├── .gitattributes ├── 0. Python_Intro.ipynb ├── 01. Pandas.ipynb ├── 01. PandasAndR.ipynb ├── 02. DataVisualization.ipynb ├── 03. Dimension Reduction.ipynb ├── 04. Logits and SVM.ipynb ├── 05. Decision Trees.ipynb ├── 06. Classification.ipynb ├── 07. Regression.ipynb ├── 09. Clustering and Discretization.ipynb ├── 10. Clustering Validity.ipynb ├── 11. More Clustering.ipynb ├── 12. Association Analysis.ipynb ├── 13. Recommendation Systems - Copy.ipynb ├── 13. Recommendation Systems.ipynb ├── 14. OGrisel Pandas.ipynb ├── E00_Numpy and Pandas Review.ipynb ├── E01_StopAndFrisk_DateTimeExample.ipynb ├── ICA1_DataMining-PartA.ipynb ├── ICA2_DataMining-PartA.ipynb ├── ICA3_DataMining-PartA.ipynb ├── ICA4_DataMining-PartA.ipynb ├── ICA5-PartA ├── ICA5_DataMining-PartA.ipynb ├── Rframe_as_pandas.csv ├── titanic2.raw.rdata └── titanic_raw.csv ├── README.md ├── Syllabus.pdf ├── data ├── .DS_Store ├── SQF 2012.csv ├── diabetes.arff ├── guido.png ├── heart_disease.csv ├── heart_disease_sql ├── ospd.txt ├── python_ranking.png ├── titanic.csv ├── titanic_raw.csv └── titanic_train.csv └── statcompare.py /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /E00_Numpy and Pandas Review.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Why python for data analysis?\n", 8 | "There are lots of reasons that we want to use python for doing data science. It is certainly one of the younger programming languages used in the data science ecosystem (compared to say R and SAS) but it is used just as frequently for analysis as SAS and R. Having a good foundation in python, R, and SAS should be a *must* for **every data scientist**. \n", 9 | "\n", 10 | "In this course, python allows for an open source method of performing machine learning that runs from just about any machine. So let's start with looking at Numpy and Pandas packages for analyzing data. \n", 11 | "\n", 12 | "With that in mind, let's go over the following:\n", 13 | "- Numpy matrices\n", 14 | "- Simple operations on arrays and matrices\n", 15 | "- Indexing with numpy\n", 16 | "- Pandas for tabular data\n", 17 | "- Representing categorical data (discussion point)" 18 | ] 19 | }, 20 | { 21 | "cell_type": "code", 22 | "execution_count": 1, 23 | "metadata": {}, 24 | "outputs": [ 25 | { 26 | "data": { 27 | "text/plain": [ 28 | "array([[0.41692445, 0.26797325, 0.76390328],\n", 29 | " [0.62033705, 0.39400496, 0.75837898],\n", 30 | " [0.79553523, 0.53961973, 0.35636875],\n", 31 | " [0.1530034 , 0.62451399, 0.39696761],\n", 32 | " [0.47501647, 0.54509539, 0.4492403 ]])" 33 | ] 34 | }, 35 | "execution_count": 1, 36 | "metadata": {}, 37 | "output_type": "execute_result" 38 | } 39 | ], 40 | "source": [ 41 | "import numpy as np\n", 42 | "\n", 43 | "x = np.random.rand(5,3)\n", 44 | "x" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 2, 50 | "metadata": {}, 51 | "outputs": [ 52 | { 53 | "data": { 54 | "text/plain": [ 55 | "(5, 3)" 56 | ] 57 | }, 58 | "execution_count": 2, 59 | "metadata": {}, 60 | "output_type": "execute_result" 61 | } 62 | ], 63 | "source": [ 64 | "x.shape" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 3, 70 | "metadata": {}, 71 | "outputs": [ 72 | { 73 | "data": { 74 | "text/plain": [ 75 | "dtype('float64')" 76 | ] 77 | }, 78 | "execution_count": 3, 79 | "metadata": {}, 80 | "output_type": "execute_result" 81 | } 82 | ], 83 | "source": [ 84 | "x.dtype" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 4, 90 | "metadata": {}, 91 | "outputs": [ 92 | { 93 | "ename": "ValueError", 94 | "evalue": "operands could not be broadcast together with shapes (5,3) (3,4) ", 95 | "output_type": "error", 96 | "traceback": [ 97 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 98 | "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", 99 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mz\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 100 | "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (5,3) (3,4) " 101 | ] 102 | } 103 | ], 104 | "source": [ 105 | "y = np.random.rand(3,4)\n", 106 | "z = x*y\n", 107 | "z" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": 5, 113 | "metadata": {}, 114 | "outputs": [ 115 | { 116 | "data": { 117 | "text/plain": [ 118 | "array([[1.12584244, 0.7018893 , 0.65830929, 0.29644248],\n", 119 | " [1.32167909, 0.87565909, 0.97452776, 0.32785765],\n", 120 | " [1.12419143, 0.8855276 , 1.28208108, 0.24136954],\n", 121 | " [0.58547789, 0.7762429 , 0.7478308 , 0.21071869],\n", 122 | " [0.91876454, 0.82980165, 0.98019355, 0.24299231]])" 123 | ] 124 | }, 125 | "execution_count": 5, 126 | "metadata": {}, 127 | "output_type": "execute_result" 128 | } 129 | ], 130 | "source": [ 131 | "z = x @ y\n", 132 | "\n", 133 | "z" 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "execution_count": 6, 139 | "metadata": {}, 140 | "outputs": [ 141 | { 142 | "data": { 143 | "text/plain": [ 144 | "matrix([[1.12584244, 0.7018893 , 0.65830929, 0.29644248],\n", 145 | " [1.32167909, 0.87565909, 0.97452776, 0.32785765],\n", 146 | " [1.12419143, 0.8855276 , 1.28208108, 0.24136954],\n", 147 | " [0.58547789, 0.7762429 , 0.7478308 , 0.21071869],\n", 148 | " [0.91876454, 0.82980165, 0.98019355, 0.24299231]])" 149 | ] 150 | }, 151 | "execution_count": 6, 152 | "metadata": {}, 153 | "output_type": "execute_result" 154 | } 155 | ], 156 | "source": [ 157 | "x = np.mat(x)\n", 158 | "y = np.mat(y)\n", 159 | "z = x*y\n", 160 | "z" 161 | ] 162 | }, 163 | { 164 | "cell_type": "markdown", 165 | "metadata": {}, 166 | "source": [ 167 | "# Indexing" 168 | ] 169 | }, 170 | { 171 | "cell_type": "code", 172 | "execution_count": 7, 173 | "metadata": {}, 174 | "outputs": [ 175 | { 176 | "data": { 177 | "text/plain": [ 178 | "array([[1, 2, 3],\n", 179 | " [4, 5, 6],\n", 180 | " [7, 8, 9]])" 181 | ] 182 | }, 183 | "execution_count": 7, 184 | "metadata": {}, 185 | "output_type": "execute_result" 186 | } 187 | ], 188 | "source": [ 189 | "x1 = np.array([[1,2,3],\n", 190 | " [4,5,6],\n", 191 | " [7,8,9]])\n", 192 | "x1" 193 | ] 194 | }, 195 | { 196 | "cell_type": "code", 197 | "execution_count": 8, 198 | "metadata": {}, 199 | "outputs": [ 200 | { 201 | "name": "stdout", 202 | "output_type": "stream", 203 | "text": [ 204 | "2\n", 205 | "5\n", 206 | "8\n" 207 | ] 208 | } 209 | ], 210 | "source": [ 211 | "for row in range(x1.shape[0]):\n", 212 | " print (x1[row,1])" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": 9, 218 | "metadata": {}, 219 | "outputs": [ 220 | { 221 | "data": { 222 | "text/plain": [ 223 | "array([2, 5, 8])" 224 | ] 225 | }, 226 | "execution_count": 9, 227 | "metadata": {}, 228 | "output_type": "execute_result" 229 | } 230 | ], 231 | "source": [ 232 | "x1[:,1]" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": 10, 238 | "metadata": {}, 239 | "outputs": [ 240 | { 241 | "data": { 242 | "text/plain": [ 243 | "array([False, True, True])" 244 | ] 245 | }, 246 | "execution_count": 10, 247 | "metadata": {}, 248 | "output_type": "execute_result" 249 | } 250 | ], 251 | "source": [ 252 | "x1[:,1]>3" 253 | ] 254 | }, 255 | { 256 | "cell_type": "code", 257 | "execution_count": 11, 258 | "metadata": {}, 259 | "outputs": [ 260 | { 261 | "data": { 262 | "text/plain": [ 263 | "array([[4, 5, 6],\n", 264 | " [7, 8, 9]])" 265 | ] 266 | }, 267 | "execution_count": 11, 268 | "metadata": {}, 269 | "output_type": "execute_result" 270 | } 271 | ], 272 | "source": [ 273 | "x1[ x1[:,1]>3 ]" 274 | ] 275 | }, 276 | { 277 | "cell_type": "code", 278 | "execution_count": 12, 279 | "metadata": {}, 280 | "outputs": [ 281 | { 282 | "data": { 283 | "text/plain": [ 284 | "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" 285 | ] 286 | }, 287 | "execution_count": 12, 288 | "metadata": {}, 289 | "output_type": "execute_result" 290 | } 291 | ], 292 | "source": [ 293 | "x2 = np.array(range(10))\n", 294 | "x2" 295 | ] 296 | }, 297 | { 298 | "cell_type": "code", 299 | "execution_count": 13, 300 | "metadata": {}, 301 | "outputs": [ 302 | { 303 | "data": { 304 | "text/plain": [ 305 | "(10,)" 306 | ] 307 | }, 308 | "execution_count": 13, 309 | "metadata": {}, 310 | "output_type": "execute_result" 311 | } 312 | ], 313 | "source": [ 314 | "x2.shape" 315 | ] 316 | }, 317 | { 318 | "cell_type": "code", 319 | "execution_count": 14, 320 | "metadata": {}, 321 | "outputs": [ 322 | { 323 | "data": { 324 | "text/plain": [ 325 | "array([False, False, False, False, False, False, True, True, True,\n", 326 | " True])" 327 | ] 328 | }, 329 | "execution_count": 14, 330 | "metadata": {}, 331 | "output_type": "execute_result" 332 | } 333 | ], 334 | "source": [ 335 | "idx = x2>5\n", 336 | "idx" 337 | ] 338 | }, 339 | { 340 | "cell_type": "code", 341 | "execution_count": 15, 342 | "metadata": {}, 343 | "outputs": [ 344 | { 345 | "data": { 346 | "text/plain": [ 347 | "array([6, 7, 8, 9])" 348 | ] 349 | }, 350 | "execution_count": 15, 351 | "metadata": {}, 352 | "output_type": "execute_result" 353 | } 354 | ], 355 | "source": [ 356 | "x2[idx]" 357 | ] 358 | }, 359 | { 360 | "cell_type": "code", 361 | "execution_count": 16, 362 | "metadata": {}, 363 | "outputs": [ 364 | { 365 | "data": { 366 | "text/plain": [ 367 | "array([6, 7, 8, 9])" 368 | ] 369 | }, 370 | "execution_count": 16, 371 | "metadata": {}, 372 | "output_type": "execute_result" 373 | } 374 | ], 375 | "source": [ 376 | "x2[x2>5]" 377 | ] 378 | }, 379 | { 380 | "cell_type": "markdown", 381 | "metadata": {}, 382 | "source": [ 383 | "# Named columns\n", 384 | "So what if we have a matrix of data where each row is some observation of features and the feature values are represented in each column?" 385 | ] 386 | }, 387 | { 388 | "cell_type": "code", 389 | "execution_count": 17, 390 | "metadata": {}, 391 | "outputs": [ 392 | { 393 | "data": { 394 | "text/plain": [ 395 | "array([[ 64, 2100, 1],\n", 396 | " [ 50, 2200, 1],\n", 397 | " [ 48, 2300, 1],\n", 398 | " [ 34, 0, 2],\n", 399 | " [ 30, 100, 2]])" 400 | ] 401 | }, 402 | "execution_count": 17, 403 | "metadata": {}, 404 | "output_type": "execute_result" 405 | } 406 | ], 407 | "source": [ 408 | "col_names = ['temperature','time','day']\n", 409 | "data = np.array([[64,2100,1],\n", 410 | " [50,2200,1],\n", 411 | " [48,2300,1],\n", 412 | " [34,0, 2],\n", 413 | " [30,100, 2]])\n", 414 | "data" 415 | ] 416 | }, 417 | { 418 | "cell_type": "code", 419 | "execution_count": 18, 420 | "metadata": {}, 421 | "outputs": [ 422 | { 423 | "data": { 424 | "text/plain": [ 425 | "array([[ 64, 2100, 1],\n", 426 | " [ 50, 2200, 1],\n", 427 | " [ 48, 2300, 1]])" 428 | ] 429 | }, 430 | "execution_count": 18, 431 | "metadata": {}, 432 | "output_type": "execute_result" 433 | } 434 | ], 435 | "source": [ 436 | "data2 = data[data[:,1]>1500]\n", 437 | "data2" 438 | ] 439 | }, 440 | { 441 | "cell_type": "code", 442 | "execution_count": 19, 443 | "metadata": {}, 444 | "outputs": [ 445 | { 446 | "data": { 447 | "text/html": [ 448 | "
\n", 449 | "\n", 462 | "\n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | " \n", 488 | " \n", 489 | " \n", 490 | " \n", 491 | " \n", 492 | " \n", 493 | " \n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | " \n", 502 | " \n", 503 | "
temperaturetimeday
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" 505 | ], 506 | "text/plain": [ 507 | " temperature time day\n", 508 | "0 64 2100 1\n", 509 | "1 50 2200 1\n", 510 | "2 48 2300 1\n", 511 | "3 34 0 2\n", 512 | "4 30 100 2" 513 | ] 514 | }, 515 | "execution_count": 19, 516 | "metadata": {}, 517 | "output_type": "execute_result" 518 | } 519 | ], 520 | "source": [ 521 | "# pandas to the rescue\n", 522 | "import pandas as pd\n", 523 | "\n", 524 | "df = pd.DataFrame(data,columns=col_names)\n", 525 | "df" 526 | ] 527 | }, 528 | { 529 | "cell_type": "code", 530 | "execution_count": 20, 531 | "metadata": {}, 532 | "outputs": [ 533 | { 534 | "data": { 535 | "text/html": [ 536 | "
\n", 537 | "\n", 550 | "\n", 551 | " \n", 552 | " \n", 553 | " \n", 554 | " \n", 555 | " \n", 556 | " \n", 557 | " \n", 558 | " \n", 559 | " \n", 560 | " \n", 561 | " \n", 562 | " \n", 563 | " \n", 564 | " \n", 565 | " \n", 566 | " \n", 567 | " \n", 568 | " \n", 569 | " \n", 570 | " \n", 571 | " \n", 572 | " \n", 573 | " \n", 574 | " \n", 575 | " \n", 576 | " \n", 577 | " \n", 578 | " \n", 579 | "
temperaturetimeday
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" 581 | ], 582 | "text/plain": [ 583 | " temperature time day\n", 584 | "0 64 2100 1\n", 585 | "1 50 2200 1\n", 586 | "2 48 2300 1" 587 | ] 588 | }, 589 | "execution_count": 20, 590 | "metadata": {}, 591 | "output_type": "execute_result" 592 | } 593 | ], 594 | "source": [ 595 | "df[df.time>1500]" 596 | ] 597 | }, 598 | { 599 | "cell_type": "code", 600 | "execution_count": 21, 601 | "metadata": {}, 602 | "outputs": [ 603 | { 604 | "name": "stdout", 605 | "output_type": "stream", 606 | "text": [ 607 | "\n", 608 | "RangeIndex: 5 entries, 0 to 4\n", 609 | "Data columns (total 3 columns):\n", 610 | "temperature 5 non-null int32\n", 611 | "time 5 non-null int32\n", 612 | "day 5 non-null int32\n", 613 | "dtypes: int32(3)\n", 614 | "memory usage: 188.0 bytes\n" 615 | ] 616 | } 617 | ], 618 | "source": [ 619 | "df.info()" 620 | ] 621 | }, 622 | { 623 | "cell_type": "code", 624 | "execution_count": 22, 625 | "metadata": {}, 626 | "outputs": [], 627 | "source": [ 628 | "df.day[df.day==1] = 'Mon'" 629 | ] 630 | }, 631 | { 632 | "cell_type": "code", 633 | "execution_count": 23, 634 | "metadata": {}, 635 | "outputs": [ 636 | { 637 | "data": { 638 | "text/html": [ 639 | "
\n", 640 | "\n", 653 | "\n", 654 | " \n", 655 | " \n", 656 | " \n", 657 | " \n", 658 | " \n", 659 | " \n", 660 | " \n", 661 | " \n", 662 | " \n", 663 | " \n", 664 | " \n", 665 | " \n", 666 | " \n", 667 | " \n", 668 | " \n", 669 | " \n", 670 | " \n", 671 | " \n", 672 | " \n", 673 | " \n", 674 | " \n", 675 | " \n", 676 | " \n", 677 | " \n", 678 | " \n", 679 | " \n", 680 | " \n", 681 | " \n", 682 | " \n", 683 | " \n", 684 | " \n", 685 | " \n", 686 | " \n", 687 | " \n", 688 | " \n", 689 | " \n", 690 | " \n", 691 | " \n", 692 | " \n", 693 | " \n", 694 | "
temperaturetimeday
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" 696 | ], 697 | "text/plain": [ 698 | " temperature time day\n", 699 | "0 64 2100 Mon\n", 700 | "1 50 2200 Mon\n", 701 | "2 48 2300 Mon\n", 702 | "3 34 0 2\n", 703 | "4 30 100 2" 704 | ] 705 | }, 706 | "execution_count": 23, 707 | "metadata": {}, 708 | "output_type": "execute_result" 709 | } 710 | ], 711 | "source": [ 712 | "df" 713 | ] 714 | }, 715 | { 716 | "cell_type": "code", 717 | "execution_count": 24, 718 | "metadata": {}, 719 | "outputs": [ 720 | { 721 | "data": { 722 | "text/html": [ 723 | "
