├── README.md ├── zoo.csv ├── trainer.ipynb └── .ipynb_checkpoints └── trainer-checkpoint.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # Intel O-Level Certification Program 2 | Dataset training. 3 | Dataset from [Kaggle](https://www.kaggle.com/datasets/uciml/zoo-animal-classification). 4 | 5 | -------------------------------------------------------------------------------- /zoo.csv: -------------------------------------------------------------------------------- 1 | animal_name,hair,feathers,eggs,milk,airborne,aquatic,predator,toothed,backbone,breathes,venomous,fins,legs,tail,domestic,catsize,class_type 2 | aardvark,1,0,0,1,0,0,1,1,1,1,0,0,4,0,0,1,1 3 | antelope,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1 4 | bass,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4 5 | bear,1,0,0,1,0,0,1,1,1,1,0,0,4,0,0,1,1 6 | boar,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 7 | buffalo,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1 8 | calf,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1 9 | carp,0,0,1,0,0,1,0,1,1,0,0,1,0,1,1,0,4 10 | catfish,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4 11 | cavy,1,0,0,1,0,0,0,1,1,1,0,0,4,0,1,0,1 12 | cheetah,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 13 | chicken,0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,2 14 | chub,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4 15 | clam,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,7 16 | crab,0,0,1,0,0,1,1,0,0,0,0,0,4,0,0,0,7 17 | crayfish,0,0,1,0,0,1,1,0,0,0,0,0,6,0,0,0,7 18 | crow,0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,0,2 19 | deer,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1 20 | dogfish,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,4 21 | dolphin,0,0,0,1,0,1,1,1,1,1,0,1,0,1,0,1,1 22 | dove,0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,2 23 | duck,0,1,1,0,1,1,0,0,1,1,0,0,2,1,0,0,2 24 | elephant,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1 25 | flamingo,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,1,2 26 | flea,0,0,1,0,0,0,0,0,0,1,0,0,6,0,0,0,6 27 | frog,0,0,1,0,0,1,1,1,1,1,0,0,4,0,0,0,5 28 | frog,0,0,1,0,0,1,1,1,1,1,1,0,4,0,0,0,5 29 | fruitbat,1,0,0,1,1,0,0,1,1,1,0,0,2,1,0,0,1 30 | giraffe,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1 31 | girl,1,0,0,1,0,0,1,1,1,1,0,0,2,0,1,1,1 32 | gnat,0,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,6 33 | goat,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1 34 | gorilla,1,0,0,1,0,0,0,1,1,1,0,0,2,0,0,1,1 35 | gull,0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,2 36 | haddock,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,4 37 | hamster,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,0,1 38 | hare,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,0,1 39 | hawk,0,1,1,0,1,0,1,0,1,1,0,0,2,1,0,0,2 40 | herring,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4 41 | honeybee,1,0,1,0,1,0,0,0,0,1,1,0,6,0,1,0,6 42 | housefly,1,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,6 43 | kiwi,0,1,1,0,0,0,1,0,1,1,0,0,2,1,0,0,2 44 | ladybird,0,0,1,0,1,0,1,0,0,1,0,0,6,0,0,0,6 45 | lark,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2 46 | leopard,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 47 | lion,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 48 | lobster,0,0,1,0,0,1,1,0,0,0,0,0,6,0,0,0,7 49 | lynx,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 50 | mink,1,0,0,1,0,1,1,1,1,1,0,0,4,1,0,1,1 51 | mole,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,0,1 52 | mongoose,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 53 | moth,1,0,1,0,1,0,0,0,0,1,0,0,6,0,0,0,6 54 | newt,0,0,1,0,0,1,1,1,1,1,0,0,4,1,0,0,5 55 | octopus,0,0,1,0,0,1,1,0,0,0,0,0,8,0,0,1,7 56 | opossum,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,0,1 57 | oryx,1,0,0,1,0,0,0,1,1,1,0,0,4,1,0,1,1 58 | ostrich,0,1,1,0,0,0,0,0,1,1,0,0,2,1,0,1,2 59 | parakeet,0,1,1,0,1,0,0,0,1,1,0,0,2,1,1,0,2 60 | penguin,0,1,1,0,0,1,1,0,1,1,0,0,2,1,0,1,2 61 | pheasant,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2 62 | pike,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,4 63 | piranha,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,0,4 64 | pitviper,0,0,1,0,0,0,1,1,1,1,1,0,0,1,0,0,3 65 | platypus,1,0,1,1,0,1,1,0,1,1,0,0,4,1,0,1,1 66 | polecat,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 67 | pony,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1 68 | porpoise,0,0,0,1,0,1,1,1,1,1,0,1,0,1,0,1,1 69 | puma,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 70 | pussycat,1,0,0,1,0,0,1,1,1,1,0,0,4,1,1,1,1 71 | raccoon,1,0,0,1,0,0,1,1,1,1,0,0,4,1,0,1,1 72 | reindeer,1,0,0,1,0,0,0,1,1,1,0,0,4,1,1,1,1 73 | rhea,0,1,1,0,0,0,1,0,1,1,0,0,2,1,0,1,2 74 | scorpion,0,0,0,0,0,0,1,0,0,1,1,0,8,1,0,0,7 75 | seahorse,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,4 76 | seal,1,0,0,1,0,1,1,1,1,1,0,1,0,0,0,1,1 77 | sealion,1,0,0,1,0,1,1,1,1,1,0,1,2,1,0,1,1 78 | seasnake,0,0,0,0,0,1,1,1,1,0,1,0,0,1,0,0,3 79 | seawasp,0,0,1,0,0,1,1,0,0,0,1,0,0,0,0,0,7 80 | skimmer,0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,2 81 | skua,0,1,1,0,1,1,1,0,1,1,0,0,2,1,0,0,2 82 | slowworm,0,0,1,0,0,0,1,1,1,1,0,0,0,1,0,0,3 83 | slug,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,7 84 | sole,0,0,1,0,0,1,0,1,1,0,0,1,0,1,0,0,4 85 | sparrow,0,1,1,0,1,0,0,0,1,1,0,0,2,1,0,0,2 86 | squirrel,1,0,0,1,0,0,0,1,1,1,0,0,2,1,0,0,1 87 | starfish,0,0,1,0,0,1,1,0,0,0,0,0,5,0,0,0,7 88 | stingray,0,0,1,0,0,1,1,1,1,0,1,1,0,1,0,1,4 89 | swan,0,1,1,0,1,1,0,0,1,1,0,0,2,1,0,1,2 90 | termite,0,0,1,0,0,0,0,0,0,1,0,0,6,0,0,0,6 91 | toad,0,0,1,0,0,1,0,1,1,1,0,0,4,0,0,0,5 92 | tortoise,0,0,1,0,0,0,0,0,1,1,0,0,4,1,0,1,3 93 | tuatara,0,0,1,0,0,0,1,1,1,1,0,0,4,1,0,0,3 94 | tuna,0,0,1,0,0,1,1,1,1,0,0,1,0,1,0,1,4 95 | 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animal_namehairfeatherseggsmilkairborneaquaticpredatortoothedbackbonebreathesvenomousfinslegstaildomesticcatsizeclass_type
0aardvark10010011110040011
1antelope10010001110041011
2bass00100111100101004
3bear10010011110040011
4boar10010011110041011
.........................................................
