalization) before applying KNN algorithm to any dataset. if we don't do so, KNN may generate wrong predictions.
4. KNN is sensitive to noise in the dataset. we need to manually impute missing values and remove outliers.