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temperaturetimeday
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" 780 | ], 781 | "text/plain": [ 782 | " temperature time day\n", 783 | "0 64 2100 Mon\n", 784 | "1 50 2200 Mon\n", 785 | "2 48 2300 Mon\n", 786 | "3 34 0 Tues\n", 787 | "4 30 100 Tues" 788 | ] 789 | }, 790 | "execution_count": 24, 791 | "metadata": {}, 792 | "output_type": "execute_result" 793 | } 794 | ], 795 | "source": [ 796 | "df.day.replace(to_replace=range(7),\n", 797 | " value=['Su','Mon','Tues','Wed','Th','Fri','Sat'],\n", 798 | " inplace=True)\n", 799 | "df" 800 | ] 801 | }, 802 | { 803 | "cell_type": "code", 804 | "execution_count": 25, 805 | "metadata": {}, 806 | "outputs": [ 807 | { 808 | "name": "stdout", 809 | "output_type": "stream", 810 | "text": [ 811 | "\n", 812 | "RangeIndex: 5 entries, 0 to 4\n", 813 | "Data columns (total 3 columns):\n", 814 | "temperature 5 non-null int32\n", 815 | "time 5 non-null int32\n", 816 | "day 5 non-null object\n", 817 | "dtypes: int32(2), object(1)\n", 818 | "memory usage: 208.0+ bytes\n" 819 | ] 820 | } 821 | ], 822 | "source": [ 823 | "df.info()" 824 | ] 825 | }, 826 | { 827 | "cell_type": "code", 828 | "execution_count": null, 829 | "metadata": { 830 | "collapsed": true 831 | }, 832 | "outputs": [], 833 | "source": [] 834 | } 835 | ], 836 | "metadata": { 837 | "anaconda-cloud": {}, 838 | "kernelspec": { 839 | "display_name": "Python 3", 840 | "language": "python", 841 | "name": "python3" 842 | }, 843 | "language_info": { 844 | "codemirror_mode": { 845 | "name": "ipython", 846 | "version": 3 847 | }, 848 | "file_extension": ".py", 849 | "mimetype": "text/x-python", 850 | "name": "python", 851 | "nbconvert_exporter": "python", 852 | "pygments_lexer": "ipython3", 853 | "version": "3.7.1" 854 | } 855 | }, 856 | "nbformat": 4, 857 | "nbformat_minor": 1 858 | } 859 | -------------------------------------------------------------------------------- /ICA1_DataMining-PartA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "text/html": [ 11 | "" 12 | ], 13 | "text/plain": [ 14 | "" 15 | ] 16 | }, 17 | "execution_count": 1, 18 | "metadata": {}, 19 | "output_type": "execute_result" 20 | } 21 | ], 22 | "source": [ 23 | "# Ebnable HTML/CSS \n", 24 | "from IPython.core.display import HTML\n", 25 | "HTML(\"\")" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "metadata": {}, 31 | "source": [ 32 | "___\n", 33 | "Enter Team Member Names here (double click to edit):\n", 34 | "\n", 35 | "- Name 1:\n", 36 | "- Name 2:\n", 37 | "- Name 3:\n" 38 | ] 39 | }, 40 | { 41 | "cell_type": "markdown", 42 | "metadata": {}, 43 | "source": [ 44 | "# In Class Assignment One\n", 45 | "In the following assignment you will be asked to fill in python code and derivations for a number of different problems. Please read all instructions carefully and turn in the rendered notebook (or HTML of the rendered notebook) before the end of class (or right after class). The initial portion of this notebook is given before class and the remainder is given during class. Please answer the initial questions before class. Once class has started you may rework your answers as a team for the initial part of the assignment. \n", 46 | "\n", 47 | "## Contents\n", 48 | "* Loading the Data\n", 49 | "* Linear Regression\n", 50 | "\n", 51 | "**These portions are not yet accessible until the start of class:**\n", 52 | "* Using Scikit Learn for Regression\n", 53 | "* Linear Classification\n", 54 | "\n", 55 | "________________________________________________________________________________________________________\n", 56 | "\n", 57 | "\n", 58 | "## Loading the Data\n", 59 | "Please run the following code to read in the \"diabetes\" dataset from sklearn's data loading module. \n", 60 | "\n", 61 | "This will load the data into the variable `ds`. `ds` is a dictionary object with fields like `ds.data`, which is a matrix of the continuous features in the dataset. The object is not a pandas dataframe. It is a numpy matrix. Each row is a set of observed instances, each column is a different feature. It also has a field called `ds.target` that is a continuous value we are trying to predict. Each entry in `ds.target` is a label for each row of the `ds.data` matrix. " 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 2, 67 | "metadata": {}, 68 | "outputs": [ 69 | { 70 | "name": "stdout", 71 | "output_type": "stream", 72 | "text": [ 73 | "features shape: (442, 10) format is: ('rows', 'columns')\n", 74 | "range of target: 25.0 346.0\n" 75 | ] 76 | } 77 | ], 78 | "source": [ 79 | "from __future__ import print_function\n", 80 | "from sklearn.datasets import load_diabetes\n", 81 | "import numpy as np\n", 82 | "\n", 83 | "\n", 84 | "\n", 85 | "ds = load_diabetes()\n", 86 | "\n", 87 | "# this holds the continuous feature data\n", 88 | "# because ds.data is a matrix, there are some special properties we can access (like 'shape')\n", 89 | "print('features shape:', ds.data.shape, 'format is:', ('rows','columns')) # there are 442 instances and 10 features per instance\n", 90 | "print('range of target:', np.min(ds.target),np.max(ds.target))" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 3, 96 | "metadata": {}, 97 | "outputs": [ 98 | { 99 | "name": "stdout", 100 | "output_type": "stream", 101 | "text": [ 102 | "array([[ 0.03807591, 0.05068012, 0.06169621, ..., -0.00259226,\n", 103 | " 0.01990842, -0.01764613],\n", 104 | " [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n", 105 | " -0.06832974, -0.09220405],\n", 106 | " [ 0.08529891, 0.05068012, 0.04445121, ..., -0.00259226,\n", 107 | " 0.00286377, -0.02593034],\n", 108 | " ...,\n", 109 | " [ 0.04170844, 0.05068012, -0.01590626, ..., -0.01107952,\n", 110 | " -0.04687948, 0.01549073],\n", 111 | " [-0.04547248, -0.04464164, 0.03906215, ..., 0.02655962,\n", 112 | " 0.04452837, -0.02593034],\n", 113 | " [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n", 114 | " -0.00421986, 0.00306441]])\n", 115 | "array([151., 75., 141., 206., 135., 97., 138., 63., 110., 310., 101.,\n", 116 | " 69., 179., 185., 118., 171., 166., 144., 97., 168., 68., 49.,\n", 117 | " 68., 245., 184., 202., 137., 85., 131., 283., 129., 59., 341.,\n", 118 | " 87., 65., 102., 265., 276., 252., 90., 100., 55., 61., 92.,\n", 119 | " 259., 53., 190., 142., 75., 142., 155., 225., 59., 104., 182.,\n", 120 | " 128., 52., 37., 170., 170., 61., 144., 52., 128., 71., 163.,\n", 121 | " 150., 97., 160., 178., 48., 270., 202., 111., 85., 42., 170.,\n", 122 | " 200., 252., 113., 143., 51., 52., 210., 65., 141., 55., 134.,\n", 123 | " 42., 111., 98., 164., 48., 96., 90., 162., 150., 279., 92.,\n", 124 | " 83., 128., 102., 302., 198., 95., 53., 134., 144., 232., 81.,\n", 125 | " 104., 59., 246., 297., 258., 229., 275., 281., 179., 200., 200.,\n", 126 | " 173., 180., 84., 121., 161., 99., 109., 115., 268., 274., 158.,\n", 127 | " 107., 83., 103., 272., 85., 280., 336., 281., 118., 317., 235.,\n", 128 | " 60., 174., 259., 178., 128., 96., 126., 288., 88., 292., 71.,\n", 129 | " 197., 186., 25., 84., 96., 195., 53., 217., 172., 131., 214.,\n", 130 | " 59., 70., 220., 268., 152., 47., 74., 295., 101., 151., 127.,\n", 131 | " 237., 225., 81., 151., 107., 64., 138., 185., 265., 101., 137.,\n", 132 | " 143., 141., 79., 292., 178., 91., 116., 86., 122., 72., 129.,\n", 133 | " 142., 90., 158., 39., 196., 222., 277., 99., 196., 202., 155.,\n", 134 | " 77., 191., 70., 73., 49., 65., 263., 248., 296., 214., 185.,\n", 135 | " 78., 93., 252., 150., 77., 208., 77., 108., 160., 53., 220.,\n", 136 | " 154., 259., 90., 246., 124., 67., 72., 257., 262., 275., 177.,\n", 137 | " 71., 47., 187., 125., 78., 51., 258., 215., 303., 243., 91.,\n", 138 | " 150., 310., 153., 346., 63., 89., 50., 39., 103., 308., 116.,\n", 139 | " 145., 74., 45., 115., 264., 87., 202., 127., 182., 241., 66.,\n", 140 | " 94., 283., 64., 102., 200., 265., 94., 230., 181., 156., 233.,\n", 141 | " 60., 219., 80., 68., 332., 248., 84., 200., 55., 85., 89.,\n", 142 | " 31., 129., 83., 275., 65., 198., 236., 253., 124., 44., 172.,\n", 143 | " 114., 142., 109., 180., 144., 163., 147., 97., 220., 190., 109.,\n", 144 | " 191., 122., 230., 242., 248., 249., 192., 131., 237., 78., 135.,\n", 145 | " 244., 199., 270., 164., 72., 96., 306., 91., 214., 95., 216.,\n", 146 | " 263., 178., 113., 200., 139., 139., 88., 148., 88., 243., 71.,\n", 147 | " 77., 109., 272., 60., 54., 221., 90., 311., 281., 182., 321.,\n", 148 | " 58., 262., 206., 233., 242., 123., 167., 63., 197., 71., 168.,\n", 149 | " 140., 217., 121., 235., 245., 40., 52., 104., 132., 88., 69.,\n", 150 | " 219., 72., 201., 110., 51., 277., 63., 118., 69., 273., 258.,\n", 151 | " 43., 198., 242., 232., 175., 93., 168., 275., 293., 281., 72.,\n", 152 | " 140., 189., 181., 209., 136., 261., 113., 131., 174., 257., 55.,\n", 153 | " 84., 42., 146., 212., 233., 91., 111., 152., 120., 67., 310.,\n", 154 | " 94., 183., 66., 173., 72., 49., 64., 48., 178., 104., 132.,\n", 155 | " 220., 57.])\n" 156 | ] 157 | } 158 | ], 159 | "source": [ 160 | "from pprint import pprint\n", 161 | "\n", 162 | "# we can set the fields inside of ds and set them to new variables in python\n", 163 | "pprint(ds.data) # prints out elements of the matrix\n", 164 | "pprint(ds.target) # prints the vector (all 442 items)" 165 | ] 166 | }, 167 | { 168 | "cell_type": "markdown", 169 | "metadata": {}, 170 | "source": [ 171 | "________________________________________________________________________________________________________\n", 172 | "\n", 173 | "## Using Linear Regression \n", 174 | "In the videos, we derived the formula for calculating the optimal values of the regression weights (you must be connected to the internet for this equation to show up properly):\n", 175 | "\n", 176 | "$$ w = (X^TX)^{-1}X^Ty $$\n", 177 | "\n", 178 | "where $X$ is the matrix of values with a bias column of ones appended onto it. For the diabetes dataset one could construct this $X$ matrix by stacking a column of ones onto the `ds.data` matrix. \n", 179 | "\n", 180 | "$$ X=\\begin{bmatrix}\n", 181 | " & \\vdots & & 1 \\\\\n", 182 | " \\dotsb & \\text{ds.data} & \\dotsb & \\vdots\\\\\n", 183 | " & \\vdots & & 1\\\\\n", 184 | " \\end{bmatrix}\n", 185 | "$$\n", 186 | "\n", 187 | "**Question 1:** For the diabetes dataset, how many elements will the vector $w$ contain?" 188 | ] 189 | }, 190 | { 191 | "cell_type": "code", 192 | "execution_count": 7, 193 | "metadata": {}, 194 | "outputs": [], 195 | "source": [ 196 | "# Enter your answer here (or write code to calculate it)\n", 197 | " \n", 198 | "\n", 199 | "#" 200 | ] 201 | }, 202 | { 203 | "cell_type": "markdown", 204 | "metadata": {}, 205 | "source": [ 206 | "________________________________________________________________________________________________________\n", 207 | "\n", 208 | "**Exercise 1:** In the following empty cell, use this equation and numpy matrix operations to find the values of the vector $w$. You will need to be sure $X$ and $y$ are created like the instructor talked about in the video. Don't forget to include any modifications to $X$ to account for the bias term in $w$. You might be interested in the following functions:\n", 209 | "\n", 210 | "- `np.hstack((mat1,mat2))` stack two matrices horizontally, to create a new matrix\n", 211 | "- `np.ones((rows,cols))` create a matrix full of ones\n", 212 | "- `my_mat.T` takes transpose of numpy matrix named `my_mat`\n", 213 | "- `np.dot(mat1,mat2)` or `mat1 @ mat2` is matrix multiplication for two matrices\n", 214 | "- `np.linalg.inv(mat)` gets the inverse of the variable `mat`" 215 | ] 216 | }, 217 | { 218 | "cell_type": "code", 219 | "execution_count": 21, 220 | "metadata": {}, 221 | "outputs": [ 222 | { 223 | "name": "stdout", 224 | "output_type": "stream", 225 | "text": [ 226 | "[[ -10.01219782]\n", 227 | " [-239.81908937]\n", 228 | " [ 519.83978679]\n", 229 | " [ 324.39042769]\n", 230 | " [-792.18416163]\n", 231 | " [ 476.74583782]\n", 232 | " [ 101.04457032]\n", 233 | " [ 177.06417623]\n", 234 | " [ 751.27932109]\n", 235 | " [ 67.62538639]\n", 236 | " [ 152.13348416]]\n" 237 | ] 238 | } 239 | ], 240 | "source": [ 241 | "# Write you code here, print the values of the regression weights using the 'print()' function in python\n" 242 | ] 243 | }, 244 | { 245 | "cell_type": "code", 246 | "execution_count": null, 247 | "metadata": {}, 248 | "outputs": [], 249 | "source": [] 250 | } 251 | ], 252 | "metadata": { 253 | "anaconda-cloud": {}, 254 | "kernelspec": { 255 | "display_name": "Python 3", 256 | "language": "python", 257 | "name": "python3" 258 | }, 259 | "language_info": { 260 | "codemirror_mode": { 261 | "name": "ipython", 262 | "version": 3 263 | }, 264 | "file_extension": ".py", 265 | "mimetype": "text/x-python", 266 | "name": "python", 267 | "nbconvert_exporter": "python", 268 | "pygments_lexer": "ipython3", 269 | "version": "3.7.1" 270 | } 271 | }, 272 | "nbformat": 4, 273 | "nbformat_minor": 1 274 | } 275 | -------------------------------------------------------------------------------- /ICA2_DataMining-PartA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "text/html": [ 11 | "" 12 | ], 13 | "text/plain": [ 14 | "" 15 | ] 16 | }, 17 | "execution_count": 1, 18 | "metadata": {}, 19 | "output_type": "execute_result" 20 | } 21 | ], 22 | "source": [ 23 | "# Ebnable HTML/CSS \n", 24 | "from IPython.core.display import HTML\n", 25 | "HTML(\"\")" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "metadata": {}, 31 | "source": [ 32 | "\n", 33 | "___\n", 34 | "Enter Team Member Names here (double click to edit):\n", 35 | "\n", 36 | "- Name 1:\n", 37 | "- Name 2:\n", 38 | "- Name 3:\n", 39 | "\n" 40 | ] 41 | }, 42 | { 43 | "cell_type": "markdown", 44 | "metadata": {}, 45 | "source": [ 46 | "________\n", 47 | "\n", 48 | "# Live Session Assignment Two\n", 49 | "In the following assignment you will be asked to fill in python code and derivations for a number of different problems. Please read all instructions carefully and turn in the rendered notebook (.ipynb file, remember to save it!!) or HTML of the rendered notebook before the end of class.