96wallaby10010001110021011
97wasp10101000011060006
98wolf10010011110041011
99worm00100000010000007
100wren01101000110021002
\n", 349 | "

101 rows × 18 columns

\n", 350 | "
" 351 | ], 352 | "text/plain": [ 353 | " animal_name hair feathers eggs milk airborne aquatic predator \\\n", 354 | "0 aardvark 1 0 0 1 0 0 1 \n", 355 | "1 antelope 1 0 0 1 0 0 0 \n", 356 | "2 bass 0 0 1 0 0 1 1 \n", 357 | "3 bear 1 0 0 1 0 0 1 \n", 358 | "4 boar 1 0 0 1 0 0 1 \n", 359 | ".. ... ... ... ... ... ... ... ... \n", 360 | "96 wallaby 1 0 0 1 0 0 0 \n", 361 | "97 wasp 1 0 1 0 1 0 0 \n", 362 | "98 wolf 1 0 0 1 0 0 1 \n", 363 | "99 worm 0 0 1 0 0 0 0 \n", 364 | "100 wren 0 1 1 0 1 0 0 \n", 365 | "\n", 366 | " toothed backbone breathes venomous fins legs tail domestic \\\n", 367 | "0 1 1 1 0 0 4 0 0 \n", 368 | "1 1 1 1 0 0 4 1 0 \n", 369 | "2 1 1 0 0 1 0 1 0 \n", 370 | "3 1 1 1 0 0 4 0 0 \n", 371 | "4 1 1 1 0 0 4 1 0 \n", 372 | ".. ... ... ... ... ... ... ... ... \n", 373 | "96 1 1 1 0 0 2 1 0 \n", 374 | "97 0 0 1 1 0 6 0 0 \n", 375 | "98 1 1 1 0 0 4 1 0 \n", 376 | "99 0 0 1 0 0 0 0 0 \n", 377 | "100 0 1 1 0 0 2 1 0 \n", 378 | "\n", 379 | " catsize class_type \n", 380 | "0 1 1 \n", 381 | "1 1 1 \n", 382 | "2 0 4 \n", 383 | "3 1 1 \n", 384 | "4 1 1 \n", 385 | ".. ... ... \n", 386 | "96 1 1 \n", 387 | "97 0 6 \n", 388 | "98 1 1 \n", 389 | "99 0 7 \n", 390 | "100 0 2 \n", 391 | "\n", 392 | "[101 rows x 18 columns]" 393 | ] 394 | }, 395 | "execution_count": 44, 396 | "metadata": {}, 397 | "output_type": "execute_result" 398 | }, 399 | { 400 | "data": { 401 | "application/javascript": [ 402 | "\n", 403 | " setTimeout(function() {\n", 404 | " var nbb_cell_id = 44;\n", 405 | " var nbb_unformatted_code = \"import pandas as pd\\n\\ndata = pd.read_csv(\\\"./zoo.csv\\\")\\ndata\";\n", 406 | " var nbb_formatted_code = \"import pandas as pd\\n\\ndata = pd.read_csv(\\\"./zoo.csv\\\")\\ndata\";\n", 407 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 408 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 409 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 410 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 411 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 412 | " }\n", 413 | " break;\n", 414 | " }\n", 415 | " }\n", 416 | " }, 500);\n", 417 | " " 418 | ], 419 | "text/plain": [ 420 | "" 421 | ] 422 | }, 423 | "metadata": {}, 424 | "output_type": "display_data" 425 | }, 426 | { 427 | "data": { 428 | "application/javascript": [ 429 | "\n", 430 | " setTimeout(function() {\n", 431 | " var nbb_cell_id = 44;\n", 432 | " var nbb_unformatted_code = \"import pandas as pd\\n\\ndata = pd.read_csv(\\\"./zoo.csv\\\")\\ndata\";\n", 433 | " var nbb_formatted_code = \"import pandas as pd\\n\\ndata = pd.read_csv(\\\"./zoo.csv\\\")\\ndata\";\n", 434 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 435 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 436 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 437 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 438 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 439 | " }\n", 440 | " break;\n", 441 | " }\n", 442 | " }\n", 443 | " }, 500);\n", 444 | " " 445 | ], 446 | "text/plain": [ 447 | "" 448 | ] 449 | }, 450 | "metadata": {}, 451 | "output_type": "display_data" 452 | } 453 | ], 454 | "source": [ 455 | "import pandas as pd\n", 456 | "\n", 457 | "data = pd.read_csv(\"./zoo.csv\")\n", 458 | "data" 459 | ] 460 | }, 461 | { 462 | "cell_type": "code", 463 | "execution_count": 45, 464 | "id": "1403a11d", 465 | "metadata": {}, 466 | "outputs": [ 467 | { 468 | "data": { 469 | "text/html": [ 470 | "
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hairfeatherseggsmilkairborneaquaticpredatortoothedbackbonebreathesvenomousfinstaildomesticcatsize
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96100100011100101
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\n", 706 | "

101 rows × 15 columns

\n", 707 | "
" 708 | ], 709 | "text/plain": [ 710 | " hair feathers eggs milk airborne aquatic predator toothed \\\n", 711 | "0 1 0 0 1 0 0 1 1 \n", 712 | "1 1 0 0 1 0 0 0 1 \n", 713 | "2 0 0 1 0 0 1 1 1 \n", 714 | "3 1 0 0 1 0 0 1 1 \n", 715 | "4 1 0 0 1 0 0 1 1 \n", 716 | ".. ... ... ... ... ... ... ... ... \n", 717 | "96 1 0 0 1 0 0 0 1 \n", 718 | "97 1 0 1 0 1 0 0 0 \n", 719 | "98 1 0 0 1 0 0 1 1 \n", 720 | "99 0 0 1 0 0 0 0 0 \n", 721 | "100 0 1 1 0 1 0 0 0 \n", 722 | "\n", 723 | " backbone breathes venomous fins tail domestic catsize \n", 724 | "0 1 1 0 0 0 0 1 \n", 725 | "1 1 1 0 0 1 0 1 \n", 726 | "2 1 0 0 1 1 0 0 \n", 727 | "3 1 1 0 0 0 0 1 \n", 728 | "4 1 1 0 0 1 0 1 \n", 729 | ".. ... ... ... ... ... ... ... \n", 730 | "96 1 1 0 0 1 0 1 \n", 731 | "97 0 1 1 0 0 0 0 \n", 732 | "98 1 1 0 0 1 0 1 \n", 733 | "99 0 1 0 0 0 0 0 \n", 734 | "100 1 1 0 0 1 0 0 \n", 735 | "\n", 736 | "[101 rows x 15 columns]" 737 | ] 738 | }, 739 | "execution_count": 45, 740 | "metadata": {}, 741 | "output_type": "execute_result" 742 | }, 743 | { 744 | "data": { 745 | "application/javascript": [ 746 | "\n", 747 | " setTimeout(function() {\n", 748 | " var nbb_cell_id = 45;\n", 749 | " var nbb_unformatted_code = \"data.drop([\\\"animal_name\\\", \\\"legs\\\", \\\"class_type\\\"], inplace=True, axis=1)\\ndata\";\n", 750 | " var nbb_formatted_code = \"data.drop([\\\"animal_name\\\", \\\"legs\\\", \\\"class_type\\\"], inplace=True, axis=1)\\ndata\";\n", 751 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 752 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 753 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 754 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 755 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 756 | " }\n", 757 | " break;\n", 758 | " }\n", 759 | " }\n", 760 | " }, 500);\n", 761 | " " 762 | ], 763 | "text/plain": [ 764 | "" 765 | ] 766 | }, 767 | "metadata": {}, 768 | "output_type": "display_data" 769 | }, 770 | { 771 | "data": { 772 | "application/javascript": [ 773 | "\n", 774 | " setTimeout(function() {\n", 775 | " var nbb_cell_id = 45;\n", 776 | " var nbb_unformatted_code = \"data.drop([\\\"animal_name\\\", \\\"legs\\\", \\\"class_type\\\"], inplace=True, axis=1)\\ndata\";\n", 777 | " var nbb_formatted_code = \"data.drop([\\\"animal_name\\\", \\\"legs\\\", \\\"class_type\\\"], inplace=True, axis=1)\\ndata\";\n", 778 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 779 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 780 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 781 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 782 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 783 | " }\n", 784 | " break;\n", 785 | " }\n", 786 | " }\n", 787 | " }, 500);\n", 788 | " " 789 | ], 790 | "text/plain": [ 791 | "" 792 | ] 793 | }, 794 | "metadata": {}, 795 | "output_type": "display_data" 796 | } 797 | ], 798 | "source": [ 799 | "data.drop([\"animal_name\", \"legs\", \"class_type\"], inplace=True, axis=1)\n", 800 | "data" 801 | ] 802 | }, 803 | { 804 | "cell_type": "code", 805 | "execution_count": 46, 806 | "id": "0f567730", 807 | "metadata": {}, 808 | "outputs": [ 809 | { 810 | "data": { 811 | "application/javascript": [ 812 | "\n", 813 | " setTimeout(function() {\n", 814 | " var nbb_cell_id = 46;\n", 815 | " var nbb_unformatted_code = \"def accuracy(real, predict):\\n return sum(y_data == y_pred) / float(real.shape[0])\";\n", 816 | " var nbb_formatted_code = \"def accuracy(real, predict):\\n return sum(y_data == y_pred) / float(real.shape[0])\";\n", 817 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 818 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 819 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 820 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 821 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 822 | " }\n", 823 | " break;\n", 824 | " }\n", 825 | " }\n", 826 | " }, 500);\n", 827 | " " 828 | ], 829 | "text/plain": [ 830 | "" 831 | ] 832 | }, 833 | "metadata": {}, 834 | "output_type": "display_data" 835 | }, 836 | { 837 | "data": { 838 | "application/javascript": [ 839 | "\n", 840 | " setTimeout(function() {\n", 841 | " var nbb_cell_id = 46;\n", 842 | " var nbb_unformatted_code = \"def accuracy(real, predict):\\n return sum(y_data == y_pred) / float(real.shape[0])\";\n", 843 | " var nbb_formatted_code = \"def accuracy(real, predict):\\n return sum(y_data == y_pred) / float(real.shape[0])\";\n", 844 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 845 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 846 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 847 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 848 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 849 | " }\n", 850 | " break;\n", 851 | " }\n", 852 | " }\n", 853 | " }, 500);\n", 854 | " " 855 | ], 856 | "text/plain": [ 857 | "" 858 | ] 859 | }, 860 | "metadata": {}, 861 | "output_type": "display_data" 862 | } 863 | ], 864 | "source": [ 865 | "def accuracy(real, predict):\n", 866 | " return