\n", 50 | "\n", 51 | "## Contents\n", 52 | "* Loading the Classification Data\n", 53 | "* Using Decision Trees - Gini\n", 54 | "\n", 55 | "**These contents will become available during the live session: **\n", 56 | "* Using Decision Trees - Entropy\n", 57 | "* Multi-way Splits\n", 58 | "* Decision Trees in Scikit-Learn\n", 59 | "\n", 60 | "________________________________________________________________________________________________________\n", 61 | "\n", 62 | "Back to Top\n", 63 | "## Loading the Classification Data\n", 64 | "Please run the following code to read in the \"digits\" dataset from sklearn's data loading module. This is identical to the first in class assignment for loading the data into matrices. `ds.data` is a matrix of feature values and `ds.target` is a column vector of the class output (in our case, the hand written digit we want to classify). Each class is a number (0 through 9) that we want to classify as one of ten hand written digits. \n", 65 | "\n" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 2, 71 | "metadata": {}, 72 | "outputs": [ 73 | { 74 | "name": "stdout", 75 | "output_type": "stream", 76 | "text": [ 77 | "features shape: (1797, 64)\n", 78 | "target shape: (1797,)\n", 79 | "range of target: 0 9\n" 80 | ] 81 | } 82 | ], 83 | "source": [ 84 | "from __future__ import print_function\n", 85 | "from sklearn.datasets import load_digits\n", 86 | "import numpy as np\n", 87 | "\n", 88 | "ds = load_digits()\n", 89 | "\n", 90 | "# this holds the continuous feature data\n", 91 | "print('features shape:', ds.data.shape) # there are 1797 instances and 64 features per instance\n", 92 | "print('target shape:', ds.target.shape )\n", 93 | "print('range of target:', np.min(ds.target),np.max(ds.target))" 94 | ] 95 | }, 96 | { 97 | "cell_type": "markdown", 98 | "metadata": {}, 99 | "source": [ 100 | "________________________________________________________________________________________________________\n", 101 | "\n", 102 | "Back to Top\n", 103 | "## Using Decision Trees\n", 104 | "In the videos, we talked about the splitting conditions for different attributes. Specifically, we discussed the number of ways in which it is possible to split a node, depending on the attribute types. To understand the possible splits, we need to understand the attributes. For the question below, you might find the description in the `ds['DESCR']` field to be useful. You can see the field using `print(ds['DESCR'])`\n", 105 | "\n", 106 | "**Question 1:** For the digits dataset, what are the type(s) of the attributes? How many attributes are there? What do they represent?\n" 107 | ] 108 | }, 109 | { 110 | "cell_type": "code", 111 | "execution_count": 3, 112 | "metadata": {}, 113 | "outputs": [], 114 | "source": [ 115 | "## Enter your comments here\n", 116 | "\n", 117 | "\n", 118 | "\n", 119 | "## Enter comments here" 120 | ] 121 | }, 122 | { 123 | "cell_type": "markdown", 124 | "metadata": {}, 125 | "source": [ 126 | "___\n", 127 | "## Using the gini coefficient\n", 128 | "We talked about the gini index in the videos. The gini coefficient for a **given split** is given by:\n", 129 | "$$Gini=\\sum_{t=1}^T \\frac{n_t}{N}gini(t)$$\n", 130 | "where $T$ is the total number of splits (2 for binary attributes), $n_t$ is the number of instances in node $t$ after splitting, and $N$ is the total number of instances in the parent node. $gini(t)$ is the **gini index for each individual node that is created by the split** and is given by:\n", 131 | "$$gini(t)=1-\\sum_{j=0}^{C-1} p(j|t)^2$$\n", 132 | "where $C$ is the total number of possible classes and $p(j|t)$ is the probability of class $j$ in node $t$ (i.e., $n_j==$ the count of instances belonging to class $j$ in node $t$, normalized by the total number of instances in node $t$).\n", 133 | "$$ p(j|t) = \\frac{n_j}{n_t}$$ \n", 134 | "\n", 135 | "For the given dataset, $gini(t)$ has been programmed for you in the function `gini_index`. \n", 136 | "\n", 137 | "* `def gini_index(classes_in_split):`\n", 138 | " * To use the function, pass in a `numpy` array of the class labels for a node as (i.e., pass in the rows from `ds.target` that make up a node in the tree) and the gini will be returned for that node. \n" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 3, 144 | "metadata": {}, 145 | "outputs": [], 146 | "source": [ 147 | "# compute the gini of several examples for the starting dataset\n", 148 | "# This function \"gini_index\" is written for you. Once you run this block, you \n", 149 | "# will have access to the function for the notebook. You do not need to know \n", 150 | "# how this function works--only what it returns \n", 151 | "# This function returns the gini index for an array of classes in a node.\n", 152 | "def gini_index(classes_in_split):\n", 153 | " # pay no attention to this code in the function-- it just computes the gini for a given split \n", 154 | " classes_in_split = np.reshape(classes_in_split,(len(classes_in_split),-1))\n", 155 | " unique_classes = np.unique(classes_in_split)\n", 156 | " gini = 1\n", 157 | " for c in unique_classes:\n", 158 | " gini -= (np.sum(classes_in_split==c) / float(len(classes_in_split)))**2\n", 159 | " \n", 160 | " return gini" 161 | ] 162 | }, 163 | { 164 | "cell_type": "markdown", 165 | "metadata": {}, 166 | "source": [ 167 | "In the example below, the function is used calculate the gini for splitting the dataset on feature 28, with value 2.5. In this example, we need to create two separate tree nodes: the first node has all the `ds.target` labels when feature 28 is greater than 2.5, the second node has all the rows from `ds.target` where feature 28 is less than 2.5. The steps are outlined below. **Read this carefully to understand what the code does below in the block following this.**\n", 168 | "- Feature 28 is saved into a separate variable `feature28 = ds.data[:,28]`\n", 169 | "- First all the target classes for the first node are calculated using `numpy` indexing `ds.target[feature28>2.5]` \n", 170 | " - Note: this grabs all the rows in `ds.target` (the classes) which have feature 28 greater than 2.5 (similar to indexing in pandas)\n", 171 | "- Second, those classes are passed into the function to get the gini for the right node in this split (i.e., feature 28 being greater than the threshold 2.5). \n", 172 | " - `gini_r = gini_index(ds.target[feature28>2.5])`\n", 173 | "- Third, the gini is calculated for the left node in the tree. This grabs only the rows in `ds.target` where feature 28 is less than 2.5.\n", 174 | " - `gini_l = gini_index(ds.target[feature28<=2.5])`\n", 175 | "- Combining the gini indices is left as an exercise in the next section" 176 | ] 177 | }, 178 | { 179 | "cell_type": "code", 180 | "execution_count": 4, 181 | "metadata": {}, 182 | "outputs": [ 183 | { 184 | "name": "stdout", 185 | "output_type": "stream", 186 | "text": [ 187 | "gini for right node of split: 0.8845857867667073\n", 188 | "gini for left node of split: 0.7115407566535388\n" 189 | ] 190 | } 191 | ], 192 | "source": [ 193 | "#==========================Use the gini_index Example===============\n", 194 | "# get the value for this feature as a column vector \n", 195 | "# (this is like grabbing one column of the record table)\n", 196 | "feature28 = ds.data[:,28]\n", 197 | "\n", 198 | "# if we split on the value of 2.5, then this is the gini for each resulting node:\n", 199 | "gini_gr = gini_index(ds.target[feature28>2.5]) # just like in pandas, we are sending in the rows where feature28>2.5\n", 200 | "gini_lte = gini_index(ds.target[feature28<=2.5]) # and sending the rows where feature28<=2.5\n", 201 | "\n", 202 | "# compute gini example. This splits on attribute '28' with a value of 2.5\n", 203 | "print('gini for right node of split:', gini_gr)\n", 204 | "print('gini for left node of split:', gini_lte)" 205 | ] 206 | }, 207 | { 208 | "cell_type": "markdown", 209 | "metadata": {}, 210 | "source": [ 211 | "**Question 2:** Now, using the above values `gini_r` and `gini_l`. Calculate the combined Gini for the entire split. You will need to write the weighted summation (based upon the number of instances inside each node). To count the number of instances greater than a value using numpy, you can use broadcasting, which is a special way of indexing into a numpy array. For example, the code `some_array>5` will return a new numpy array of true/false elements. It is the same size as `some_array` and is marked true where the array is greater than `5`, and false otherwise. By taking the `sum` of this array, we can count how many times `some_array` is greater than `5`. \n", 212 | "\n", 213 | "`counts = sum(some_array>5)` \n", 214 | " \n", 215 | "You will need to use this syntax to count the values in each node as a result of splitting. " 216 | ] 217 | }, 218 | { 219 | "cell_type": "code", 220 | "execution_count": 21, 221 | "metadata": {}, 222 | "outputs": [ 223 | { 224 | "name": "stdout", 225 | "output_type": "stream", 226 | "text": [ 227 | "The total gini of the split for a threshold of 2.5 is: ??\n" 228 | ] 229 | } 230 | ], 231 | "source": [ 232 | "## Enter your code here\n", 233 | "\n", 234 | "\n", 235 | "## Enter your code here\n", 236 | "print('The total gini of the split for a threshold of 2.5 is:',\"??\")" 237 | ] 238 | }, 239 | { 240 | "cell_type": "code", 241 | "execution_count": null, 242 | "metadata": {}, 243 | "outputs": [], 244 | "source": [] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": null, 249 | "metadata": { 250 | "collapsed": true 251 | }, 252 | "outputs": [], 253 | "source": [] 254 | } 255 | ], 256 | "metadata": { 257 | "anaconda-cloud": {}, 258 | "kernelspec": { 259 | "display_name": "Python 3", 260 | "language": "python", 261 | "name": "python3" 262 | }, 263 | "language_info": { 264 | "codemirror_mode": { 265 | "name": "ipython", 266 | "version": 3 267 | }, 268 | "file_extension": ".py", 269 | "mimetype": "text/x-python", 270 | "name": "python", 271 | "nbconvert_exporter": "python", 272 | "pygments_lexer": "ipython3", 273 | "version": "3.7.1" 274 | } 275 | }, 276 | "nbformat": 4, 277 | "nbformat_minor": 1 278 | } 279 | -------------------------------------------------------------------------------- /ICA3_DataMining-PartA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "text/html": [ 11 | "" 12 | ], 13 | "text/plain": [ 14 | "" 15 | ] 16 | }, 17 | "execution_count": 1, 18 | "metadata": {}, 19 | "output_type": "execute_result" 20 | } 21 | ], 22 | "source": [ 23 | "# Enable HTML/CSS \n", 24 | "from IPython.core.display import HTML\n", 25 | "HTML(\"\")" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "metadata": {}, 31 | "source": [ 32 | "___\n", 33 | "Enter Team Member Names here (double click to edit):\n", 34 | "\n", 35 | "- Name 1:\n", 36 | "- Name 2:\n", 37 | "- Name 3:\n", 38 | "\n", 39 | "________\n", 40 | "\n", 41 | "# In Class Assignment Three\n", 42 | "In the following assignment you will be asked to fill in python code and derivations for a number of different problems. Please read all instructions carefully and turn in the rendered notebook (or HTML of the rendered notebook) before the end of class.\n", 43 | "\n", 44 | "\n", 45 | "## Contents\n", 46 | "* Loading the Data\n", 47 | "* Measuring Distances\n", 48 | "\n", 49 | "** Available after class begins: **\n", 50 | "* K-Nearest Neighbors\n", 51 | "* Naive Bayes\n", 52 | "\n", 53 | "________________________________________________________________________________________________________\n", 54 | "\n", 55 | "Back to Top\n", 56 | "## Downloading the Document Data\n", 57 | "Please run the following code to read in the \"20 newsgroups\" dataset from sklearn's data loading module." 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 2, 63 | "metadata": {}, 64 | "outputs": [ 65 | { 66 | "name": "stderr", 67 | "output_type": "stream", 68 | "text": [ 69 | "Downloading 20news dataset. This may take a few minutes.\n", 70 | "Downloading dataset from https://ndownloader.figshare.com/files/5975967 (14 MB)\n" 71 | ] 72 | }, 73 | { 74 | "name": "stdout", 75 | "output_type": "stream", 76 | "text": [ 77 | "features shape: (11314, 130107)\n", 78 | "target shape: (11314,)\n", 79 | "range of target: 0 19\n", 80 | "Data type is 0.1214353154362896 % of the data is non-zero\n" 81 | ] 82 | } 83 | ], 84 | "source": [ 85 | "from __future__ import print_function\n", 86 | "from sklearn.datasets import fetch_20newsgroups_vectorized\n", 87 | "import numpy as np\n", 88 | "\n", 89 | "# this takes about 30 seconds to compute, read the next section while this downloads\n", 90 | "ds = fetch_20newsgroups_vectorized(subset='train')\n", 91 | "\n", 92 | "# this holds the continuous feature data (which is tfidf)\n", 93 | "print('features shape:', ds.data.shape) # there are ~11000 instances and ~130k features per instance\n", 94 | "print('target shape:', ds.target.shape) \n", 95 | "print('range of target:', np.min(ds.target),np.max(ds.target))\n", 96 | "print('Data type is', type(ds.data), float(ds.data.nnz)/(ds.data.shape[0]*ds.data.shape[1])*100, '% of the data is non-zero')" 97 | ] 98 | }, 99 | { 100 | "cell_type": "markdown", 101 | "metadata": {}, 102 | "source": [ 103 | "## Understanding the Dataset\n", 104 | "Look at the description for the 20 newsgroups dataset at http://qwone.com/~jason/20Newsgroups/. You have just downloaded the \"vectorized\" version of the dataset, which means all the words inside the articles have gone through a transformation that binned them into 130 thousand features related to the words in them. \n", 105 | "\n", 106 | "**Question Set 1**:\n", 107 | "- How many instances are in the dataset? \n", 108 | "- What does each instance represent? \n", 109 | "- How many classes are in the dataset and what does each class represent?\n", 110 | "- Would you expect a classifier trained on this data would generalize to documents written in the past week? Why or why not?\n", 111 | "- Is the data represented as a sparse or dense matrix?" 112 | ] 113 | }, 114 | { 115 | "cell_type": "markdown", 116 | "metadata": {}, 117 | "source": [ 118 | "___\n", 119 | "Enter your answer here:\n", 120 | "\n", 121 | "*Double click to edit*\n", 122 | "\n", 123 | "\n", 124 | "\n" 125 | ] 126 | }, 127 | { 128 | "cell_type": "markdown", 129 | "metadata": {}, 130 | "source": [ 131 | "___\n", 132 | "\n", 133 | "Back to Top\n", 134 | "## Measures of Distance\n", 135 | "In the following block of code, we isolate three instances from the dataset. The instance \"`a`\" is from the group *computer graphics*, \"`b`\" is from from the group *recreation autos*, and \"`c`\" is from group *recreation motorcycle*. **Exercise for part 2**: Calculate the: \n", 136 | "- (1) Euclidean distance\n", 137 | "- (2) Cosine distance \n", 138 | "- (3) Jaccard similarity \n", 139 | "\n", 140 | "between each pair of instances using the imported functions below. Remember that the Jaccard similarity is only for binary valued vectors, so convert vectors to binary using a threshold. \n", 141 | "\n", 142 | "**Question for part 2**: Which distance seems more appropriate to use for this data? **Why**?" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": 3, 148 | "metadata": {}, 149 | "outputs": [ 150 | { 151 | "name": "stdout", 152 | "output_type": "stream", 153 | "text": [ 154 | "Instance A is from class comp.graphics\n", 155 | "Instance B is from class rec.autos\n", 156 | "Instance C is from class rec.motorcycles\n", 157 | "\n", 158 | "\n", 159 | "Euclidean Distance\n", 160 | " ab: Placeholder ac: Placeholder bc: Placeholder\n", 161 | "Cosine Distance\n", 162 | " ab: Placeholder ac: Placeholder bc: Placeholder\n", 163 | "Jaccard Dissimilarity (vectors should be boolean values)\n", 164 | " ab: Placeholder ac: Placeholder bc: Placeholder\n", 165 | "\n", 166 | "\n", 167 | "The most appropriate distance is...\n", 168 | "Placeholder\n" 169 | ] 170 | } 171 | ], 172 | "source": [ 173 | "from scipy.spatial.distance import cosine\n", 174 | "from scipy.spatial.distance import euclidean\n", 175 | "from scipy.spatial.distance import jaccard\n", 176 | "import numpy as np\n", 177 | "\n", 178 | "# get first instance (comp)\n", 179 | "idx = 550\n", 180 | "a = ds.data[idx].todense()\n", 181 | "a_class = ds.target_names[ds.target[idx]]\n", 182 | "print('Instance A is from class', a_class)\n", 183 | "\n", 184 | "# get second instance (autos)\n", 185 | "idx = 4000\n", 186 | "b = ds.data[idx].todense()\n", 187 | "b_class = ds.target_names[ds.target[idx]]\n", 188 | "print('Instance B is from class', b_class)\n", 189 | "\n", 190 | "# get third instance (motorcycle)\n", 191 | "idx = 7000\n", 192 | "c = ds.data[idx].todense()\n", 193 | "c_class = ds.target_names[ds.target[idx]]\n", 194 | "print('Instance C is from class', c_class)\n", 195 | "\n", 196 | "# Enter distance comparison below for each pair of vectors:\n", 197 | "p = 'Placeholder'\n", 198 | "print('\\n\\nEuclidean Distance\\n ab:', p, 'ac:', p, 'bc:',p)\n", 199 | "print('Cosine Distance\\n ab:', p, 'ac:', p, 'bc:', p)\n", 200 | "print('Jaccard Dissimilarity (vectors should be boolean values)\\n ab:', p, 'ac:', p, 'bc:', p)\n", 201 | "\n", 202 | "print('\\n\\nThe most appropriate distance is...')\n", 203 | "print(p)" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": null, 209 | "metadata": { 210 | "collapsed": true 211 | }, 212 | "outputs": [], 213 | "source": [] 214 | } 215 | ], 216 | "metadata": { 217 | "anaconda-cloud": {}, 218 | "kernelspec": { 219 | "display_name": "Python 3", 220 | "language": "python", 221 | "name": "python3" 222 | }, 223 | "language_info": { 224 | "codemirror_mode": { 225 | "name": "ipython", 226 | "version": 3 227 | }, 228 | "file_extension": ".py", 229 | "mimetype": "text/x-python", 230 | "name": "python", 231 | "nbconvert_exporter": "python", 232 | "pygments_lexer": "ipython3", 233 | "version": "3.7.1" 234 | } 235 | }, 236 | "nbformat": 4, 237 | "nbformat_minor": 1 238 | } 239 | -------------------------------------------------------------------------------- /ICA5-PartA/titanic2.raw.rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jakemdrew/DataMiningNotebooks/5f1fa5d53dbe558224889e87e84fd784d7fba8b6/ICA5-PartA/titanic2.raw.rdata -------------------------------------------------------------------------------- /ICA5-PartA/titanic_raw.csv: -------------------------------------------------------------------------------- 1 | Class,Sex,Age,Survived 2 | 3rd,Male,Child,No 3 | 3rd,Male,Child,No 4 | 3rd,Male,Child,No 5 | 3rd,Male,Child,No 6 | 3rd,Male,Child,No 7 | 3rd,Male,Child,No 8 | 3rd,Male,Child,No 9 | 3rd,Male,Child,No 10 | 3rd,Male,Child,No 11 | 3rd,Male,Child,No 12 | 3rd,Male,Child,No 13 | 3rd,Male,Child,No 14 | 3rd,Male,Child,No 15 | 3rd,Male,Child,No 16 | 3rd,Male,Child,No 17 | 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3rd,Male,Adult,No 554 | 3rd,Male,Adult,No 555 | 3rd,Male,Adult,No 556 | 3rd,Male,Adult,No 557 | 3rd,Male,Adult,No 558 | 3rd,Male,Adult,No 559 | 3rd,Male,Adult,No 560 | 3rd,Male,Adult,No 561 | 3rd,Male,Adult,No 562 | 3rd,Male,Adult,No 563 | 3rd,Male,Adult,No 564 | 3rd,Male,Adult,No 565 | 3rd,Male,Adult,No 566 | 3rd,Male,Adult,No 567 | 3rd,Male,Adult,No 568 | 3rd,Male,Adult,No 569 | 3rd,Male,Adult,No 570 | 3rd,Male,Adult,No 571 | 3rd,Male,Adult,No 572 | 3rd,Male,Adult,No 573 | 3rd,Male,Adult,No 574 | 3rd,Male,Adult,No 575 | 3rd,Male,Adult,No 576 | 3rd,Male,Adult,No 577 | 3rd,Male,Adult,No 578 | 3rd,Male,Adult,No 579 | 3rd,Male,Adult,No 580 | 3rd,Male,Adult,No 581 | 3rd,Male,Adult,No 582 | 3rd,Male,Adult,No 583 | 3rd,Male,Adult,No 584 | 3rd,Male,Adult,No 585 | 3rd,Male,Adult,No 586 | 3rd,Male,Adult,No 587 | 3rd,Male,Adult,No 588 | 3rd,Male,Adult,No 589 | 3rd,Male,Adult,No 590 | 3rd,Male,Adult,No 591 | 3rd,Male,Adult,No 592 | 3rd,Male,Adult,No 593 | 3rd,Male,Adult,No 594 | 3rd,Male,Adult,No 595 | 3rd,Male,Adult,No 596 | 3rd,Male,Adult,No 597 | 3rd,Male,Adult,No 598 | 3rd,Male,Adult,No 599 | 3rd,Male,Adult,No 600 | 3rd,Male,Adult,No 601 | 3rd,Male,Adult,No 602 | 3rd,Male,Adult,No 603 | 3rd,Male,Adult,No 604 | 3rd,Male,Adult,No 605 | 3rd,Male,Adult,No 606 | 3rd,Male,Adult,No 607 | 3rd,Male,Adult,No 608 | 3rd,Male,Adult,No 609 | 3rd,Male,Adult,No 610 | 3rd,Male,Adult,No 611 | 3rd,Male,Adult,No 612 | 3rd,Male,Adult,No 613 | 3rd,Male,Adult,No 614 | 3rd,Male,Adult,No 615 | 3rd,Male,Adult,No 616 | 3rd,Male,Adult,No 617 | 3rd,Male,Adult,No 618 | 3rd,Male,Adult,No 619 | 3rd,Male,Adult,No 620 | 3rd,Male,Adult,No 621 | 3rd,Male,Adult,No 622 | 3rd,Male,Adult,No 623 | 3rd,Male,Adult,No 624 | 3rd,Male,Adult,No 625 | 3rd,Male,Adult,No 626 | 3rd,Male,Adult,No 627 | 3rd,Male,Adult,No 628 | 3rd,Male,Adult,No 629 | 3rd,Male,Adult,No 630 | 3rd,Male,Adult,No 631 | 3rd,Male,Adult,No 632 | 3rd,Male,Adult,No 633 | 3rd,Male,Adult,No 634 | 3rd,Male,Adult,No 635 | 3rd,Male,Adult,No 636 | 3rd,Male,Adult,No 637 | 3rd,Male,Adult,No 638 | 3rd,Male,Adult,No 639 | 3rd,Male,Adult,No 640 | 3rd,Male,Adult,No 641 | 3rd,Male,Adult,No 642 | 3rd,Male,Adult,No 643 | 3rd,Male,Adult,No 644 | 3rd,Male,Adult,No 645 | 3rd,Male,Adult,No 646 | 3rd,Male,Adult,No 647 | 3rd,Male,Adult,No 648 | 3rd,Male,Adult,No 649 | 3rd,Male,Adult,No 650 | 3rd,Male,Adult,No 651 | 3rd,Male,Adult,No 652 | 3rd,Male,Adult,No 653 | 3rd,Male,Adult,No 654 | 3rd,Male,Adult,No 655 | 3rd,Male,Adult,No 656 | 3rd,Male,Adult,No 657 | 3rd,Male,Adult,No 658 | 3rd,Male,Adult,No 659 | 3rd,Male,Adult,No 660 | 3rd,Male,Adult,No 661 | 3rd,Male,Adult,No 662 | 3rd,Male,Adult,No 663 | 3rd,Male,Adult,No 664 | 3rd,Male,Adult,No 665 | 3rd,Male,Adult,No 666 | 3rd,Male,Adult,No 667 | 3rd,Male,Adult,No 668 | 3rd,Male,Adult,No 669 | 3rd,Male,Adult,No 670 | 3rd,Male,Adult,No 671 | 3rd,Male,Adult,No 672 | 3rd,Male,Adult,No 673 | 3rd,Male,Adult,No 674 | 3rd,Male,Adult,No 675 | 3rd,Male,Adult,No 676 | 3rd,Male,Adult,No 677 | 3rd,Male,Adult,No 678 | 3rd,Male,Adult,No 679 | 3rd,Male,Adult,No 680 | 3rd,Male,Adult,No 681 | 3rd,Male,Adult,No 682 | 3rd,Male,Adult,No 683 | 3rd,Male,Adult,No 684 | 3rd,Male,Adult,No 685 | 3rd,Male,Adult,No 686 | 3rd,Male,Adult,No 687 | 3rd,Male,Adult,No 688 | 3rd,Male,Adult,No 689 | 3rd,Male,Adult,No 690 | 3rd,Male,Adult,No 691 | 3rd,Male,Adult,No 692 | 3rd,Male,Adult,No 693 | 3rd,Male,Adult,No 694 | 3rd,Male,Adult,No 695 | 3rd,Male,Adult,No 696 | 3rd,Male,Adult,No 697 | 3rd,Male,Adult,No 698 | 3rd,Male,Adult,No 699 | 3rd,Male,Adult,No 700 | 3rd,Male,Adult,No 701 | 3rd,Male,Adult,No 702 | 3rd,Male,Adult,No 703 | 3rd,Male,Adult,No 704 | 3rd,Male,Adult,No 705 | 3rd,Male,Adult,No 706 | 3rd,Male,Adult,No 707 | 3rd,Male,Adult,No 708 | 3rd,Male,Adult,No 709 | 3rd,Male,Adult,No 710 | 3rd,Male,Adult,No 711 | 3rd,Male,Adult,No 712 | 3rd,Male,Adult,No 713 | Crew,Male,Adult,No 714 | Crew,Male,Adult,No 715 | Crew,Male,Adult,No 716 | Crew,Male,Adult,No 717 | Crew,Male,Adult,No 718 | Crew,Male,Adult,No 719 | Crew,Male,Adult,No 720 | Crew,Male,Adult,No 721 | Crew,Male,Adult,No 722 | Crew,Male,Adult,No 723 | Crew,Male,Adult,No 724 | Crew,Male,Adult,No 725 | Crew,Male,Adult,No 726 | Crew,Male,Adult,No 727 | Crew,Male,Adult,No 728 | Crew,Male,Adult,No 729 | Crew,Male,Adult,No 730 | Crew,Male,Adult,No 731 | Crew,Male,Adult,No 732 | Crew,Male,Adult,No 733 | Crew,Male,Adult,No 734 | Crew,Male,Adult,No 735 | Crew,Male,Adult,No 736 | Crew,Male,Adult,No 737 | Crew,Male,Adult,No 738 | Crew,Male,Adult,No 739 | Crew,Male,Adult,No 740 | Crew,Male,Adult,No 741 | Crew,Male,Adult,No 742 | Crew,Male,Adult,No 743 | Crew,Male,Adult,No 744 | Crew,Male,Adult,No 745 | Crew,Male,Adult,No 746 | Crew,Male,Adult,No 747 | Crew,Male,Adult,No 748 | Crew,Male,Adult,No 749 | Crew,Male,Adult,No 750 | Crew,Male,Adult,No 751 | Crew,Male,Adult,No 752 | Crew,Male,Adult,No 753 | Crew,Male,Adult,No 754 | Crew,Male,Adult,No 755 | Crew,Male,Adult,No 756 | Crew,Male,Adult,No 757 | Crew,Male,Adult,No 758 | Crew,Male,Adult,No 759 | Crew,Male,Adult,No 760 | Crew,Male,Adult,No 761 | Crew,Male,Adult,No 762 | Crew,Male,Adult,No 763 | Crew,Male,Adult,No 764 | Crew,Male,Adult,No 765 | Crew,Male,Adult,No 766 | Crew,Male,Adult,No 767 | Crew,Male,Adult,No 768 | Crew,Male,Adult,No 769 | Crew,Male,Adult,No 770 | Crew,Male,Adult,No 771 | Crew,Male,Adult,No 772 | Crew,Male,Adult,No 773 | Crew,Male,Adult,No 774 | Crew,Male,Adult,No 775 | Crew,Male,Adult,No 776 | Crew,Male,Adult,No 777 | Crew,Male,Adult,No 778 | Crew,Male,Adult,No 779 | Crew,Male,Adult,No 780 | Crew,Male,Adult,No 781 | Crew,Male,Adult,No 782 | Crew,Male,Adult,No 783 | Crew,Male,Adult,No 784 | Crew,Male,Adult,No 785 | Crew,Male,Adult,No 786 | Crew,Male,Adult,No 787 | Crew,Male,Adult,No 788 | Crew,Male,Adult,No 789 | Crew,Male,Adult,No 790 | Crew,Male,Adult,No 791 | Crew,Male,Adult,No 792 | Crew,Male,Adult,No 793 | Crew,Male,Adult,No 794 | Crew,Male,Adult,No 795 | Crew,Male,Adult,No 796 | Crew,Male,Adult,No 797 | Crew,Male,Adult,No 798 | Crew,Male,Adult,No 799 | Crew,Male,Adult,No 800 | Crew,Male,Adult,No 801 | Crew,Male,Adult,No 802 | Crew,Male,Adult,No 803 | Crew,Male,Adult,No 804 | Crew,Male,Adult,No 805 | Crew,Male,Adult,No 806 | Crew,Male,Adult,No 807 | Crew,Male,Adult,No 808 | Crew,Male,Adult,No 809 | Crew,Male,Adult,No 810 | Crew,Male,Adult,No 811 | Crew,Male,Adult,No 812 | Crew,Male,Adult,No 813 | Crew,Male,Adult,No 814 | Crew,Male,Adult,No 815 | Crew,Male,Adult,No 816 | Crew,Male,Adult,No 817 | Crew,Male,Adult,No 818 | Crew,Male,Adult,No 819 | Crew,Male,Adult,No 820 | Crew,Male,Adult,No 821 | Crew,Male,Adult,No 822 | Crew,Male,Adult,No 823 | Crew,Male,Adult,No 824 | Crew,Male,Adult,No 825 | Crew,Male,Adult,No 826 | Crew,Male,Adult,No 827 | Crew,Male,Adult,No 828 | Crew,Male,Adult,No 829 | Crew,Male,Adult,No 830 | Crew,Male,Adult,No 831 | Crew,Male,Adult,No 832 | Crew,Male,Adult,No 833 | Crew,Male,Adult,No 834 | Crew,Male,Adult,No 835 | Crew,Male,Adult,No 836 | Crew,Male,Adult,No 837 | Crew,Male,Adult,No 838 | Crew,Male,Adult,No 839 | Crew,Male,Adult,No 840 | Crew,Male,Adult,No 841 | Crew,Male,Adult,No 842 | Crew,Male,Adult,No 843 | Crew,Male,Adult,No 844 | Crew,Male,Adult,No 845 | Crew,Male,Adult,No 846 | Crew,Male,Adult,No 847 | Crew,Male,Adult,No 848 | Crew,Male,Adult,No 849 | Crew,Male,Adult,No 850 | Crew,Male,Adult,No 851 | Crew,Male,Adult,No 852 | Crew,Male,Adult,No 853 | Crew,Male,Adult,No 854 | Crew,Male,Adult,No 855 | Crew,Male,Adult,No 856 | Crew,Male,Adult,No 857 | Crew,Male,Adult,No 858 | Crew,Male,Adult,No 859 | Crew,Male,Adult,No 860 | Crew,Male,Adult,No 861 | Crew,Male,Adult,No 862 | Crew,Male,Adult,No 863 | Crew,Male,Adult,No 864 | Crew,Male,Adult,No 865 | Crew,Male,Adult,No 866 | Crew,Male,Adult,No 867 | Crew,Male,Adult,No 868 | Crew,Male,Adult,No 869 | Crew,Male,Adult,No 870 | Crew,Male,Adult,No 871 | Crew,Male,Adult,No 872 | Crew,Male,Adult,No 873 | Crew,Male,Adult,No 874 | Crew,Male,Adult,No 875 | Crew,Male,Adult,No 876 | Crew,Male,Adult,No 877 | Crew,Male,Adult,No 878 | Crew,Male,Adult,No 879 | Crew,Male,Adult,No 880 | Crew,Male,Adult,No 881 | Crew,Male,Adult,No 882 | Crew,Male,Adult,No 883 | Crew,Male,Adult,No 884 | Crew,Male,Adult,No 885 | Crew,Male,Adult,No 886 | Crew,Male,Adult,No 887 | Crew,Male,Adult,No 888 | Crew,Male,Adult,No 889 | Crew,Male,Adult,No 890 | Crew,Male,Adult,No 891 | Crew,Male,Adult,No 892 | Crew,Male,Adult,No 893 | Crew,Male,Adult,No 894 | Crew,Male,Adult,No 895 | Crew,Male,Adult,No 896 | Crew,Male,Adult,No 897 | Crew,Male,Adult,No 898 | Crew,Male,Adult,No 899 | Crew,Male,Adult,No 900 | Crew,Male,Adult,No 901 | Crew,Male,Adult,No 902 | Crew,Male,Adult,No 903 | Crew,Male,Adult,No 904 | Crew,Male,Adult,No 905 | Crew,Male,Adult,No 906 | Crew,Male,Adult,No 907 | Crew,Male,Adult,No 908 | Crew,Male,Adult,No 909 | Crew,Male,Adult,No 910 | Crew,Male,Adult,No 911 | Crew,Male,Adult,No 912 | Crew,Male,Adult,No 913 | Crew,Male,Adult,No 914 | Crew,Male,Adult,No 915 | Crew,Male,Adult,No 916 | Crew,Male,Adult,No 917 | Crew,Male,Adult,No 918 | Crew,Male,Adult,No 919 | Crew,Male,Adult,No 920 | Crew,Male,Adult,No 921 | Crew,Male,Adult,No 922 | Crew,Male,Adult,No 923 | Crew,Male,Adult,No 924 | Crew,Male,Adult,No 925 | Crew,Male,Adult,No 926 | Crew,Male,Adult,No 927 | Crew,Male,Adult,No 928 | Crew,Male,Adult,No 929 | Crew,Male,Adult,No 930 | Crew,Male,Adult,No 931 | Crew,Male,Adult,No 932 | Crew,Male,Adult,No 933 | Crew,Male,Adult,No 934 | Crew,Male,Adult,No 935 | Crew,Male,Adult,No 936 | Crew,Male,Adult,No 937 | Crew,Male,Adult,No 938 | Crew,Male,Adult,No 939 | Crew,Male,Adult,No 940 | Crew,Male,Adult,No 941 | Crew,Male,Adult,No 942 | Crew,Male,Adult,No 943 | Crew,Male,Adult,No 944 | Crew,Male,Adult,No 945 | Crew,Male,Adult,No 946 | Crew,Male,Adult,No 947 | Crew,Male,Adult,No 948 | Crew,Male,Adult,No 949 | Crew,Male,Adult,No 950 | Crew,Male,Adult,No 951 | Crew,Male,Adult,No 952 | Crew,Male,Adult,No 953 | Crew,Male,Adult,No 954 | Crew,Male,Adult,No 955 | Crew,Male,Adult,No 956 | Crew,Male,Adult,No 957 | Crew,Male,Adult,No 958 | Crew,Male,Adult,No 959 | Crew,Male,Adult,No 960 | Crew,Male,Adult,No 961 | Crew,Male,Adult,No 962 | Crew,Male,Adult,No 963 | Crew,Male,Adult,No 964 | Crew,Male,Adult,No 965 | Crew,Male,Adult,No 966 | Crew,Male,Adult,No 967 | Crew,Male,Adult,No 968 | Crew,Male,Adult,No 969 | Crew,Male,Adult,No 970 | Crew,Male,Adult,No 971 | Crew,Male,Adult,No 972 | Crew,Male,Adult,No 973 | Crew,Male,Adult,No 974 | Crew,Male,Adult,No 975 | Crew,Male,Adult,No 976 | Crew,Male,Adult,No 977 | Crew,Male,Adult,No 978 | Crew,Male,Adult,No 979 | Crew,Male,Adult,No 980 | Crew,Male,Adult,No 981 | Crew,Male,Adult,No 982 | Crew,Male,Adult,No 983 | Crew,Male,Adult,No 984 | Crew,Male,Adult,No 985 | Crew,Male,Adult,No 986 | Crew,Male,Adult,No 987 | Crew,Male,Adult,No 988 | Crew,Male,Adult,No 989 | Crew,Male,Adult,No 990 | Crew,Male,Adult,No 991 | Crew,Male,Adult,No 992 | Crew,Male,Adult,No 993 | Crew,Male,Adult,No 994 | Crew,Male,Adult,No 995 | Crew,Male,Adult,No 996 | Crew,Male,Adult,No 997 | Crew,Male,Adult,No 998 | Crew,Male,Adult,No 999 | Crew,Male,Adult,No 1000 | Crew,Male,Adult,No 1001 | Crew,Male,Adult,No 1002 | Crew,Male,Adult,No 1003 | Crew,Male,Adult,No 1004 | Crew,Male,Adult,No 1005 | Crew,Male,Adult,No 1006 | Crew,Male,Adult,No 1007 | Crew,Male,Adult,No 1008 | Crew,Male,Adult,No 1009 | Crew,Male,Adult,No 1010 | Crew,Male,Adult,No 1011 | Crew,Male,Adult,No 1012 | Crew,Male,Adult,No 1013 | Crew,Male,Adult,No 1014 | Crew,Male,Adult,No 1015 | Crew,Male,Adult,No 1016 | Crew,Male,Adult,No 1017 | Crew,Male,Adult,No 1018 | Crew,Male,Adult,No 1019 | Crew,Male,Adult,No 1020 | Crew,Male,Adult,No 1021 | Crew,Male,Adult,No 1022 | Crew,Male,Adult,No 1023 | Crew,Male,Adult,No 1024 | Crew,Male,Adult,No 1025 | Crew,Male,Adult,No 1026 | Crew,Male,Adult,No 1027 | Crew,Male,Adult,No 1028 | Crew,Male,Adult,No 1029 | Crew,Male,Adult,No 1030 | Crew,Male,Adult,No 1031 | Crew,Male,Adult,No 1032 | Crew,Male,Adult,No 1033 | Crew,Male,Adult,No 1034 | Crew,Male,Adult,No 1035 | Crew,Male,Adult,No 1036 | Crew,Male,Adult,No 1037 | Crew,Male,Adult,No 1038 | Crew,Male,Adult,No 1039 | Crew,Male,Adult,No 1040 | Crew,Male,Adult,No 1041 | Crew,Male,Adult,No 1042 | Crew,Male,Adult,No 1043 | Crew,Male,Adult,No 1044 | Crew,Male,Adult,No 1045 | Crew,Male,Adult,No 1046 | Crew,Male,Adult,No 1047 | Crew,Male,Adult,No 1048 | Crew,Male,Adult,No 1049 | Crew,Male,Adult,No 1050 | Crew,Male,Adult,No 1051 | Crew,Male,Adult,No 1052 | Crew,Male,Adult,No 1053 | Crew,Male,Adult,No 1054 | Crew,Male,Adult,No 1055 | Crew,Male,Adult,No 1056 | Crew,Male,Adult,No 1057 | Crew,Male,Adult,No 1058 | Crew,Male,Adult,No 1059 | Crew,Male,Adult,No 1060 | Crew,Male,Adult,No 1061 | Crew,Male,Adult,No 1062 | Crew,Male,Adult,No 1063 | Crew,Male,Adult,No 1064 | Crew,Male,Adult,No 1065 | Crew,Male,Adult,No 1066 | Crew,Male,Adult,No 1067 | Crew,Male,Adult,No 1068 | Crew,Male,Adult,No 1069 | Crew,Male,Adult,No 1070 | Crew,Male,Adult,No 1071 | Crew,Male,Adult,No 1072 | Crew,Male,Adult,No 1073 | Crew,Male,Adult,No 1074 | Crew,Male,Adult,No 1075 | Crew,Male,Adult,No 1076 | Crew,Male,Adult,No 1077 | Crew,Male,Adult,No 1078 | Crew,Male,Adult,No 1079 | Crew,Male,Adult,No 1080 | Crew,Male,Adult,No 1081 | Crew,Male,Adult,No 1082 | Crew,Male,Adult,No 1083 | Crew,Male,Adult,No 1084 | Crew,Male,Adult,No 1085 | Crew,Male,Adult,No 1086 | Crew,Male,Adult,No 1087 | Crew,Male,Adult,No 1088 | Crew,Male,Adult,No 1089 | Crew,Male,Adult,No 1090 | Crew,Male,Adult,No 1091 | Crew,Male,Adult,No 1092 | Crew,Male,Adult,No 1093 | Crew,Male,Adult,No 1094 | Crew,Male,Adult,No 1095 | Crew,Male,Adult,No 1096 | Crew,Male,Adult,No 1097 | Crew,Male,Adult,No 1098 | Crew,Male,Adult,No 1099 | Crew,Male,Adult,No 1100 | Crew,Male,Adult,No 1101 | Crew,Male,Adult,No 1102 | Crew,Male,Adult,No 1103 | Crew,Male,Adult,No 1104 | Crew,Male,Adult,No 1105 | Crew,Male,Adult,No 1106 | Crew,Male,Adult,No 1107 | Crew,Male,Adult,No 1108 | Crew,Male,Adult,No 1109 | Crew,Male,Adult,No 1110 | Crew,Male,Adult,No 1111 | Crew,Male,Adult,No 1112 | Crew,Male,Adult,No 1113 | Crew,Male,Adult,No 1114 | Crew,Male,Adult,No 1115 | Crew,Male,Adult,No 1116 | Crew,Male,Adult,No 1117 | Crew,Male,Adult,No 1118 | Crew,Male,Adult,No 1119 | Crew,Male,Adult,No 1120 | Crew,Male,Adult,No 1121 | Crew,Male,Adult,No 1122 | Crew,Male,Adult,No 1123 | Crew,Male,Adult,No 1124 | Crew,Male,Adult,No 1125 | Crew,Male,Adult,No 1126 | Crew,Male,Adult,No 1127 | Crew,Male,Adult,No 1128 | Crew,Male,Adult,No 1129 | Crew,Male,Adult,No 1130 | Crew,Male,Adult,No 1131 | Crew,Male,Adult,No 1132 | Crew,Male,Adult,No 1133 | Crew,Male,Adult,No 1134 | Crew,Male,Adult,No 1135 | Crew,Male,Adult,No 1136 | Crew,Male,Adult,No 1137 | Crew,Male,Adult,No 1138 | Crew,Male,Adult,No 1139 | Crew,Male,Adult,No 1140 | Crew,Male,Adult,No 1141 | Crew,Male,Adult,No 1142 | Crew,Male,Adult,No 1143 | Crew,Male,Adult,No 1144 | Crew,Male,Adult,No 1145 | Crew,Male,Adult,No 1146 | Crew,Male,Adult,No 1147 | Crew,Male,Adult,No 1148 | Crew,Male,Adult,No 1149 | Crew,Male,Adult,No 1150 | Crew,Male,Adult,No 1151 | Crew,Male,Adult,No 1152 | Crew,Male,Adult,No 1153 | Crew,Male,Adult,No 1154 | Crew,Male,Adult,No 1155 | Crew,Male,Adult,No 1156 | Crew,Male,Adult,No 1157 | Crew,Male,Adult,No 1158 | Crew,Male,Adult,No 1159 | Crew,Male,Adult,No 1160 | Crew,Male,Adult,No 1161 | Crew,Male,Adult,No 1162 | Crew,Male,Adult,No 1163 | Crew,Male,Adult,No 1164 | Crew,Male,Adult,No 1165 | Crew,Male,Adult,No 1166 | Crew,Male,Adult,No 1167 | Crew,Male,Adult,No 1168 | Crew,Male,Adult,No 1169 | Crew,Male,Adult,No 1170 | Crew,Male,Adult,No 1171 | Crew,Male,Adult,No 1172 | Crew,Male,Adult,No 1173 | Crew,Male,Adult,No 1174 | Crew,Male,Adult,No 1175 | Crew,Male,Adult,No 1176 | Crew,Male,Adult,No 1177 | Crew,Male,Adult,No 1178 | Crew,Male,Adult,No 1179 | Crew,Male,Adult,No 1180 | Crew,Male,Adult,No 1181 | Crew,Male,Adult,No 1182 | Crew,Male,Adult,No 1183 | Crew,Male,Adult,No 1184 | Crew,Male,Adult,No 1185 | Crew,Male,Adult,No 1186 | Crew,Male,Adult,No 1187 | Crew,Male,Adult,No 1188 | Crew,Male,Adult,No 1189 | Crew,Male,Adult,No 1190 | Crew,Male,Adult,No 1191 | Crew,Male,Adult,No 1192 | Crew,Male,Adult,No 1193 | Crew,Male,Adult,No 1194 | Crew,Male,Adult,No 1195 | Crew,Male,Adult,No 1196 | Crew,Male,Adult,No 1197 | Crew,Male,Adult,No 1198 | Crew,Male,Adult,No 1199 | Crew,Male,Adult,No 1200 | Crew,Male,Adult,No 1201 | Crew,Male,Adult,No 1202 | Crew,Male,Adult,No 1203 | Crew,Male,Adult,No 1204 | Crew,Male,Adult,No 1205 | Crew,Male,Adult,No 1206 | Crew,Male,Adult,No 1207 | Crew,Male,Adult,No 1208 | Crew,Male,Adult,No 1209 | Crew,Male,Adult,No 1210 | Crew,Male,Adult,No 1211 | Crew,Male,Adult,No 1212 | Crew,Male,Adult,No 1213 | Crew,Male,Adult,No 1214 | Crew,Male,Adult,No 1215 | Crew,Male,Adult,No 1216 | Crew,Male,Adult,No 1217 | Crew,Male,Adult,No 1218 | Crew,Male,Adult,No 1219 | Crew,Male,Adult,No 1220 | Crew,Male,Adult,No 1221 | Crew,Male,Adult,No 1222 | Crew,Male,Adult,No 1223 | Crew,Male,Adult,No 1224 | Crew,Male,Adult,No 1225 | Crew,Male,Adult,No 1226 | Crew,Male,Adult,No 1227 | Crew,Male,Adult,No 1228 | Crew,Male,Adult,No 1229 | Crew,Male,Adult,No 1230 | Crew,Male,Adult,No 1231 | Crew,Male,Adult,No 1232 | Crew,Male,Adult,No 1233 | Crew,Male,Adult,No 1234 | Crew,Male,Adult,No 1235 | Crew,Male,Adult,No 1236 | Crew,Male,Adult,No 1237 | Crew,Male,Adult,No 1238 | Crew,Male,Adult,No 1239 | Crew,Male,Adult,No 1240 | Crew,Male,Adult,No 1241 | Crew,Male,Adult,No 1242 | Crew,Male,Adult,No 1243 | Crew,Male,Adult,No 1244 | Crew,Male,Adult,No 1245 | Crew,Male,Adult,No 1246 | Crew,Male,Adult,No 1247 | Crew,Male,Adult,No 1248 | Crew,Male,Adult,No 1249 | Crew,Male,Adult,No 1250 | Crew,Male,Adult,No 1251 | Crew,Male,Adult,No 1252 | Crew,Male,Adult,No 1253 | Crew,Male,Adult,No 1254 | Crew,Male,Adult,No 1255 | Crew,Male,Adult,No 1256 | Crew,Male,Adult,No 1257 | Crew,Male,Adult,No 1258 | Crew,Male,Adult,No 1259 | Crew,Male,Adult,No 1260 | Crew,Male,Adult,No 1261 | Crew,Male,Adult,No 1262 | Crew,Male,Adult,No 1263 | Crew,Male,Adult,No 1264 | Crew,Male,Adult,No 1265 | Crew,Male,Adult,No 1266 | Crew,Male,Adult,No 1267 | Crew,Male,Adult,No 1268 | Crew,Male,Adult,No 1269 | Crew,Male,Adult,No 1270 | Crew,Male,Adult,No 1271 | Crew,Male,Adult,No 1272 | Crew,Male,Adult,No 1273 | Crew,Male,Adult,No 1274 | Crew,Male,Adult,No 1275 | Crew,Male,Adult,No 1276 | Crew,Male,Adult,No 1277 | Crew,Male,Adult,No 1278 | Crew,Male,Adult,No 1279 | Crew,Male,Adult,No 1280 | Crew,Male,Adult,No 1281 | Crew,Male,Adult,No 1282 | Crew,Male,Adult,No 1283 | Crew,Male,Adult,No 1284 | Crew,Male,Adult,No 1285 | Crew,Male,Adult,No 1286 | Crew,Male,Adult,No 1287 | Crew,Male,Adult,No 1288 | Crew,Male,Adult,No 1289 | Crew,Male,Adult,No 1290 | Crew,Male,Adult,No 1291 | Crew,Male,Adult,No 1292 | Crew,Male,Adult,No 1293 | Crew,Male,Adult,No 1294 | Crew,Male,Adult,No 1295 | Crew,Male,Adult,No 1296 | Crew,Male,Adult,No 1297 | Crew,Male,Adult,No 1298 | Crew,Male,Adult,No 1299 | Crew,Male,Adult,No 1300 | Crew,Male,Adult,No 1301 | Crew,Male,Adult,No 1302 | Crew,Male,Adult,No 1303 | Crew,Male,Adult,No 1304 | Crew,Male,Adult,No 1305 | Crew,Male,Adult,No 1306 | Crew,Male,Adult,No 1307 | Crew,Male,Adult,No 1308 | Crew,Male,Adult,No 1309 | Crew,Male,Adult,No 1310 | Crew,Male,Adult,No 1311 | Crew,Male,Adult,No 1312 | Crew,Male,Adult,No 1313 | Crew,Male,Adult,No 1314 | Crew,Male,Adult,No 1315 | Crew,Male,Adult,No 1316 | Crew,Male,Adult,No 1317 | Crew,Male,Adult,No 1318 | Crew,Male,Adult,No 1319 | Crew,Male,Adult,No 1320 | Crew,Male,Adult,No 1321 | Crew,Male,Adult,No 1322 | Crew,Male,Adult,No 1323 | Crew,Male,Adult,No 1324 | Crew,Male,Adult,No 1325 | Crew,Male,Adult,No 1326 | Crew,Male,Adult,No 1327 | Crew,Male,Adult,No 1328 | Crew,Male,Adult,No 1329 | Crew,Male,Adult,No 1330 | Crew,Male,Adult,No 1331 | Crew,Male,Adult,No 1332 | Crew,Male,Adult,No 1333 | Crew,Male,Adult,No 1334 | Crew,Male,Adult,No 1335 | Crew,Male,Adult,No 1336 | Crew,Male,Adult,No 1337 | Crew,Male,Adult,No 1338 | Crew,Male,Adult,No 1339 | Crew,Male,Adult,No 1340 | Crew,Male,Adult,No 1341 | Crew,Male,Adult,No 1342 | Crew,Male,Adult,No 1343 | Crew,Male,Adult,No 1344 | Crew,Male,Adult,No 1345 | Crew,Male,Adult,No 1346 | Crew,Male,Adult,No 1347 | Crew,Male,Adult,No 1348 | Crew,Male,Adult,No 1349 | Crew,Male,Adult,No 1350 | Crew,Male,Adult,No 1351 | Crew,Male,Adult,No 1352 | Crew,Male,Adult,No 1353 | Crew,Male,Adult,No 1354 | Crew,Male,Adult,No 1355 | Crew,Male,Adult,No 1356 | Crew,Male,Adult,No 1357 | Crew,Male,Adult,No 1358 | Crew,Male,Adult,No 1359 | Crew,Male,Adult,No 1360 | Crew,Male,Adult,No 1361 | Crew,Male,Adult,No 1362 | Crew,Male,Adult,No 1363 | Crew,Male,Adult,No 1364 | Crew,Male,Adult,No 1365 | Crew,Male,Adult,No 1366 | Crew,Male,Adult,No 1367 | Crew,Male,Adult,No 1368 | Crew,Male,Adult,No 1369 | Crew,Male,Adult,No 1370 | Crew,Male,Adult,No 1371 | Crew,Male,Adult,No 1372 | Crew,Male,Adult,No 1373 | Crew,Male,Adult,No 1374 | Crew,Male,Adult,No 1375 | Crew,Male,Adult,No 1376 | Crew,Male,Adult,No 1377 | Crew,Male,Adult,No 1378 | Crew,Male,Adult,No 1379 | Crew,Male,Adult,No 1380 | Crew,Male,Adult,No 1381 | Crew,Male,Adult,No 1382 | Crew,Male,Adult,No 1383 | 1st,Female,Adult,No 1384 | 1st,Female,Adult,No 1385 | 1st,Female,Adult,No 1386 | 1st,Female,Adult,No 1387 | 2nd,Female,Adult,No 1388 | 2nd,Female,Adult,No 1389 | 2nd,Female,Adult,No 1390 | 2nd,Female,Adult,No 1391 | 2nd,Female,Adult,No 1392 | 2nd,Female,Adult,No 1393 | 2nd,Female,Adult,No 1394 | 2nd,Female,Adult,No 1395 | 2nd,Female,Adult,No 1396 | 2nd,Female,Adult,No 1397 | 2nd,Female,Adult,No 1398 | 2nd,Female,Adult,No 1399 | 2nd,Female,Adult,No 1400 | 3rd,Female,Adult,No 1401 | 3rd,Female,Adult,No 1402 | 3rd,Female,Adult,No 1403 | 3rd,Female,Adult,No 1404 | 3rd,Female,Adult,No 1405 | 3rd,Female,Adult,No 1406 | 3rd,Female,Adult,No 1407 | 3rd,Female,Adult,No 1408 | 3rd,Female,Adult,No 1409 | 3rd,Female,Adult,No 1410 | 3rd,Female,Adult,No 1411 | 3rd,Female,Adult,No 1412 | 3rd,Female,Adult,No 1413 | 3rd,Female,Adult,No 1414 | 3rd,Female,Adult,No 1415 | 3rd,Female,Adult,No 1416 | 3rd,Female,Adult,No 1417 | 3rd,Female,Adult,No 1418 | 3rd,Female,Adult,No 1419 | 3rd,Female,Adult,No 1420 | 3rd,Female,Adult,No 1421 | 3rd,Female,Adult,No 1422 | 3rd,Female,Adult,No 1423 | 3rd,Female,Adult,No 1424 | 3rd,Female,Adult,No 1425 | 3rd,Female,Adult,No 1426 | 3rd,Female,Adult,No 1427 | 3rd,Female,Adult,No 1428 | 3rd,Female,Adult,No 1429 | 3rd,Female,Adult,No 1430 | 3rd,Female,Adult,No 1431 | 3rd,Female,Adult,No 1432 | 3rd,Female,Adult,No 1433 | 3rd,Female,Adult,No 1434 | 3rd,Female,Adult,No 1435 | 3rd,Female,Adult,No 1436 | 3rd,Female,Adult,No 1437 | 3rd,Female,Adult,No 1438 | 3rd,Female,Adult,No 1439 | 3rd,Female,Adult,No 1440 | 3rd,Female,Adult,No 1441 | 3rd,Female,Adult,No 1442 | 3rd,Female,Adult,No 1443 | 3rd,Female,Adult,No 1444 | 3rd,Female,Adult,No 1445 | 3rd,Female,Adult,No 1446 | 3rd,Female,Adult,No 1447 | 3rd,Female,Adult,No 1448 | 3rd,Female,Adult,No 1449 | 3rd,Female,Adult,No 1450 | 3rd,Female,Adult,No 1451 | 3rd,Female,Adult,No 1452 | 3rd,Female,Adult,No 1453 | 3rd,Female,Adult,No 1454 | 3rd,Female,Adult,No 1455 | 3rd,Female,Adult,No 1456 | 3rd,Female,Adult,No 1457 | 3rd,Female,Adult,No 1458 | 3rd,Female,Adult,No 1459 | 3rd,Female,Adult,No 1460 | 3rd,Female,Adult,No 1461 | 3rd,Female,Adult,No 1462 | 3rd,Female,Adult,No 1463 | 3rd,Female,Adult,No 1464 | 3rd,Female,Adult,No 1465 | 3rd,Female,Adult,No 1466 | 3rd,Female,Adult,No 1467 | 3rd,Female,Adult,No 1468 | 3rd,Female,Adult,No 1469 | 3rd,Female,Adult,No 1470 | 3rd,Female,Adult,No 1471 | 3rd,Female,Adult,No 1472 | 3rd,Female,Adult,No 1473 | 3rd,Female,Adult,No 1474 | 3rd,Female,Adult,No 1475 | 3rd,Female,Adult,No 1476 | 3rd,Female,Adult,No 1477 | 3rd,Female,Adult,No 1478 | 3rd,Female,Adult,No 1479 | 3rd,Female,Adult,No 1480 | 3rd,Female,Adult,No 1481 | 3rd,Female,Adult,No 1482 | 3rd,Female,Adult,No 1483 | 3rd,Female,Adult,No 1484 | 3rd,Female,Adult,No 1485 | 3rd,Female,Adult,No 1486 | 3rd,Female,Adult,No 1487 | 3rd,Female,Adult,No 1488 | 3rd,Female,Adult,No 1489 | Crew,Female,Adult,No 1490 | Crew,Female,Adult,No 1491 | Crew,Female,Adult,No 1492 | 1st,Male,Child,Yes 1493 | 1st,Male,Child,Yes 1494 | 1st,Male,Child,Yes 1495 | 1st,Male,Child,Yes 1496 | 1st,Male,Child,Yes 1497 | 2nd,Male,Child,Yes 1498 | 2nd,Male,Child,Yes 1499 | 2nd,Male,Child,Yes 1500 | 2nd,Male,Child,Yes 1501 | 2nd,Male,Child,Yes 1502 | 2nd,Male,Child,Yes 1503 | 2nd,Male,Child,Yes 1504 | 2nd,Male,Child,Yes 1505 | 2nd,Male,Child,Yes 1506 | 2nd,Male,Child,Yes 1507 | 2nd,Male,Child,Yes 1508 | 3rd,Male,Child,Yes 1509 | 3rd,Male,Child,Yes 1510 | 3rd,Male,Child,Yes 1511 | 3rd,Male,Child,Yes 1512 | 3rd,Male,Child,Yes 1513 | 3rd,Male,Child,Yes 1514 | 3rd,Male,Child,Yes 1515 | 3rd,Male,Child,Yes 1516 | 3rd,Male,Child,Yes 1517 | 3rd,Male,Child,Yes 1518 | 3rd,Male,Child,Yes 1519 | 3rd,Male,Child,Yes 1520 | 3rd,Male,Child,Yes 1521 | 1st,Female,Child,Yes 1522 | 2nd,Female,Child,Yes 1523 | 2nd,Female,Child,Yes 1524 | 2nd,Female,Child,Yes 1525 | 2nd,Female,Child,Yes 1526 | 2nd,Female,Child,Yes 1527 | 2nd,Female,Child,Yes 1528 | 2nd,Female,Child,Yes 1529 | 2nd,Female,Child,Yes 1530 | 2nd,Female,Child,Yes 1531 | 2nd,Female,Child,Yes 1532 | 