sum(y_data == y_pred) / float(real.shape[0])" 867 | ] 868 | }, 869 | { 870 | "cell_type": "code", 871 | "execution_count": 47, 872 | "id": "e09bc7bf", 873 | "metadata": {}, 874 | "outputs": [ 875 | { 876 | "data": { 877 | "application/javascript": [ 878 | "\n", 879 | " setTimeout(function() {\n", 880 | " var nbb_cell_id = 47;\n", 881 | " var nbb_unformatted_code = \"target = \\\"feathers\\\"\\nx_cols = [x for x in data.columns if x != target]\\nX_data = data[x_cols]\\ny_data = data[target]\";\n", 882 | " var nbb_formatted_code = \"target = \\\"feathers\\\"\\nx_cols = [x for x in data.columns if x != target]\\nX_data = data[x_cols]\\ny_data = data[target]\";\n", 883 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 884 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 885 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 886 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 887 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 888 | " }\n", 889 | " break;\n", 890 | " }\n", 891 | " }\n", 892 | " }, 500);\n", 893 | " " 894 | ], 895 | "text/plain": [ 896 | "" 897 | ] 898 | }, 899 | "metadata": {}, 900 | "output_type": "display_data" 901 | }, 902 | { 903 | "data": { 904 | "application/javascript": [ 905 | "\n", 906 | " setTimeout(function() {\n", 907 | " var nbb_cell_id = 47;\n", 908 | " var nbb_unformatted_code = \"target = \\\"feathers\\\"\\nx_cols = [x for x in data.columns if x != target]\\nX_data = data[x_cols]\\ny_data = data[target]\";\n", 909 | " var nbb_formatted_code = \"target = \\\"feathers\\\"\\nx_cols = [x for x in data.columns if x != target]\\nX_data = data[x_cols]\\ny_data = data[target]\";\n", 910 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 911 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 912 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 913 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 914 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 915 | " }\n", 916 | " break;\n", 917 | " }\n", 918 | " }\n", 919 | " }, 500);\n", 920 | " " 921 | ], 922 | "text/plain": [ 923 | "" 924 | ] 925 | }, 926 | "metadata": {}, 927 | "output_type": "display_data" 928 | } 929 | ], 930 | "source": [ 931 | "target = \"feathers\"\n", 932 | "x_cols = [x for x in data.columns if x != target]\n", 933 | "X_data = data[x_cols]\n", 934 | "y_data = data[target]" 935 | ] 936 | }, 937 | { 938 | "cell_type": "code", 939 | "execution_count": 54, 940 | "id": "885e5fd4", 941 | "metadata": {}, 942 | "outputs": [ 943 | { 944 | "name": "stdout", 945 | "output_type": "stream", 946 | "text": [ 947 | "Accuracy of target having feathers is 99.00990099009901%\n" 948 | ] 949 | }, 950 | { 951 | "data": { 952 | "application/javascript": [ 953 | "\n", 954 | " setTimeout(function() {\n", 955 | " var nbb_cell_id = 54;\n", 956 | " var nbb_unformatted_code = \"from sklearn.neighbors import KNeighborsClassifier\\n\\nknn = KNeighborsClassifier(n_neighbors=3)\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nknn = KNeighborsClassifier(n_neighbors=5, weights=\\\"distance\\\")\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nprint(f\\\"Accuracy of target having {target} is {(accuracy(y_data, y_pred))*100}%\\\")\";\n", 957 | " var nbb_formatted_code = \"from sklearn.neighbors import KNeighborsClassifier\\n\\nknn = KNeighborsClassifier(n_neighbors=3)\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nknn = KNeighborsClassifier(n_neighbors=5, weights=\\\"distance\\\")\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nprint(f\\\"Accuracy of target having {target} is {(accuracy(y_data, y_pred))*100}%\\\")\";\n", 958 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 959 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 960 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 961 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 962 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 963 | " }\n", 964 | " break;\n", 965 | " }\n", 966 | " }\n", 967 | " }, 500);\n", 968 | " " 969 | ], 970 | "text/plain": [ 971 | "" 972 | ] 973 | }, 974 | "metadata": {}, 975 | "output_type": "display_data" 976 | }, 977 | { 978 | "data": { 979 | "application/javascript": [ 980 | "\n", 981 | " setTimeout(function() {\n", 982 | " var nbb_cell_id = 54;\n", 983 | " var nbb_unformatted_code = \"from sklearn.neighbors import KNeighborsClassifier\\n\\nknn = KNeighborsClassifier(n_neighbors=3)\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nknn = KNeighborsClassifier(n_neighbors=5, weights=\\\"distance\\\")\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nprint(f\\\"Accuracy of target having {target} is {(accuracy(y_data, y_pred))*100}%\\\")\";\n", 984 | " var nbb_formatted_code = \"from sklearn.neighbors import KNeighborsClassifier\\n\\nknn = KNeighborsClassifier(n_neighbors=3)\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nknn = KNeighborsClassifier(n_neighbors=5, weights=\\\"distance\\\")\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nprint(f\\\"Accuracy of target having {target} is {(accuracy(y_data, y_pred))*100}%\\\")\";\n", 985 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 986 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 987 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 988 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 989 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 990 | " }\n", 991 | " break;\n", 992 | " }\n", 993 | " }\n", 994 | " }, 500);\n", 995 | " " 996 | ], 997 | "text/plain": [ 998 | "" 999 | ] 1000 | }, 1001 | "metadata": {}, 1002 | "output_type": "display_data" 1003 | } 1004 | ], 1005 | "source": [ 1006 | "from sklearn.neighbors import KNeighborsClassifier\n", 1007 | "\n", 1008 | "knn = KNeighborsClassifier(n_neighbors=3)\n", 1009 | "\n", 1010 | "knn = knn.fit(X_data, y_data)\n", 1011 | "\n", 1012 | "y_pred = knn.predict(X_data)\n", 1013 | "\n", 1014 | "knn = KNeighborsClassifier(n_neighbors=5, weights=\"distance\")\n", 1015 | "\n", 1016 | "knn = knn.fit(X_data, y_data)\n", 1017 | "\n", 1018 | "y_pred = knn.predict(X_data)\n", 1019 | "\n", 1020 | "print(f\"Accuracy of target having {target} is {(accuracy(y_data, y_pred))*100}%\")" 1021 | ] 1022 | } 1023 | ], 1024 | "metadata": { 1025 | "kernelspec": { 1026 | "display_name": "Python 3 (ipykernel)", 1027 | "language": "python", 1028 | "name": "python3" 1029 | }, 1030 | "language_info": { 1031 | "codemirror_mode": { 1032 | "name": "ipython", 1033 | "version": 3 1034 | }, 1035 | "file_extension": ".py", 1036 | "mimetype": "text/x-python", 1037 | "name": "python", 1038 | "nbconvert_exporter": "python", 1039 | "pygments_lexer": "ipython3", 1040 | "version": "3.9.16" 1041 | } 1042 | }, 1043 | "nbformat": 4, 1044 | "nbformat_minor": 5 1045 | } 1046 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/trainer-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 42, 6 | "id": "80805b5e", 7 | "metadata": {}, 8 | "outputs": [ 9 | { 10 | "data": { 11 | "application/javascript": [ 12 | "\n", 13 | " setTimeout(function() {\n", 14 | " var nbb_cell_id = 42;\n", 15 | " var nbb_unformatted_code = \"%reload_ext nb_black\";\n", 16 | " var nbb_formatted_code = \"%reload_ext nb_black\";\n", 17 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 18 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 19 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 20 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 21 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 22 | " }\n", 23 | " break;\n", 24 | " }\n", 25 | " }\n", 26 | " }, 500);\n", 27 | " " 28 | ], 29 | "text/plain": [ 30 | "" 31 | ] 32 | }, 33 | "metadata": {}, 34 | "output_type": "display_data" 35 | }, 36 | { 37 | "data": { 38 | "application/javascript": [ 39 | "\n", 40 | " setTimeout(function() {\n", 41 | " var nbb_cell_id = 42;\n", 42 | " var nbb_unformatted_code = \"%reload_ext nb_black\";\n", 43 | " var nbb_formatted_code = \"%reload_ext nb_black\";\n", 44 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 45 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 46 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 47 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 48 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 49 | " }\n", 50 | " break;\n", 51 | " }\n", 52 | " }\n", 53 | " }, 500);\n", 54 | " " 55 | ], 56 | "text/plain": [ 57 | "" 58 | ] 59 | }, 60 | "metadata": {}, 61 | "output_type": "display_data" 62 | } 63 | ], 64 | "source": [ 65 | "%load_ext nb_black" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 44, 71 | "id": "79daf9c8", 72 | "metadata": {}, 73 | "outputs": [ 74 | { 75 | "data": { 76 | "text/html": [ 77 | "
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animal_namehairfeatherseggsmilkairborneaquaticpredatortoothedbackbonebreathesvenomousfinslegstaildomesticcatsizeclass_type
0aardvark10010011110040011
1antelope10010001110041011
2bass00100111100101004
3bear10010011110040011
4boar10010011110041011
.........................................................