2nd,Female,Child,Yes 1533 | 2nd,Female,Child,Yes 1534 | 2nd,Female,Child,Yes 1535 | 3rd,Female,Child,Yes 1536 | 3rd,Female,Child,Yes 1537 | 3rd,Female,Child,Yes 1538 | 3rd,Female,Child,Yes 1539 | 3rd,Female,Child,Yes 1540 | 3rd,Female,Child,Yes 1541 | 3rd,Female,Child,Yes 1542 | 3rd,Female,Child,Yes 1543 | 3rd,Female,Child,Yes 1544 | 3rd,Female,Child,Yes 1545 | 3rd,Female,Child,Yes 1546 | 3rd,Female,Child,Yes 1547 | 3rd,Female,Child,Yes 1548 | 3rd,Female,Child,Yes 1549 | 1st,Male,Adult,Yes 1550 | 1st,Male,Adult,Yes 1551 | 1st,Male,Adult,Yes 1552 | 1st,Male,Adult,Yes 1553 | 1st,Male,Adult,Yes 1554 | 1st,Male,Adult,Yes 1555 | 1st,Male,Adult,Yes 1556 | 1st,Male,Adult,Yes 1557 | 1st,Male,Adult,Yes 1558 | 1st,Male,Adult,Yes 1559 | 1st,Male,Adult,Yes 1560 | 1st,Male,Adult,Yes 1561 | 1st,Male,Adult,Yes 1562 | 1st,Male,Adult,Yes 1563 | 1st,Male,Adult,Yes 1564 | 1st,Male,Adult,Yes 1565 | 1st,Male,Adult,Yes 1566 | 1st,Male,Adult,Yes 1567 | 1st,Male,Adult,Yes 1568 | 1st,Male,Adult,Yes 1569 | 1st,Male,Adult,Yes 1570 | 1st,Male,Adult,Yes 1571 | 1st,Male,Adult,Yes 1572 | 1st,Male,Adult,Yes 1573 | 1st,Male,Adult,Yes 1574 | 1st,Male,Adult,Yes 1575 | 1st,Male,Adult,Yes 1576 | 1st,Male,Adult,Yes 1577 | 1st,Male,Adult,Yes 1578 | 1st,Male,Adult,Yes 1579 | 1st,Male,Adult,Yes 1580 | 1st,Male,Adult,Yes 1581 | 1st,Male,Adult,Yes 1582 | 1st,Male,Adult,Yes 1583 | 1st,Male,Adult,Yes 1584 | 1st,Male,Adult,Yes 1585 | 1st,Male,Adult,Yes 1586 | 1st,Male,Adult,Yes 1587 | 1st,Male,Adult,Yes 1588 | 1st,Male,Adult,Yes 1589 | 1st,Male,Adult,Yes 1590 | 1st,Male,Adult,Yes 1591 | 1st,Male,Adult,Yes 1592 | 1st,Male,Adult,Yes 1593 | 1st,Male,Adult,Yes 1594 | 1st,Male,Adult,Yes 1595 | 1st,Male,Adult,Yes 1596 | 1st,Male,Adult,Yes 1597 | 1st,Male,Adult,Yes 1598 | 1st,Male,Adult,Yes 1599 | 1st,Male,Adult,Yes 1600 | 1st,Male,Adult,Yes 1601 | 1st,Male,Adult,Yes 1602 | 1st,Male,Adult,Yes 1603 | 1st,Male,Adult,Yes 1604 | 1st,Male,Adult,Yes 1605 | 1st,Male,Adult,Yes 1606 | 2nd,Male,Adult,Yes 1607 | 2nd,Male,Adult,Yes 1608 | 2nd,Male,Adult,Yes 1609 | 2nd,Male,Adult,Yes 1610 | 2nd,Male,Adult,Yes 1611 | 2nd,Male,Adult,Yes 1612 | 2nd,Male,Adult,Yes 1613 | 2nd,Male,Adult,Yes 1614 | 2nd,Male,Adult,Yes 1615 | 2nd,Male,Adult,Yes 1616 | 2nd,Male,Adult,Yes 1617 | 2nd,Male,Adult,Yes 1618 | 2nd,Male,Adult,Yes 1619 | 2nd,Male,Adult,Yes 1620 | 3rd,Male,Adult,Yes 1621 | 3rd,Male,Adult,Yes 1622 | 3rd,Male,Adult,Yes 1623 | 3rd,Male,Adult,Yes 1624 | 3rd,Male,Adult,Yes 1625 | 3rd,Male,Adult,Yes 1626 | 3rd,Male,Adult,Yes 1627 | 3rd,Male,Adult,Yes 1628 | 3rd,Male,Adult,Yes 1629 | 3rd,Male,Adult,Yes 1630 | 3rd,Male,Adult,Yes 1631 | 3rd,Male,Adult,Yes 1632 | 3rd,Male,Adult,Yes 1633 | 3rd,Male,Adult,Yes 1634 | 3rd,Male,Adult,Yes 1635 | 3rd,Male,Adult,Yes 1636 | 3rd,Male,Adult,Yes 1637 | 3rd,Male,Adult,Yes 1638 | 3rd,Male,Adult,Yes 1639 | 3rd,Male,Adult,Yes 1640 | 3rd,Male,Adult,Yes 1641 | 3rd,Male,Adult,Yes 1642 | 3rd,Male,Adult,Yes 1643 | 3rd,Male,Adult,Yes 1644 | 3rd,Male,Adult,Yes 1645 | 3rd,Male,Adult,Yes 1646 | 3rd,Male,Adult,Yes 1647 | 3rd,Male,Adult,Yes 1648 | 3rd,Male,Adult,Yes 1649 | 3rd,Male,Adult,Yes 1650 | 3rd,Male,Adult,Yes 1651 | 3rd,Male,Adult,Yes 1652 | 3rd,Male,Adult,Yes 1653 | 3rd,Male,Adult,Yes 1654 | 3rd,Male,Adult,Yes 1655 | 3rd,Male,Adult,Yes 1656 | 3rd,Male,Adult,Yes 1657 | 3rd,Male,Adult,Yes 1658 | 3rd,Male,Adult,Yes 1659 | 3rd,Male,Adult,Yes 1660 | 3rd,Male,Adult,Yes 1661 | 3rd,Male,Adult,Yes 1662 | 3rd,Male,Adult,Yes 1663 | 3rd,Male,Adult,Yes 1664 | 3rd,Male,Adult,Yes 1665 | 3rd,Male,Adult,Yes 1666 | 3rd,Male,Adult,Yes 1667 | 3rd,Male,Adult,Yes 1668 | 3rd,Male,Adult,Yes 1669 | 3rd,Male,Adult,Yes 1670 | 3rd,Male,Adult,Yes 1671 | 3rd,Male,Adult,Yes 1672 | 3rd,Male,Adult,Yes 1673 | 3rd,Male,Adult,Yes 1674 | 3rd,Male,Adult,Yes 1675 | 3rd,Male,Adult,Yes 1676 | 3rd,Male,Adult,Yes 1677 | 3rd,Male,Adult,Yes 1678 | 3rd,Male,Adult,Yes 1679 | 3rd,Male,Adult,Yes 1680 | 3rd,Male,Adult,Yes 1681 | 3rd,Male,Adult,Yes 1682 | 3rd,Male,Adult,Yes 1683 | 3rd,Male,Adult,Yes 1684 | 3rd,Male,Adult,Yes 1685 | 3rd,Male,Adult,Yes 1686 | 3rd,Male,Adult,Yes 1687 | 3rd,Male,Adult,Yes 1688 | 3rd,Male,Adult,Yes 1689 | 3rd,Male,Adult,Yes 1690 | 3rd,Male,Adult,Yes 1691 | 3rd,Male,Adult,Yes 1692 | 3rd,Male,Adult,Yes 1693 | 3rd,Male,Adult,Yes 1694 | 3rd,Male,Adult,Yes 1695 | Crew,Male,Adult,Yes 1696 | Crew,Male,Adult,Yes 1697 | Crew,Male,Adult,Yes 1698 | Crew,Male,Adult,Yes 1699 | Crew,Male,Adult,Yes 1700 | Crew,Male,Adult,Yes 1701 | Crew,Male,Adult,Yes 1702 | Crew,Male,Adult,Yes 1703 | Crew,Male,Adult,Yes 1704 | Crew,Male,Adult,Yes 1705 | Crew,Male,Adult,Yes 1706 | Crew,Male,Adult,Yes 1707 | Crew,Male,Adult,Yes 1708 | Crew,Male,Adult,Yes 1709 | Crew,Male,Adult,Yes 1710 | Crew,Male,Adult,Yes 1711 | Crew,Male,Adult,Yes 1712 | Crew,Male,Adult,Yes 1713 | Crew,Male,Adult,Yes 1714 | Crew,Male,Adult,Yes 1715 | Crew,Male,Adult,Yes 1716 | Crew,Male,Adult,Yes 1717 | Crew,Male,Adult,Yes 1718 | Crew,Male,Adult,Yes 1719 | Crew,Male,Adult,Yes 1720 | Crew,Male,Adult,Yes 1721 | Crew,Male,Adult,Yes 1722 | Crew,Male,Adult,Yes 1723 | Crew,Male,Adult,Yes 1724 | Crew,Male,Adult,Yes 1725 | Crew,Male,Adult,Yes 1726 | Crew,Male,Adult,Yes 1727 | Crew,Male,Adult,Yes 1728 | Crew,Male,Adult,Yes 1729 | Crew,Male,Adult,Yes 1730 | Crew,Male,Adult,Yes 1731 | Crew,Male,Adult,Yes 1732 | Crew,Male,Adult,Yes 1733 | Crew,Male,Adult,Yes 1734 | Crew,Male,Adult,Yes 1735 | Crew,Male,Adult,Yes 1736 | Crew,Male,Adult,Yes 1737 | Crew,Male,Adult,Yes 1738 | Crew,Male,Adult,Yes 1739 | Crew,Male,Adult,Yes 1740 | Crew,Male,Adult,Yes 1741 | Crew,Male,Adult,Yes 1742 | Crew,Male,Adult,Yes 1743 | Crew,Male,Adult,Yes 1744 | Crew,Male,Adult,Yes 1745 | Crew,Male,Adult,Yes 1746 | Crew,Male,Adult,Yes 1747 | Crew,Male,Adult,Yes 1748 | Crew,Male,Adult,Yes 1749 | Crew,Male,Adult,Yes 1750 | Crew,Male,Adult,Yes 1751 | Crew,Male,Adult,Yes 1752 | Crew,Male,Adult,Yes 1753 | Crew,Male,Adult,Yes 1754 | Crew,Male,Adult,Yes 1755 | Crew,Male,Adult,Yes 1756 | Crew,Male,Adult,Yes 1757 | Crew,Male,Adult,Yes 1758 | Crew,Male,Adult,Yes 1759 | Crew,Male,Adult,Yes 1760 | Crew,Male,Adult,Yes 1761 | Crew,Male,Adult,Yes 1762 | Crew,Male,Adult,Yes 1763 | Crew,Male,Adult,Yes 1764 | Crew,Male,Adult,Yes 1765 | Crew,Male,Adult,Yes 1766 | Crew,Male,Adult,Yes 1767 | Crew,Male,Adult,Yes 1768 | Crew,Male,Adult,Yes 1769 | Crew,Male,Adult,Yes 1770 | Crew,Male,Adult,Yes 1771 | Crew,Male,Adult,Yes 1772 | Crew,Male,Adult,Yes 1773 | Crew,Male,Adult,Yes 1774 | Crew,Male,Adult,Yes 1775 | Crew,Male,Adult,Yes 1776 | Crew,Male,Adult,Yes 1777 | Crew,Male,Adult,Yes 1778 | Crew,Male,Adult,Yes 1779 | Crew,Male,Adult,Yes 1780 | Crew,Male,Adult,Yes 1781 | Crew,Male,Adult,Yes 1782 | Crew,Male,Adult,Yes 1783 | Crew,Male,Adult,Yes 1784 | Crew,Male,Adult,Yes 1785 | Crew,Male,Adult,Yes 1786 | Crew,Male,Adult,Yes 1787 | Crew,Male,Adult,Yes 1788 | Crew,Male,Adult,Yes 1789 | Crew,Male,Adult,Yes 1790 | Crew,Male,Adult,Yes 1791 | Crew,Male,Adult,Yes 1792 | Crew,Male,Adult,Yes 1793 | Crew,Male,Adult,Yes 1794 | Crew,Male,Adult,Yes 1795 | Crew,Male,Adult,Yes 1796 | Crew,Male,Adult,Yes 1797 | Crew,Male,Adult,Yes 1798 | Crew,Male,Adult,Yes 1799 | Crew,Male,Adult,Yes 1800 | Crew,Male,Adult,Yes 1801 | Crew,Male,Adult,Yes 1802 | Crew,Male,Adult,Yes 1803 | Crew,Male,Adult,Yes 1804 | Crew,Male,Adult,Yes 1805 | Crew,Male,Adult,Yes 1806 | Crew,Male,Adult,Yes 1807 | Crew,Male,Adult,Yes 1808 | Crew,Male,Adult,Yes 1809 | Crew,Male,Adult,Yes 1810 | Crew,Male,Adult,Yes 1811 | Crew,Male,Adult,Yes 1812 | Crew,Male,Adult,Yes 1813 | Crew,Male,Adult,Yes 1814 | Crew,Male,Adult,Yes 1815 | Crew,Male,Adult,Yes 1816 | Crew,Male,Adult,Yes 1817 | Crew,Male,Adult,Yes 1818 | Crew,Male,Adult,Yes 1819 | Crew,Male,Adult,Yes 1820 | Crew,Male,Adult,Yes 1821 | Crew,Male,Adult,Yes 1822 | Crew,Male,Adult,Yes 1823 | Crew,Male,Adult,Yes 1824 | Crew,Male,Adult,Yes 1825 | Crew,Male,Adult,Yes 1826 | Crew,Male,Adult,Yes 1827 | Crew,Male,Adult,Yes 1828 | Crew,Male,Adult,Yes 1829 | Crew,Male,Adult,Yes 1830 | Crew,Male,Adult,Yes 1831 | Crew,Male,Adult,Yes 1832 | Crew,Male,Adult,Yes 1833 | Crew,Male,Adult,Yes 1834 | Crew,Male,Adult,Yes 1835 | Crew,Male,Adult,Yes 1836 | Crew,Male,Adult,Yes 1837 | Crew,Male,Adult,Yes 1838 | Crew,Male,Adult,Yes 1839 | Crew,Male,Adult,Yes 1840 | Crew,Male,Adult,Yes 1841 | Crew,Male,Adult,Yes 1842 | Crew,Male,Adult,Yes 1843 | Crew,Male,Adult,Yes 1844 | Crew,Male,Adult,Yes 1845 | Crew,Male,Adult,Yes 1846 | Crew,Male,Adult,Yes 1847 | Crew,Male,Adult,Yes 1848 | Crew,Male,Adult,Yes 1849 | Crew,Male,Adult,Yes 1850 | Crew,Male,Adult,Yes 1851 | Crew,Male,Adult,Yes 1852 | Crew,Male,Adult,Yes 1853 | Crew,Male,Adult,Yes 1854 | Crew,Male,Adult,Yes 1855 | Crew,Male,Adult,Yes 1856 | Crew,Male,Adult,Yes 1857 | Crew,Male,Adult,Yes 1858 | Crew,Male,Adult,Yes 1859 | Crew,Male,Adult,Yes 1860 | Crew,Male,Adult,Yes 1861 | Crew,Male,Adult,Yes 1862 | Crew,Male,Adult,Yes 1863 | Crew,Male,Adult,Yes 1864 | Crew,Male,Adult,Yes 1865 | Crew,Male,Adult,Yes 1866 | Crew,Male,Adult,Yes 1867 | Crew,Male,Adult,Yes 1868 | Crew,Male,Adult,Yes 1869 | Crew,Male,Adult,Yes 1870 | Crew,Male,Adult,Yes 1871 | Crew,Male,Adult,Yes 1872 | Crew,Male,Adult,Yes 1873 | Crew,Male,Adult,Yes 1874 | Crew,Male,Adult,Yes 1875 | Crew,Male,Adult,Yes 1876 | Crew,Male,Adult,Yes 1877 | Crew,Male,Adult,Yes 1878 | Crew,Male,Adult,Yes 1879 | Crew,Male,Adult,Yes 1880 | Crew,Male,Adult,Yes 1881 | Crew,Male,Adult,Yes 1882 | Crew,Male,Adult,Yes 1883 | Crew,Male,Adult,Yes 1884 | Crew,Male,Adult,Yes 1885 | Crew,Male,Adult,Yes 1886 | Crew,Male,Adult,Yes 1887 | 1st,Female,Adult,Yes 1888 | 1st,Female,Adult,Yes 1889 | 1st,Female,Adult,Yes 1890 | 1st,Female,Adult,Yes 1891 | 1st,Female,Adult,Yes 1892 | 1st,Female,Adult,Yes 1893 | 1st,Female,Adult,Yes 1894 | 1st,Female,Adult,Yes 1895 | 1st,Female,Adult,Yes 1896 | 1st,Female,Adult,Yes 1897 | 1st,Female,Adult,Yes 1898 | 1st,Female,Adult,Yes 1899 | 1st,Female,Adult,Yes 1900 | 1st,Female,Adult,Yes 1901 | 1st,Female,Adult,Yes 1902 | 1st,Female,Adult,Yes 1903 | 1st,Female,Adult,Yes 1904 | 1st,Female,Adult,Yes 1905 | 1st,Female,Adult,Yes 1906 | 1st,Female,Adult,Yes 1907 | 1st,Female,Adult,Yes 1908 | 1st,Female,Adult,Yes 1909 | 1st,Female,Adult,Yes 1910 | 1st,Female,Adult,Yes 1911 | 1st,Female,Adult,Yes 1912 | 1st,Female,Adult,Yes 1913 | 1st,Female,Adult,Yes 1914 | 1st,Female,Adult,Yes 1915 | 1st,Female,Adult,Yes 1916 | 1st,Female,Adult,Yes 1917 | 1st,Female,Adult,Yes 1918 | 1st,Female,Adult,Yes 1919 | 1st,Female,Adult,Yes 1920 | 1st,Female,Adult,Yes 1921 | 1st,Female,Adult,Yes 1922 | 1st,Female,Adult,Yes 1923 | 1st,Female,Adult,Yes 1924 | 1st,Female,Adult,Yes 1925 | 1st,Female,Adult,Yes 1926 | 1st,Female,Adult,Yes 1927 | 1st,Female,Adult,Yes 1928 | 1st,Female,Adult,Yes 1929 | 1st,Female,Adult,Yes 1930 | 1st,Female,Adult,Yes 1931 | 1st,Female,Adult,Yes 1932 | 1st,Female,Adult,Yes 1933 | 1st,Female,Adult,Yes 1934 | 1st,Female,Adult,Yes 1935 | 1st,Female,Adult,Yes 1936 | 1st,Female,Adult,Yes 1937 | 1st,Female,Adult,Yes 1938 | 1st,Female,Adult,Yes 1939 | 1st,Female,Adult,Yes 1940 | 1st,Female,Adult,Yes 1941 | 1st,Female,Adult,Yes 1942 | 1st,Female,Adult,Yes 1943 | 1st,Female,Adult,Yes 1944 | 1st,Female,Adult,Yes 1945 | 1st,Female,Adult,Yes 1946 | 1st,Female,Adult,Yes 1947 | 1st,Female,Adult,Yes 1948 | 1st,Female,Adult,Yes 1949 | 1st,Female,Adult,Yes 1950 | 1st,Female,Adult,Yes 1951 | 1st,Female,Adult,Yes 1952 | 1st,Female,Adult,Yes 1953 | 1st,Female,Adult,Yes 1954 | 1st,Female,Adult,Yes 1955 | 1st,Female,Adult,Yes 1956 | 1st,Female,Adult,Yes 1957 | 1st,Female,Adult,Yes 1958 | 1st,Female,Adult,Yes 1959 | 1st,Female,Adult,Yes 1960 | 1st,Female,Adult,Yes 1961 | 1st,Female,Adult,Yes 1962 | 1st,Female,Adult,Yes 1963 | 1st,Female,Adult,Yes 1964 | 1st,Female,Adult,Yes 1965 | 1st,Female,Adult,Yes 1966 | 1st,Female,Adult,Yes 1967 | 1st,Female,Adult,Yes 1968 | 1st,Female,Adult,Yes 1969 | 1st,Female,Adult,Yes 1970 | 1st,Female,Adult,Yes 1971 | 1st,Female,Adult,Yes 1972 | 1st,Female,Adult,Yes 1973 | 1st,Female,Adult,Yes 1974 | 1st,Female,Adult,Yes 1975 | 1st,Female,Adult,Yes 1976 | 1st,Female,Adult,Yes 1977 | 1st,Female,Adult,Yes 1978 | 1st,Female,Adult,Yes 1979 | 1st,Female,Adult,Yes 1980 | 1st,Female,Adult,Yes 1981 | 1st,Female,Adult,Yes 1982 | 1st,Female,Adult,Yes 1983 | 1st,Female,Adult,Yes 1984 | 1st,Female,Adult,Yes 1985 | 1st,Female,Adult,Yes 1986 | 1st,Female,Adult,Yes 1987 | 1st,Female,Adult,Yes 1988 | 1st,Female,Adult,Yes 1989 | 1st,Female,Adult,Yes 1990 | 1st,Female,Adult,Yes 1991 | 1st,Female,Adult,Yes 1992 | 1st,Female,Adult,Yes 1993 | 1st,Female,Adult,Yes 1994 | 1st,Female,Adult,Yes 1995 | 1st,Female,Adult,Yes 1996 | 1st,Female,Adult,Yes 1997 | 1st,Female,Adult,Yes 1998 | 1st,Female,Adult,Yes 1999 | 1st,Female,Adult,Yes 2000 | 1st,Female,Adult,Yes 2001 | 1st,Female,Adult,Yes 2002 | 1st,Female,Adult,Yes 2003 | 1st,Female,Adult,Yes 2004 | 1st,Female,Adult,Yes 2005 | 1st,Female,Adult,Yes 2006 | 1st,Female,Adult,Yes 2007 | 1st,Female,Adult,Yes 2008 | 1st,Female,Adult,Yes 2009 | 1st,Female,Adult,Yes 2010 | 1st,Female,Adult,Yes 2011 | 1st,Female,Adult,Yes 2012 | 1st,Female,Adult,Yes 2013 | 1st,Female,Adult,Yes 2014 | 1st,Female,Adult,Yes 2015 | 1st,Female,Adult,Yes 2016 | 1st,Female,Adult,Yes 2017 | 1st,Female,Adult,Yes 2018 | 1st,Female,Adult,Yes 2019 | 1st,Female,Adult,Yes 2020 | 1st,Female,Adult,Yes 2021 | 1st,Female,Adult,Yes 2022 | 1st,Female,Adult,Yes 2023 | 1st,Female,Adult,Yes 2024 | 1st,Female,Adult,Yes 2025 | 1st,Female,Adult,Yes 2026 | 1st,Female,Adult,Yes 2027 | 2nd,Female,Adult,Yes 2028 | 2nd,Female,Adult,Yes 2029 | 2nd,Female,Adult,Yes 2030 | 2nd,Female,Adult,Yes 2031 | 2nd,Female,Adult,Yes 2032 | 2nd,Female,Adult,Yes 2033 | 2nd,Female,Adult,Yes 2034 | 2nd,Female,Adult,Yes 2035 | 2nd,Female,Adult,Yes 2036 | 2nd,Female,Adult,Yes 2037 | 2nd,Female,Adult,Yes 2038 | 2nd,Female,Adult,Yes 2039 | 2nd,Female,Adult,Yes 2040 | 2nd,Female,Adult,Yes 2041 | 2nd,Female,Adult,Yes 2042 | 2nd,Female,Adult,Yes 2043 | 2nd,Female,Adult,Yes 2044 | 2nd,Female,Adult,Yes 2045 | 2nd,Female,Adult,Yes 2046 | 2nd,Female,Adult,Yes 2047 | 2nd,Female,Adult,Yes 2048 | 2nd,Female,Adult,Yes 2049 | 2nd,Female,Adult,Yes 2050 | 2nd,Female,Adult,Yes 2051 | 2nd,Female,Adult,Yes 2052 | 2nd,Female,Adult,Yes 2053 | 2nd,Female,Adult,Yes 2054 | 2nd,Female,Adult,Yes 2055 | 2nd,Female,Adult,Yes 2056 | 2nd,Female,Adult,Yes 2057 | 2nd,Female,Adult,Yes 2058 | 2nd,Female,Adult,Yes 2059 | 2nd,Female,Adult,Yes 2060 | 2nd,Female,Adult,Yes 2061 | 2nd,Female,Adult,Yes 2062 | 2nd,Female,Adult,Yes 2063 | 2nd,Female,Adult,Yes 2064 | 2nd,Female,Adult,Yes 2065 | 2nd,Female,Adult,Yes 2066 | 