96wallaby10010001110021011
97wasp10101000011060006
98wolf10010011110041011
99worm00100000010000007
100wren01101000110021002
\n", 349 | "

101 rows × 18 columns

\n", 350 | "
" 351 | ], 352 | "text/plain": [ 353 | " animal_name hair feathers eggs milk airborne aquatic predator \\\n", 354 | "0 aardvark 1 0 0 1 0 0 1 \n", 355 | "1 antelope 1 0 0 1 0 0 0 \n", 356 | "2 bass 0 0 1 0 0 1 1 \n", 357 | "3 bear 1 0 0 1 0 0 1 \n", 358 | "4 boar 1 0 0 1 0 0 1 \n", 359 | ".. ... ... ... ... ... ... ... ... \n", 360 | "96 wallaby 1 0 0 1 0 0 0 \n", 361 | "97 wasp 1 0 1 0 1 0 0 \n", 362 | "98 wolf 1 0 0 1 0 0 1 \n", 363 | "99 worm 0 0 1 0 0 0 0 \n", 364 | "100 wren 0 1 1 0 1 0 0 \n", 365 | "\n", 366 | " toothed backbone breathes venomous fins legs tail domestic \\\n", 367 | "0 1 1 1 0 0 4 0 0 \n", 368 | "1 1 1 1 0 0 4 1 0 \n", 369 | "2 1 1 0 0 1 0 1 0 \n", 370 | "3 1 1 1 0 0 4 0 0 \n", 371 | "4 1 1 1 0 0 4 1 0 \n", 372 | ".. ... ... ... ... ... ... ... ... \n", 373 | "96 1 1 1 0 0 2 1 0 \n", 374 | "97 0 0 1 1 0 6 0 0 \n", 375 | "98 1 1 1 0 0 4 1 0 \n", 376 | "99 0 0 1 0 0 0 0 0 \n", 377 | "100 0 1 1 0 0 2 1 0 \n", 378 | "\n", 379 | " catsize class_type \n", 380 | "0 1 1 \n", 381 | "1 1 1 \n", 382 | "2 0 4 \n", 383 | "3 1 1 \n", 384 | "4 1 1 \n", 385 | ".. ... ... \n", 386 | "96 1 1 \n", 387 | "97 0 6 \n", 388 | "98 1 1 \n", 389 | "99 0 7 \n", 390 | "100 0 2 \n", 391 | "\n", 392 | "[101 rows x 18 columns]" 393 | ] 394 | }, 395 | "execution_count": 44, 396 | "metadata": {}, 397 | "output_type": "execute_result" 398 | }, 399 | { 400 | "data": { 401 | "application/javascript": [ 402 | "\n", 403 | " setTimeout(function() {\n", 404 | " var nbb_cell_id = 44;\n", 405 | " var nbb_unformatted_code = \"import pandas as pd\\n\\ndata = pd.read_csv(\\\"./zoo.csv\\\")\\ndata\";\n", 406 | " var nbb_formatted_code = \"import pandas as pd\\n\\ndata = pd.read_csv(\\\"./zoo.csv\\\")\\ndata\";\n", 407 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 408 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 409 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 410 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 411 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 412 | " }\n", 413 | " break;\n", 414 | " }\n", 415 | " }\n", 416 | " }, 500);\n", 417 | " " 418 | ], 419 | "text/plain": [ 420 | "" 421 | ] 422 | }, 423 | "metadata": {}, 424 | "output_type": "display_data" 425 | }, 426 | { 427 | "data": { 428 | "application/javascript": [ 429 | "\n", 430 | " setTimeout(function() {\n", 431 | " var nbb_cell_id = 44;\n", 432 | " var nbb_unformatted_code = \"import pandas as pd\\n\\ndata = pd.read_csv(\\\"./zoo.csv\\\")\\ndata\";\n", 433 | " var nbb_formatted_code = \"import pandas as pd\\n\\ndata = pd.read_csv(\\\"./zoo.csv\\\")\\ndata\";\n", 434 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 435 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 436 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 437 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 438 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 439 | " }\n", 440 | " break;\n", 441 | " }\n", 442 | " }\n", 443 | " }, 500);\n", 444 | " " 445 | ], 446 | "text/plain": [ 447 | "" 448 | ] 449 | }, 450 | "metadata": {}, 451 | "output_type": "display_data" 452 | } 453 | ], 454 | "source": [ 455 | "import pandas as pd\n", 456 | "\n", 457 | "data = pd.read_csv(\"./zoo.csv\")\n", 458 | "data" 459 | ] 460 | }, 461 | { 462 | "cell_type": "code", 463 | "execution_count": 45, 464 | "id": "46233a00", 465 | "metadata": {}, 466 | "outputs": [ 467 | { 468 | "data": { 469 | "text/html": [ 470 | "
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hairfeatherseggsmilkairborneaquaticpredatortoothedbackbonebreathesvenomousfinstaildomesticcatsize
0100100111100001
1100100011100101
2001001111001100
3100100111100001
4100100111100101
................................................
96100100011100101
97101010000110000
98100100111100101
99001000000100000
100011010001100100
\n", 706 | "

101 rows × 15 columns

\n", 707 | "
" 708 | ], 709 | "text/plain": [ 710 | " hair feathers eggs milk airborne aquatic predator toothed \\\n", 711 | "0 1 0 0 1 0 0 1 1 \n", 712 | "1 1 0 0 1 0 0 0 1 \n", 713 | "2 0 0 1 0 0 1 1 1 \n", 714 | "3 1 0 0 1 0 0 1 1 \n", 715 | "4 1 0 0 1 0 0 1 1 \n", 716 | ".. ... ... ... ... ... ... ... ... \n", 717 | "96 1 0 0 1 0 0 0 1 \n", 718 | "97 1 0 1 0 1 0 0 0 \n", 719 | "98 1 0 0 1 0 0 1 1 \n", 720 | "99 0 0 1 0 0 0 0 0 \n", 721 | "100 0 1 1 0 1 0 0 0 \n", 722 | "\n", 723 | " backbone breathes venomous fins tail domestic catsize \n", 724 | "0 1 1 0 0 0 0 1 \n", 725 | "1 1 1 0 0 1 0 1 \n", 726 | "2 1 0 0 1 1 0 0 \n", 727 | "3 1 1 0 0 0 0 1 \n", 728 | "4 1 1 0 0 1 0 1 \n", 729 | ".. ... ... ... ... ... ... ... \n", 730 | "96 1 1 0 0 1 0 1 \n", 731 | "97 0 1 1 0 0 0 0 \n", 732 | "98 1 1 0 0 1 0 1 \n", 733 | "99 0 1 0 0 0 0 0 \n", 734 | "100 1 1 0 0 1 0 0 \n", 735 | "\n", 736 | "[101 rows x 15 columns]" 737 | ] 738 | }, 739 | "execution_count": 45, 740 | "metadata": {}, 741 | "output_type": "execute_result" 742 | }, 743 | { 744 | "data": { 745 | "application/javascript": [ 746 | "\n", 747 | " setTimeout(function() {\n", 748 | " var nbb_cell_id = 45;\n", 749 | " var nbb_unformatted_code = \"data.drop([\\\"animal_name\\\", \\\"legs\\\", \\\"class_type\\\"], inplace=True, axis=1)\\ndata\";\n", 