2nd,Female,Adult,Yes 2067 | 2nd,Female,Adult,Yes 2068 | 2nd,Female,Adult,Yes 2069 | 2nd,Female,Adult,Yes 2070 | 2nd,Female,Adult,Yes 2071 | 2nd,Female,Adult,Yes 2072 | 2nd,Female,Adult,Yes 2073 | 2nd,Female,Adult,Yes 2074 | 2nd,Female,Adult,Yes 2075 | 2nd,Female,Adult,Yes 2076 | 2nd,Female,Adult,Yes 2077 | 2nd,Female,Adult,Yes 2078 | 2nd,Female,Adult,Yes 2079 | 2nd,Female,Adult,Yes 2080 | 2nd,Female,Adult,Yes 2081 | 2nd,Female,Adult,Yes 2082 | 2nd,Female,Adult,Yes 2083 | 2nd,Female,Adult,Yes 2084 | 2nd,Female,Adult,Yes 2085 | 2nd,Female,Adult,Yes 2086 | 2nd,Female,Adult,Yes 2087 | 2nd,Female,Adult,Yes 2088 | 2nd,Female,Adult,Yes 2089 | 2nd,Female,Adult,Yes 2090 | 2nd,Female,Adult,Yes 2091 | 2nd,Female,Adult,Yes 2092 | 2nd,Female,Adult,Yes 2093 | 2nd,Female,Adult,Yes 2094 | 2nd,Female,Adult,Yes 2095 | 2nd,Female,Adult,Yes 2096 | 2nd,Female,Adult,Yes 2097 | 2nd,Female,Adult,Yes 2098 | 2nd,Female,Adult,Yes 2099 | 2nd,Female,Adult,Yes 2100 | 2nd,Female,Adult,Yes 2101 | 2nd,Female,Adult,Yes 2102 | 2nd,Female,Adult,Yes 2103 | 2nd,Female,Adult,Yes 2104 | 2nd,Female,Adult,Yes 2105 | 2nd,Female,Adult,Yes 2106 | 2nd,Female,Adult,Yes 2107 | 3rd,Female,Adult,Yes 2108 | 3rd,Female,Adult,Yes 2109 | 3rd,Female,Adult,Yes 2110 | 3rd,Female,Adult,Yes 2111 | 3rd,Female,Adult,Yes 2112 | 3rd,Female,Adult,Yes 2113 | 3rd,Female,Adult,Yes 2114 | 3rd,Female,Adult,Yes 2115 | 3rd,Female,Adult,Yes 2116 | 3rd,Female,Adult,Yes 2117 | 3rd,Female,Adult,Yes 2118 | 3rd,Female,Adult,Yes 2119 | 3rd,Female,Adult,Yes 2120 | 3rd,Female,Adult,Yes 2121 | 3rd,Female,Adult,Yes 2122 | 3rd,Female,Adult,Yes 2123 | 3rd,Female,Adult,Yes 2124 | 3rd,Female,Adult,Yes 2125 | 3rd,Female,Adult,Yes 2126 | 3rd,Female,Adult,Yes 2127 | 3rd,Female,Adult,Yes 2128 | 3rd,Female,Adult,Yes 2129 | 3rd,Female,Adult,Yes 2130 | 3rd,Female,Adult,Yes 2131 | 3rd,Female,Adult,Yes 2132 | 3rd,Female,Adult,Yes 2133 | 3rd,Female,Adult,Yes 2134 | 3rd,Female,Adult,Yes 2135 | 3rd,Female,Adult,Yes 2136 | 3rd,Female,Adult,Yes 2137 | 3rd,Female,Adult,Yes 2138 | 3rd,Female,Adult,Yes 2139 | 3rd,Female,Adult,Yes 2140 | 3rd,Female,Adult,Yes 2141 | 3rd,Female,Adult,Yes 2142 | 3rd,Female,Adult,Yes 2143 | 3rd,Female,Adult,Yes 2144 | 3rd,Female,Adult,Yes 2145 | 3rd,Female,Adult,Yes 2146 | 3rd,Female,Adult,Yes 2147 | 3rd,Female,Adult,Yes 2148 | 3rd,Female,Adult,Yes 2149 | 3rd,Female,Adult,Yes 2150 | 3rd,Female,Adult,Yes 2151 | 3rd,Female,Adult,Yes 2152 | 3rd,Female,Adult,Yes 2153 | 3rd,Female,Adult,Yes 2154 | 3rd,Female,Adult,Yes 2155 | 3rd,Female,Adult,Yes 2156 | 3rd,Female,Adult,Yes 2157 | 3rd,Female,Adult,Yes 2158 | 3rd,Female,Adult,Yes 2159 | 3rd,Female,Adult,Yes 2160 | 3rd,Female,Adult,Yes 2161 | 3rd,Female,Adult,Yes 2162 | 3rd,Female,Adult,Yes 2163 | 3rd,Female,Adult,Yes 2164 | 3rd,Female,Adult,Yes 2165 | 3rd,Female,Adult,Yes 2166 | 3rd,Female,Adult,Yes 2167 | 3rd,Female,Adult,Yes 2168 | 3rd,Female,Adult,Yes 2169 | 3rd,Female,Adult,Yes 2170 | 3rd,Female,Adult,Yes 2171 | 3rd,Female,Adult,Yes 2172 | 3rd,Female,Adult,Yes 2173 | 3rd,Female,Adult,Yes 2174 | 3rd,Female,Adult,Yes 2175 | 3rd,Female,Adult,Yes 2176 | 3rd,Female,Adult,Yes 2177 | 3rd,Female,Adult,Yes 2178 | 3rd,Female,Adult,Yes 2179 | 3rd,Female,Adult,Yes 2180 | 3rd,Female,Adult,Yes 2181 | 3rd,Female,Adult,Yes 2182 | 3rd,Female,Adult,Yes 2183 | Crew,Female,Adult,Yes 2184 | Crew,Female,Adult,Yes 2185 | Crew,Female,Adult,Yes 2186 | Crew,Female,Adult,Yes 2187 | Crew,Female,Adult,Yes 2188 | Crew,Female,Adult,Yes 2189 | Crew,Female,Adult,Yes 2190 | Crew,Female,Adult,Yes 2191 | Crew,Female,Adult,Yes 2192 | Crew,Female,Adult,Yes 2193 | Crew,Female,Adult,Yes 2194 | Crew,Female,Adult,Yes 2195 | Crew,Female,Adult,Yes 2196 | Crew,Female,Adult,Yes 2197 | Crew,Female,Adult,Yes 2198 | Crew,Female,Adult,Yes 2199 | Crew,Female,Adult,Yes 2200 | Crew,Female,Adult,Yes 2201 | Crew,Female,Adult,Yes 2202 | Crew,Female,Adult,Yes 2203 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DataMiningNotebooks 2 | 3 | Please see the following data mining example notebooks that go along with the Machine Learning I companion class at Southern Methodist University. 4 | 5 | Notebooks are numbered by the unit (i.e., week) at which they are covered in the class. Other class examples are prefixed with "E" and then the number of the example (they are not ties with a specific week). Also included here are the initial notebooks for various in-class assignments we use throughout the semester. 6 | 7 | To use the notebooks you can either download the repository as a zip file (top right corner) or you can use a git checkout via the command: 8 | ``` 9 | git clone https://github.com/jakemdrew/DataMiningNotebooks.git 10 | ``` 11 | 12 | This will install the entire git repository into a new directory and you can always update the directory via git. 13 | 14 | Please direct any questions to the course instructor or to Professor Drew (jdrew@smu.edu) 15 | -------------------------------------------------------------------------------- /Syllabus.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jakemdrew/DataMiningNotebooks/5f1fa5d53dbe558224889e87e84fd784d7fba8b6/Syllabus.pdf -------------------------------------------------------------------------------- /data/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jakemdrew/DataMiningNotebooks/5f1fa5d53dbe558224889e87e84fd784d7fba8b6/data/.DS_Store -------------------------------------------------------------------------------- /data/diabetes.arff: -------------------------------------------------------------------------------- 1 | % 1. Title: Pima Indians Diabetes Database 2 | % 3 | % 2. Sources: 4 | % (a) Original owners: National Institute of Diabetes and Digestive and 5 | % Kidney Diseases 6 | % (b) Donor of database: Vincent Sigillito (vgs@aplcen.apl.jhu.edu) 7 | % Research Center, RMI Group Leader 8 | % Applied Physics Laboratory 9 | % The Johns Hopkins University 10 | % Johns Hopkins Road 11 | % Laurel, MD 20707 12 | % (301) 953-6231 13 | % (c) Date received: 9 May 1990 14 | % 15 | % 3. Past Usage: 16 | % 1. Smith,~J.~W., Everhart,~J.~E., Dickson,~W.~C., Knowler,~W.~C., \& 17 | % Johannes,~R.~S. (1988). Using the ADAP learning algorithm to forecast 18 | % the onset of diabetes mellitus. In {\it Proceedings of the Symposium 19 | % on Computer Applications and Medical Care} (pp. 261--265). IEEE 20 | % Computer Society Press. 21 | % 22 | % The diagnostic, binary-valued variable investigated is whether the 23 | % patient shows signs of diabetes according to World Health Organization 24 | % criteria (i.e., if the 2 hour post-load plasma glucose was at least 25 | % 200 mg/dl at any survey examination or if found during routine medical 26 | % care). The population lives near Phoenix, Arizona, USA. 27 | % 28 | % Results: Their ADAP algorithm makes a real-valued prediction between 29 | % 0 and 1. This was transformed into a binary decision using a cutoff of 30 | % 0.448. Using 576 training instances, the sensitivity and specificity 31 | % of their algorithm was 76% on the remaining 192 instances. 32 | % 33 | % 4. Relevant Information: 34 | % Several constraints were placed on the selection of these instances from 35 | % a larger database. In particular, all patients here are females at 36 | % least 21 years old of Pima Indian heritage. ADAP is an adaptive learning 37 | % routine that generates and executes digital analogs of perceptron-like 38 | % devices. It is a unique algorithm; see the paper for details. 39 | % 40 | % 5. Number of Instances: 768 41 | % 42 | % 6. Number of Attributes: 8 plus class 43 | % 44 | % 7. For Each Attribute: (all numeric-valued) 45 | % 1. Number of times pregnant 46 | % 2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test 47 | % 3. Diastolic blood pressure (mm Hg) 48 | % 4. Triceps skin fold thickness (mm) 49 | % 5. 2-Hour serum insulin (mu U/ml) 50 | % 6. Body mass index (weight in kg/(height in m)^2) 51 | % 7. Diabetes pedigree function 52 | % 8. Age (years) 53 | % 9. Class variable (0 or 1) 54 | % 55 | % 8. Missing Attribute Values: None 56 | % 57 | % 9. Class Distribution: (class value 1 is interpreted as "tested positive for 58 | % diabetes") 59 | % 60 | % Class Value Number of instances 61 | % 0 500 62 | % 1 268 63 | % 64 | % 10. Brief statistical analysis: 65 | % 66 | % Attribute number: Mean: Standard Deviation: 67 | % 1. 3.8 3.4 68 | % 2. 120.9 32.0 69 | % 3. 69.1 19.4 70 | % 4. 20.5 16.0 71 | % 5. 79.8 115.2 72 | % 6. 32.0 7.9 73 | % 7. 0.5 0.3 74 | % 8. 33.2 11.8 75 | % 76 | % 77 | % 78 | % 79 | % 80 | % 81 | % Relabeled values in attribute 'class' 82 | % From: 0 To: tested_negative 83 | % From: 1 To: tested_positive 84 | % 85 | @relation pima_diabetes 86 | @attribute 'preg' real 87 | @attribute 'plas' real 88 | @attribute 'pres' real 89 | @attribute 'skin' real 90 | @attribute 'insu' real 91 | @attribute 'mass' real 92 | @attribute 'pedi' real 93 | @attribute 'age' real 94 | @attribute 'class' { tested_negative, tested_positive} 95 | @data 96 | 6,148,72,35,0,33.6,0.627,50,tested_positive 97 | 1,85,66,29,0,26.6,0.351,31,tested_negative 98 | 8,183,64,0,0,23.3,0.672,32,tested_positive 99 | 1,89,66,23,94,28.1,0.167,21,tested_negative 100 | 0,137,40,35,168,43.1,2.288,33,tested_positive 101 | 5,116,74,0,0,25.6,0.201,30,tested_negative 102 | 3,78,50,32,88,31,0.248,26,tested_positive 103 | 10,115,0,0,0,35.3,0.134,29,tested_negative 104 | 2,197,70,45,543,30.5,0.158,53,tested_positive 105 | 8,125,96,0,0,0,0.232,54,tested_positive 106 | 4,110,92,0,0,37.6,0.191,30,tested_negative 107 | 10,168,74,0,0,38,0.537,34,tested_positive 108 | 10,139,80,0,0,27.1,1.441,57,tested_negative 109 | 1,189,60,23,846,30.1,0.398,59,tested_positive 110 | 5,166,72,19,175,25.8,0.587,51,tested_positive 111 | 7,100,0,0,0,30,0.484,32,tested_positive 112 | 0,118,84,47,230,45.8,0.551,31,tested_positive 113 | 7,107,74,0,0,29.6,0.254,31,tested_positive 114 | 1,103,30,38,83,43.3,0.183,33,tested_negative 115 | 1,115,70,30,96,34.6,0.529,32,tested_positive 116 | 3,126,88,41,235,39.3,0.704,27,tested_negative 117 | 8,99,84,0,0,35.4,0.388,50,tested_negative 118 | 7,196,90,0,0,39.8,0.451,41,tested_positive 119 | 9,119,80,35,0,29,0.263,29,tested_positive 120 | 11,143,94,33,146,36.6,0.254,51,tested_positive 121 | 10,125,70,26,115,31.1,0.205,41,tested_positive 122 | 7,147,76,0,0,39.4,0.257,43,tested_positive 123 | 1,97,66,15,140,23.2,0.487,22,tested_negative 124 | 13,145,82,19,110,22.2,0.245,57,tested_negative 125 | 5,117,92,0,0,34.1,0.337,38,tested_negative 126 | 5,109,75,26,0,36,0.546,60,tested_negative 127 | 3,158,76,36,245,31.6,0.851,28,tested_positive 128 | 3,88,58,11,54,24.8,0.267,22,tested_negative 129 | 6,92,92,0,0,19.9,0.188,28,tested_negative 130 | 10,122,78,31,0,27.6,0.512,45,tested_negative 131 | 4,103,60,33,192,24,0.966,33,tested_negative 132 | 11,138,76,0,0,33.2,0.42,35,tested_negative 133 | 9,102,76,37,0,32.9,0.665,46,tested_positive 134 | 2,90,68,42,0,38.2,0.503,27,tested_positive 135 | 4,111,72,47,207,37.1,1.39,56,tested_positive 136 | 3,180,64,25,70,34,0.271,26,tested_negative 137 | 7,133,84,0,0,40.2,0.696,37,tested_negative 138 | 7,106,92,18,0,22.7,0.235,48,tested_negative 139 | 9,171,110,24,240,45.4,0.721,54,tested_positive 140 | 7,159,64,0,0,27.4,0.294,40,tested_negative 141 | 0,180,66,39,0,42,1.893,25,tested_positive 142 | 1,146,56,0,0,29.7,0.564,29,tested_negative 143 | 2,71,70,27,0,28,0.586,22,tested_negative 144 | 7,103,66,32,0,39.1,0.344,31,tested_positive 145 | 7,105,0,0,0,0,0.305,24,tested_negative 146 | 1,103,80,11,82,19.4,0.491,22,tested_negative 147 | 1,101,50,15,36,24.2,0.526,26,tested_negative 148 | 5,88,66,21,23,24.4,0.342,30,tested_negative 149 | 8,176,90,34,300,33.7,0.467,58,tested_positive 150 | 7,150,66,42,342,34.7,0.718,42,tested_negative 151 | 1,73,50,10,0,23,0.248,21,tested_negative 152 | 7,187,68,39,304,37.7,0.254,41,tested_positive 153 | 0,100,88,60,110,46.8,0.962,31,tested_negative 154 | 0,146,82,0,0,40.5,1.781,44,tested_negative 155 | 0,105,64,41,142,41.5,0.173,22,tested_negative 156 | 2,84,0,0,0,0,0.304,21,tested_negative 157 | 8,133,72,0,0,32.9,0.27,39,tested_positive 158 | 5,44,62,0,0,25,0.587,36,tested_negative 159 | 2,141,58,34,128,25.4,0.699,24,tested_negative 160 | 7,114,66,0,0,32.8,0.258,42,tested_positive 161 | 5,99,74,27,0,29,0.203,32,tested_negative 162 | 0,109,88,30,0,32.5,0.855,38,tested_positive 163 | 2,109,92,0,0,42.7,0.845,54,tested_negative 164 | 1,95,66,13,38,19.6,0.334,25,tested_negative 165 | 4,146,85,27,100,28.9,0.189,27,tested_negative 166 | 2,100,66,20,90,32.9,0.867,28,tested_positive 167 | 5,139,64,35,140,28.6,0.411,26,tested_negative 168 | 13,126,90,0,0,43.4,0.583,42,tested_positive 169 | 4,129,86,20,270,35.1,0.231,23,tested_negative 170 | 1,79,75,30,0,32,0.396,22,tested_negative 171 | 1,0,48,20,0,24.7,0.14,22,tested_negative 172 | 7,62,78,0,0,32.6,0.391,41,tested_negative 173 | 5,95,72,33,0,37.7,0.37,27,tested_negative 174 | 0,131,0,0,0,43.2,0.27,26,tested_positive 175 | 2,112,66,22,0,25,0.307,24,tested_negative 176 | 3,113,44,13,0,22.4,0.14,22,tested_negative 177 | 2,74,0,0,0,0,0.102,22,tested_negative 178 | 7,83,78,26,71,29.3,0.767,36,tested_negative 179 | 0,101,65,28,0,24.6,0.237,22,tested_negative 180 | 5,137,108,0,0,48.8,0.227,37,tested_positive 181 | 2,110,74,29,125,32.4,0.698,27,tested_negative 182 | 13,106,72,54,0,36.6,0.178,45,tested_negative 183 | 2,100,68,25,71,38.5,0.324,26,tested_negative 184 | 15,136,70,32,110,37.1,0.153,43,tested_positive 185 | 1,107,68,19,0,26.5,0.165,24,tested_negative 186 | 1,80,55,0,0,19.1,0.258,21,tested_negative 187 | 4,123,80,15,176,32,0.443,34,tested_negative 188 | 7,81,78,40,48,46.7,0.261,42,tested_negative 189 | 4,134,72,0,0,23.8,0.277,60,tested_positive 190 | 2,142,82,18,64,24.7,0.761,21,tested_negative 191 | 6,144,72,27,228,33.9,0.255,40,tested_negative 192 | 2,92,62,28,0,31.6,0.13,24,tested_negative 193 | 1,71,48,18,76,20.4,0.323,22,tested_negative 194 | 6,93,50,30,64,28.7,0.356,23,tested_negative 195 | 1,122,90,51,220,49.7,0.325,31,tested_positive 196 | 1,163,72,0,0,39,1.222,33,tested_positive 197 | 1,151,60,0,0,26.1,0.179,22,tested_negative 198 | 0,125,96,0,0,22.5,0.262,21,tested_negative 199 | 1,81,72,18,40,26.6,0.283,24,tested_negative 200 | 2,85,65,0,0,39.6,0.93,27,tested_negative 201 | 1,126,56,29,152,28.7,0.801,21,tested_negative 202 | 1,96,122,0,0,22.4,0.207,27,tested_negative 203 | 4,144,58,28,140,29.5,0.287,37,tested_negative 204 | 3,83,58,31,18,34.3,0.336,25,tested_negative 205 | 0,95,85,25,36,37.4,0.247,24,tested_positive 206 | 3,171,72,33,135,33.3,0.199,24,tested_positive 207 | 8,155,62,26,495,34,0.543,46,tested_positive 