750 | " var nbb_formatted_code = \"data.drop([\\\"animal_name\\\", \\\"legs\\\", \\\"class_type\\\"], inplace=True, axis=1)\\ndata\";\n", 751 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 752 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 753 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 754 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 755 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 756 | " }\n", 757 | " break;\n", 758 | " }\n", 759 | " }\n", 760 | " }, 500);\n", 761 | " " 762 | ], 763 | "text/plain": [ 764 | "" 765 | ] 766 | }, 767 | "metadata": {}, 768 | "output_type": "display_data" 769 | }, 770 | { 771 | "data": { 772 | "application/javascript": [ 773 | "\n", 774 | " setTimeout(function() {\n", 775 | " var nbb_cell_id = 45;\n", 776 | " var nbb_unformatted_code = \"data.drop([\\\"animal_name\\\", \\\"legs\\\", \\\"class_type\\\"], inplace=True, axis=1)\\ndata\";\n", 777 | " var nbb_formatted_code = \"data.drop([\\\"animal_name\\\", \\\"legs\\\", \\\"class_type\\\"], inplace=True, axis=1)\\ndata\";\n", 778 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 779 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 780 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 781 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 782 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 783 | " }\n", 784 | " break;\n", 785 | " }\n", 786 | " }\n", 787 | " }, 500);\n", 788 | " " 789 | ], 790 | "text/plain": [ 791 | "" 792 | ] 793 | }, 794 | "metadata": {}, 795 | "output_type": "display_data" 796 | } 797 | ], 798 | "source": [ 799 | "data.drop([\"animal_name\", \"legs\", \"class_type\"], inplace=True, axis=1)\n", 800 | "data" 801 | ] 802 | }, 803 | { 804 | "cell_type": "code", 805 | "execution_count": 46, 806 | "id": "aa08c4e3", 807 | "metadata": {}, 808 | "outputs": [ 809 | { 810 | "data": { 811 | "application/javascript": [ 812 | "\n", 813 | " setTimeout(function() {\n", 814 | " var nbb_cell_id = 46;\n", 815 | " var nbb_unformatted_code = \"def accuracy(real, predict):\\n return sum(y_data == y_pred) / float(real.shape[0])\";\n", 816 | " var nbb_formatted_code = \"def accuracy(real, predict):\\n return sum(y_data == y_pred) / float(real.shape[0])\";\n", 817 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 818 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 819 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 820 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 821 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 822 | " }\n", 823 | " break;\n", 824 | " }\n", 825 | " }\n", 826 | " }, 500);\n", 827 | " " 828 | ], 829 | "text/plain": [ 830 | "" 831 | ] 832 | }, 833 | "metadata": {}, 834 | "output_type": "display_data" 835 | }, 836 | { 837 | "data": { 838 | "application/javascript": [ 839 | "\n", 840 | " setTimeout(function() {\n", 841 | " var nbb_cell_id = 46;\n", 842 | " var nbb_unformatted_code = \"def accuracy(real, predict):\\n return sum(y_data == y_pred) / float(real.shape[0])\";\n", 843 | " var nbb_formatted_code = \"def accuracy(real, predict):\\n return sum(y_data == y_pred) / float(real.shape[0])\";\n", 844 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 845 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 846 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 847 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 848 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 849 | " }\n", 850 | " break;\n", 851 | " }\n", 852 | " }\n", 853 | " }, 500);\n", 854 | " " 855 | ], 856 | "text/plain": [ 857 | "" 858 | ] 859 | }, 860 | "metadata": {}, 861 | "output_type": "display_data" 862 | } 863 | ], 864 | "source": [ 865 | "def accuracy(real, predict):\n", 866 | " return sum(y_data == y_pred) / float(real.shape[0])" 867 | ] 868 | }, 869 | { 870 | "cell_type": "code", 871 | "execution_count": 47, 872 | "id": "fe3267fb", 873 | "metadata": {}, 874 | "outputs": [ 875 | { 876 | "data": { 877 | "application/javascript": [ 878 | "\n", 879 | " setTimeout(function() {\n", 880 | " var nbb_cell_id = 47;\n", 881 | " var nbb_unformatted_code = \"target = \\\"feathers\\\"\\nx_cols = [x for x in data.columns if x != target]\\nX_data = data[x_cols]\\ny_data = data[target]\";\n", 882 | " var nbb_formatted_code = \"target = \\\"feathers\\\"\\nx_cols = [x for x in data.columns if x != target]\\nX_data = data[x_cols]\\ny_data = data[target]\";\n", 883 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 884 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 885 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 886 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 887 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 888 | " }\n", 889 | " break;\n", 890 | " }\n", 891 | " }\n", 892 | " }, 500);\n", 893 | " " 894 | ], 895 | "text/plain": [ 896 | "" 897 | ] 898 | }, 899 | "metadata": {}, 900 | "output_type": "display_data" 901 | }, 902 | { 903 | "data": { 904 | "application/javascript": [ 905 | "\n", 906 | " setTimeout(function() {\n", 907 | " var nbb_cell_id = 47;\n", 908 | " var nbb_unformatted_code = \"target = \\\"feathers\\\"\\nx_cols = [x for x in data.columns if x != target]\\nX_data = data[x_cols]\\ny_data = data[target]\";\n", 909 | " var nbb_formatted_code = \"target = \\\"feathers\\\"\\nx_cols = [x for x in data.columns if x != target]\\nX_data = data[x_cols]\\ny_data = data[target]\";\n", 910 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 911 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 912 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 913 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 914 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 915 | " }\n", 916 | " break;\n", 917 | " }\n", 918 | " }\n", 919 | " }, 500);\n", 920 | " " 921 | ], 922 | "text/plain": [ 923 | "" 924 | ] 925 | }, 926 | "metadata": {}, 927 | "output_type": "display_data" 928 | } 929 | ], 930 | "source": [ 931 | "target = \"feathers\"\n", 932 | "x_cols = [x for x in data.columns if x != target]\n", 933 | "X_data = data[x_cols]\n", 934 | "y_data = data[target]" 935 | ] 936 | }, 937 | { 938 | "cell_type": "code", 939 | "execution_count": 54, 940 | "id": "73627bd3", 941 | "metadata": {}, 942 | "outputs": [ 943 | { 944 | "name": "stdout", 945 | "output_type": "stream", 946 | "text": [ 947 | "Accuracy of target having feathers is 99.00990099009901%\n" 948 | ] 949 | }, 950 | { 951 | "data": { 952 | "application/javascript": [ 953 | "\n", 954 | " setTimeout(function() {\n", 955 | " var nbb_cell_id = 54;\n", 956 | " var nbb_unformatted_code = \"from sklearn.neighbors import KNeighborsClassifier\\n\\nknn = KNeighborsClassifier(n_neighbors=3)\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nknn = KNeighborsClassifier(n_neighbors=5, weights=\\\"distance\\\")\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nprint(f\\\"Accuracy of target having {target} is {(accuracy(y_data, y_pred))*100}%\\\")\";\n", 957 | " var nbb_formatted_code = \"from sklearn.neighbors import KNeighborsClassifier\\n\\nknn = KNeighborsClassifier(n_neighbors=3)\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nknn = KNeighborsClassifier(n_neighbors=5, weights=\\\"distance\\\")\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nprint(f\\\"Accuracy of target having {target} is {(accuracy(y_data, y_pred))*100}%\\\")\";\n", 958 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 959 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 960 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 961 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 962 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 963 | " }\n", 964 | " break;\n", 965 | " }\n", 966 | " }\n", 967 | " }, 500);\n", 968 | " " 969 | ], 970 | "text/plain": [ 971 | "" 972 | ] 973 | }, 974 | "metadata": {}, 975 | "output_type": "display_data" 976 | }, 977 | { 978 | "data": { 979 | "application/javascript": [ 980 | "\n", 981 | " setTimeout(function() {\n", 982 | " var nbb_cell_id = 54;\n", 983 | " var nbb_unformatted_code = \"from sklearn.neighbors import KNeighborsClassifier\\n\\nknn = KNeighborsClassifier(n_neighbors=3)\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nknn = KNeighborsClassifier(n_neighbors=5, weights=\\\"distance\\\")\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nprint(f\\\"Accuracy of target having {target} is {(accuracy(y_data, y_pred))*100}%\\\")\";\n", 984 | " var nbb_formatted_code = \"from sklearn.neighbors import KNeighborsClassifier\\n\\nknn = KNeighborsClassifier(n_neighbors=3)\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nknn = KNeighborsClassifier(n_neighbors=5, weights=\\\"distance\\\")\\n\\nknn = knn.fit(X_data, y_data)\\n\\ny_pred = knn.predict(X_data)\\n\\nprint(f\\\"Accuracy of target having {target} is {(accuracy(y_data, y_pred))*100}%\\\")\";\n", 985 | " var nbb_cells = Jupyter.notebook.get_cells();\n", 986 | " for (var i = 0; i < nbb_cells.length; ++i) {\n", 987 | " if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n", 988 | " if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n", 989 | " nbb_cells[i].set_text(nbb_formatted_code);\n", 990 | " }\n", 991 | " break;\n", 992 | " }\n", 993 | " }\n", 994 | " }, 500);\n", 995 | " " 996 | ], 997 | "text/plain": [ 998 | "" 999 | ] 1000 | }, 1001 | "metadata": {}, 1002 | "output_type": "display_data" 1003 | } 1004 | ], 1005 | "source": [ 1006 | "from sklearn.neighbors import KNeighborsClassifier\n", 1007 | "\n", 1008 | "knn = KNeighborsClassifier(n_neighbors=3)\n", 1009 | "\n", 1010 | "knn = knn.fit(X_data, y_data)\n", 1011 | "\n", 1012 | "y_pred = knn.predict(X_data)\n", 1013 | "\n", 1014 | "knn = KNeighborsClassifier(n_neighbors=5, weights=\"distance\")\n", 1015 | "\n", 1016 | "knn = knn.fit(X_data, y_data)\n", 1017 | "\n", 1018 | "y_pred = knn.predict(X_data)\n", 1019 | "\n", 1020 | "print(f\"Accuracy of target having {target} is {(accuracy(y_data, y_pred))*100}%\")" 1021 | ] 1022 | } 1023 | ], 1024 | "metadata": { 1025 | "kernelspec": { 1026 | "display_name": "Python 3 (ipykernel)", 1027 | "language": "python", 1028 | "name": "python3" 1029 | }, 1030 | "language_info": { 1031 | "codemirror_mode": { 1032 | "name": "ipython", 1033 | "version": 3 1034 | }, 1035 | "file_extension": ".py", 1036 | "mimetype": "text/x-python", 1037 | "name": "python", 1038 | "nbconvert_exporter": "python", 1039 | "pygments_lexer": "ipython3", 1040 | "version": "3.9.16" 1041 | } 1042 | }, 1043 | "nbformat": 4, 1044 | "nbformat_minor": 5 1045 | } 1046 | --------------------------------------------------------------------------------