208 | 1,89,76,34,37,31.2,0.192,23,tested_negative 209 | 4,76,62,0,0,34,0.391,25,tested_negative 210 | 7,160,54,32,175,30.5,0.588,39,tested_positive 211 | 4,146,92,0,0,31.2,0.539,61,tested_positive 212 | 5,124,74,0,0,34,0.22,38,tested_positive 213 | 5,78,48,0,0,33.7,0.654,25,tested_negative 214 | 4,97,60,23,0,28.2,0.443,22,tested_negative 215 | 4,99,76,15,51,23.2,0.223,21,tested_negative 216 | 0,162,76,56,100,53.2,0.759,25,tested_positive 217 | 6,111,64,39,0,34.2,0.26,24,tested_negative 218 | 2,107,74,30,100,33.6,0.404,23,tested_negative 219 | 5,132,80,0,0,26.8,0.186,69,tested_negative 220 | 0,113,76,0,0,33.3,0.278,23,tested_positive 221 | 1,88,30,42,99,55,0.496,26,tested_positive 222 | 3,120,70,30,135,42.9,0.452,30,tested_negative 223 | 1,118,58,36,94,33.3,0.261,23,tested_negative 224 | 1,117,88,24,145,34.5,0.403,40,tested_positive 225 | 0,105,84,0,0,27.9,0.741,62,tested_positive 226 | 4,173,70,14,168,29.7,0.361,33,tested_positive 227 | 9,122,56,0,0,33.3,1.114,33,tested_positive 228 | 3,170,64,37,225,34.5,0.356,30,tested_positive 229 | 8,84,74,31,0,38.3,0.457,39,tested_negative 230 | 2,96,68,13,49,21.1,0.647,26,tested_negative 231 | 2,125,60,20,140,33.8,0.088,31,tested_negative 232 | 0,100,70,26,50,30.8,0.597,21,tested_negative 233 | 0,93,60,25,92,28.7,0.532,22,tested_negative 234 | 0,129,80,0,0,31.2,0.703,29,tested_negative 235 | 5,105,72,29,325,36.9,0.159,28,tested_negative 236 | 3,128,78,0,0,21.1,0.268,55,tested_negative 237 | 5,106,82,30,0,39.5,0.286,38,tested_negative 238 | 2,108,52,26,63,32.5,0.318,22,tested_negative 239 | 10,108,66,0,0,32.4,0.272,42,tested_positive 240 | 4,154,62,31,284,32.8,0.237,23,tested_negative 241 | 0,102,75,23,0,0,0.572,21,tested_negative 242 | 9,57,80,37,0,32.8,0.096,41,tested_negative 243 | 2,106,64,35,119,30.5,1.4,34,tested_negative 244 | 5,147,78,0,0,33.7,0.218,65,tested_negative 245 | 2,90,70,17,0,27.3,0.085,22,tested_negative 246 | 1,136,74,50,204,37.4,0.399,24,tested_negative 247 | 4,114,65,0,0,21.9,0.432,37,tested_negative 248 | 9,156,86,28,155,34.3,1.189,42,tested_positive 249 | 1,153,82,42,485,40.6,0.687,23,tested_negative 250 | 8,188,78,0,0,47.9,0.137,43,tested_positive 251 | 7,152,88,44,0,50,0.337,36,tested_positive 252 | 2,99,52,15,94,24.6,0.637,21,tested_negative 253 | 1,109,56,21,135,25.2,0.833,23,tested_negative 254 | 2,88,74,19,53,29,0.229,22,tested_negative 255 | 17,163,72,41,114,40.9,0.817,47,tested_positive 256 | 4,151,90,38,0,29.7,0.294,36,tested_negative 257 | 7,102,74,40,105,37.2,0.204,45,tested_negative 258 | 0,114,80,34,285,44.2,0.167,27,tested_negative 259 | 2,100,64,23,0,29.7,0.368,21,tested_negative 260 | 0,131,88,0,0,31.6,0.743,32,tested_positive 261 | 6,104,74,18,156,29.9,0.722,41,tested_positive 262 | 3,148,66,25,0,32.5,0.256,22,tested_negative 263 | 4,120,68,0,0,29.6,0.709,34,tested_negative 264 | 4,110,66,0,0,31.9,0.471,29,tested_negative 265 | 3,111,90,12,78,28.4,0.495,29,tested_negative 266 | 6,102,82,0,0,30.8,0.18,36,tested_positive 267 | 6,134,70,23,130,35.4,0.542,29,tested_positive 268 | 2,87,0,23,0,28.9,0.773,25,tested_negative 269 | 1,79,60,42,48,43.5,0.678,23,tested_negative 270 | 2,75,64,24,55,29.7,0.37,33,tested_negative 271 | 8,179,72,42,130,32.7,0.719,36,tested_positive 272 | 6,85,78,0,0,31.2,0.382,42,tested_negative 273 | 0,129,110,46,130,67.1,0.319,26,tested_positive 274 | 5,143,78,0,0,45,0.19,47,tested_negative 275 | 5,130,82,0,0,39.1,0.956,37,tested_positive 276 | 6,87,80,0,0,23.2,0.084,32,tested_negative 277 | 0,119,64,18,92,34.9,0.725,23,tested_negative 278 | 1,0,74,20,23,27.7,0.299,21,tested_negative 279 | 5,73,60,0,0,26.8,0.268,27,tested_negative 280 | 4,141,74,0,0,27.6,0.244,40,tested_negative 281 | 7,194,68,28,0,35.9,0.745,41,tested_positive 282 | 8,181,68,36,495,30.1,0.615,60,tested_positive 283 | 1,128,98,41,58,32,1.321,33,tested_positive 284 | 8,109,76,39,114,27.9,0.64,31,tested_positive 285 | 5,139,80,35,160,31.6,0.361,25,tested_positive 286 | 3,111,62,0,0,22.6,0.142,21,tested_negative 287 | 9,123,70,44,94,33.1,0.374,40,tested_negative 288 | 7,159,66,0,0,30.4,0.383,36,tested_positive 289 | 11,135,0,0,0,52.3,0.578,40,tested_positive 290 | 8,85,55,20,0,24.4,0.136,42,tested_negative 291 | 5,158,84,41,210,39.4,0.395,29,tested_positive 292 | 1,105,58,0,0,24.3,0.187,21,tested_negative 293 | 3,107,62,13,48,22.9,0.678,23,tested_positive 294 | 4,109,64,44,99,34.8,0.905,26,tested_positive 295 | 4,148,60,27,318,30.9,0.15,29,tested_positive 296 | 0,113,80,16,0,31,0.874,21,tested_negative 297 | 1,138,82,0,0,40.1,0.236,28,tested_negative 298 | 0,108,68,20,0,27.3,0.787,32,tested_negative 299 | 2,99,70,16,44,20.4,0.235,27,tested_negative 300 | 6,103,72,32,190,37.7,0.324,55,tested_negative 301 | 5,111,72,28,0,23.9,0.407,27,tested_negative 302 | 8,196,76,29,280,37.5,0.605,57,tested_positive 303 | 5,162,104,0,0,37.7,0.151,52,tested_positive 304 | 1,96,64,27,87,33.2,0.289,21,tested_negative 305 | 7,184,84,33,0,35.5,0.355,41,tested_positive 306 | 2,81,60,22,0,27.7,0.29,25,tested_negative 307 | 0,147,85,54,0,42.8,0.375,24,tested_negative 308 | 7,179,95,31,0,34.2,0.164,60,tested_negative 309 | 0,140,65,26,130,42.6,0.431,24,tested_positive 310 | 9,112,82,32,175,34.2,0.26,36,tested_positive 311 | 12,151,70,40,271,41.8,0.742,38,tested_positive 312 | 5,109,62,41,129,35.8,0.514,25,tested_positive 313 | 6,125,68,30,120,30,0.464,32,tested_negative 314 | 5,85,74,22,0,29,1.224,32,tested_positive 315 | 5,112,66,0,0,37.8,0.261,41,tested_positive 316 | 0,177,60,29,478,34.6,1.072,21,tested_positive 317 | 2,158,90,0,0,31.6,0.805,66,tested_positive 318 | 7,119,0,0,0,25.2,0.209,37,tested_negative 319 | 7,142,60,33,190,28.8,0.687,61,tested_negative 320 | 1,100,66,15,56,23.6,0.666,26,tested_negative 321 | 1,87,78,27,32,34.6,0.101,22,tested_negative 322 | 0,101,76,0,0,35.7,0.198,26,tested_negative 323 | 3,162,52,38,0,37.2,0.652,24,tested_positive 324 | 4,197,70,39,744,36.7,2.329,31,tested_negative 325 | 0,117,80,31,53,45.2,0.089,24,tested_negative 326 | 4,142,86,0,0,44,0.645,22,tested_positive 327 | 6,134,80,37,370,46.2,0.238,46,tested_positive 328 | 1,79,80,25,37,25.4,0.583,22,tested_negative 329 | 4,122,68,0,0,35,0.394,29,tested_negative 330 | 3,74,68,28,45,29.7,0.293,23,tested_negative 331 | 4,171,72,0,0,43.6,0.479,26,tested_positive 332 | 7,181,84,21,192,35.9,0.586,51,tested_positive 333 | 0,179,90,27,0,44.1,0.686,23,tested_positive 334 | 9,164,84,21,0,30.8,0.831,32,tested_positive 335 | 0,104,76,0,0,18.4,0.582,27,tested_negative 336 | 1,91,64,24,0,29.2,0.192,21,tested_negative 337 | 4,91,70,32,88,33.1,0.446,22,tested_negative 338 | 3,139,54,0,0,25.6,0.402,22,tested_positive 339 | 6,119,50,22,176,27.1,1.318,33,tested_positive 340 | 2,146,76,35,194,38.2,0.329,29,tested_negative 341 | 9,184,85,15,0,30,1.213,49,tested_positive 342 | 10,122,68,0,0,31.2,0.258,41,tested_negative 343 | 0,165,90,33,680,52.3,0.427,23,tested_negative 344 | 9,124,70,33,402,35.4,0.282,34,tested_negative 345 | 1,111,86,19,0,30.1,0.143,23,tested_negative 346 | 9,106,52,0,0,31.2,0.38,42,tested_negative 347 | 2,129,84,0,0,28,0.284,27,tested_negative 348 | 2,90,80,14,55,24.4,0.249,24,tested_negative 349 | 0,86,68,32,0,35.8,0.238,25,tested_negative 350 | 12,92,62,7,258,27.6,0.926,44,tested_positive 351 | 1,113,64,35,0,33.6,0.543,21,tested_positive 352 | 3,111,56,39,0,30.1,0.557,30,tested_negative 353 | 2,114,68,22,0,28.7,0.092,25,tested_negative 354 | 1,193,50,16,375,25.9,0.655,24,tested_negative 355 | 11,155,76,28,150,33.3,1.353,51,tested_positive 356 | 3,191,68,15,130,30.9,0.299,34,tested_negative 357 | 3,141,0,0,0,30,0.761,27,tested_positive 358 | 4,95,70,32,0,32.1,0.612,24,tested_negative 359 | 3,142,80,15,0,32.4,0.2,63,tested_negative 360 | 4,123,62,0,0,32,0.226,35,tested_positive 361 | 5,96,74,18,67,33.6,0.997,43,tested_negative 362 | 0,138,0,0,0,36.3,0.933,25,tested_positive 363 | 2,128,64,42,0,40,1.101,24,tested_negative 364 | 0,102,52,0,0,25.1,0.078,21,tested_negative 365 | 2,146,0,0,0,27.5,0.24,28,tested_positive 366 | 10,101,86,37,0,45.6,1.136,38,tested_positive 367 | 2,108,62,32,56,25.2,0.128,21,tested_negative 368 | 3,122,78,0,0,23,0.254,40,tested_negative 369 | 1,71,78,50,45,33.2,0.422,21,tested_negative 370 | 13,106,70,0,0,34.2,0.251,52,tested_negative 371 | 2,100,70,52,57,40.5,0.677,25,tested_negative 372 | 7,106,60,24,0,26.5,0.296,29,tested_positive 373 | 0,104,64,23,116,27.8,0.454,23,tested_negative 374 | 5,114,74,0,0,24.9,0.744,57,tested_negative 375 | 2,108,62,10,278,25.3,0.881,22,tested_negative 376 | 0,146,70,0,0,37.9,0.334,28,tested_positive 377 | 10,129,76,28,122,35.9,0.28,39,tested_negative 378 | 7,133,88,15,155,32.4,0.262,37,tested_negative 379 | 7,161,86,0,0,30.4,0.165,47,tested_positive 380 | 2,108,80,0,0,27,0.259,52,tested_positive 381 | 7,136,74,26,135,26,0.647,51,tested_negative 382 | 5,155,84,44,545,38.7,0.619,34,tested_negative 383 | 1,119,86,39,220,45.6,0.808,29,tested_positive 384 | 4,96,56,17,49,20.8,0.34,26,tested_negative 385 | 5,108,72,43,75,36.1,0.263,33,tested_negative 386 | 0,78,88,29,40,36.9,0.434,21,tested_negative 387 | 0,107,62,30,74,36.6,0.757,25,tested_positive 388 | 2,128,78,37,182,43.3,1.224,31,tested_positive 389 | 1,128,48,45,194,40.5,0.613,24,tested_positive 390 | 0,161,50,0,0,21.9,0.254,65,tested_negative 391 | 6,151,62,31,120,35.5,0.692,28,tested_negative 392 | 2,146,70,38,360,28,0.337,29,tested_positive 393 | 0,126,84,29,215,30.7,0.52,24,tested_negative 394 | 14,100,78,25,184,36.6,0.412,46,tested_positive 395 | 8,112,72,0,0,23.6,0.84,58,tested_negative 396 | 0,167,0,0,0,32.3,0.839,30,tested_positive 397 | 2,144,58,33,135,31.6,0.422,25,tested_positive 398 | 5,77,82,41,42,35.8,0.156,35,tested_negative 399 | 5,115,98,0,0,52.9,0.209,28,tested_positive 400 | 3,150,76,0,0,21,0.207,37,tested_negative 401 | 2,120,76,37,105,39.7,0.215,29,tested_negative 402 | 10,161,68,23,132,25.5,0.326,47,tested_positive 403 | 0,137,68,14,148,24.8,0.143,21,tested_negative 404 | 0,128,68,19,180,30.5,1.391,25,tested_positive 405 | 2,124,68,28,205,32.9,0.875,30,tested_positive 406 | 6,80,66,30,0,26.2,0.313,41,tested_negative 407 | 0,106,70,37,148,39.4,0.605,22,tested_negative 408 | 2,155,74,17,96,26.6,0.433,27,tested_positive 409 | 3,113,50,10,85,29.5,0.626,25,tested_negative 410 | 7,109,80,31,0,35.9,1.127,43,tested_positive 411 | 2,112,68,22,94,34.1,0.315,26,tested_negative 412 | 3,99,80,11,64,19.3,0.284,30,tested_negative 413 | 3,182,74,0,0,30.5,0.345,29,tested_positive 414 | 3,115,66,39,140,38.1,0.15,28,tested_negative 415 | 6,194,78,0,0,23.5,0.129,59,tested_positive 416 | 4,129,60,12,231,27.5,0.527,31,tested_negative 417 | 3,112,74,30,0,31.6,0.197,25,tested_positive 418 | 0,124,70,20,0,27.4,0.254,36,tested_positive 419 | 13,152,90,33,29,26.8,0.731,43,tested_positive 420 | 2,112,75,32,0,35.7,0.148,21,tested_negative 421 | 1,157,72,21,168,25.6,0.123,24,tested_negative 422 | 1,122,64,32,156,35.1,0.692,30,tested_positive 423 | 10,179,70,0,0,35.1,0.2,37,tested_negative 424 | 2,102,86,36,120,45.5,0.127,23,tested_positive 425 | 6,105,70,32,68,30.8,0.122,37,tested_negative 426 | 8,118,72,19,0,23.1,1.476,46,tested_negative 427 | 2,87,58,16,52,32.7,0.166,25,tested_negative 428 | 1,180,0,0,0,43.3,0.282,41,tested_positive 429 | 12,106,80,0,0,23.6,0.137,44,tested_negative 430 | 1,95,60,18,58,23.9,0.26,22,tested_negative 431 | 0,165,76,43,255,47.9,0.259,26,tested_negative 432 | 0,117,0,0,0,33.8,0.932,44,tested_negative 433 | 5,115,76,0,0,31.2,0.343,44,tested_positive 434 | 9,152,78,34,171,34.2,0.893,33,tested_positive 435 | 7,178,84,0,0,39.9,0.331,41,tested_positive 436 | 1,130,70,13,105,25.9,0.472,22,tested_negative 437 | 1,95,74,21,73,25.9,0.673,36,tested_negative 438 | 1,0,68,35,0,32,0.389,22,tested_negative 439 | 5,122,86,0,0,34.7,0.29,33,tested_negative 440 | 8,95,72,0,0,36.8,0.485,57,tested_negative 441 | 8,126,88,36,108,38.5,0.349,49,tested_negative 442 | 1,139,46,19,83,28.7,0.654,22,tested_negative 443 | 3,116,0,0,0,23.5,0.187,23,tested_negative 444 | 3,99,62,19,74,21.8,0.279,26,tested_negative 445 | 5,0,80,32,0,41,0.346,37,tested_positive 446 | 4,92,80,0,0,42.2,0.237,29,tested_negative 447 | 4,137,84,0,0,31.2,0.252,30,tested_negative 448 | 3,61,82,28,0,34.4,0.243,46,tested_negative 449 | 1,90,62,12,43,27.2,0.58,24,tested_negative 450 | 3,90,78,0,0,42.7,0.559,21,tested_negative 451 | 9,165,88,0,0,30.4,0.302,49,tested_positive 452 | 1,125,50,40,167,33.3,0.962,28,tested_positive 453 | 13,129,0,30,0,39.9,0.569,44,tested_positive 454 | 12,88,74,40,54,35.3,0.378,48,tested_negative 455 | 1,196,76,36,249,36.5,0.875,29,tested_positive 456 | 5,189,64,33,325,31.2,0.583,29,tested_positive 457 | 5,158,70,0,0,29.8,0.207,63,tested_negative 458 | 5,103,108,37,0,39.2,0.305,65,tested_negative 459 | 4,146,78,0,0,38.5,0.52,67,tested_positive 460 | 4,147,74,25,293,34.9,0.385,30,tested_negative 461 | 5,99,54,28,83,34,0.499,30,tested_negative 462 | 6,124,72,0,0,27.6,0.368,29,tested_positive 463 | 0,101,64,17,0,21,0.252,21,tested_negative 464 | 3,81,86,16,66,27.5,0.306,22,tested_negative 465 | 1,133,102,28,140,32.8,0.234,45,tested_positive 466 | 3,173,82,48,465,38.4,2.137,25,tested_positive 467 | 0,118,64,23,89,0,1.731,21,tested_negative 468 | 0,84,64,22,66,35.8,0.545,21,tested_negative 469 | 2,105,58,40,94,34.9,0.225,25,tested_negative 470 | 2,122,52,43,158,36.2,0.816,28,tested_negative 471 | 12,140,82,43,325,39.2,0.528,58,tested_positive 472 | 0,98,82,15,84,25.2,0.299,22,tested_negative 473 | 1,87,60,37,75,37.2,0.509,22,tested_negative 474 | 4,156,75,0,0,48.3,0.238,32,tested_positive 475 | 0,93,100,39,72,43.4,1.021,35,tested_negative 476 | 1,107,72,30,82,30.8,0.821,24,tested_negative 477 | 0,105,68,22,0,20,0.236,22,tested_negative 478 | 1,109,60,8,182,25.4,0.947,21,tested_negative 479 | 1,90,62,18,59,25.1,1.268,25,tested_negative 480 | 1,125,70,24,110,24.3,0.221,25,tested_negative 481 | 1,119,54,13,50,22.3,0.205,24,tested_negative 482 | 5,116,74,29,0,32.3,0.66,35,tested_positive 483 | 8,105,100,36,0,43.3,0.239,45,tested_positive 484 | 5,144,82,26,285,32,0.452,58,tested_positive 485 | 3,100,68,23,81,31.6,0.949,28,tested_negative 486 | 1,100,66,29,196,32,0.444,42,tested_negative 487 | 5,166,76,0,0,45.7,0.34,27,tested_positive 488 | 1,131,64,14,415,23.7,0.389,21,tested_negative 489 | 4,116,72,12,87,22.1,0.463,37,tested_negative 490 | 4,158,78,0,0,32.9,0.803,31,tested_positive 491 | 2,127,58,24,275,27.7,1.6,25,tested_negative 492 | 3,96,56,34,115,24.7,0.944,39,tested_negative 493 | 0,131,66,40,0,34.3,0.196,22,tested_positive 494 | 3,82,70,0,0,21.1,0.389,25,tested_negative 495 | 3,193,70,31,0,34.9,0.241,25,tested_positive 496 | 4,95,64,0,0,32,0.161,31,tested_positive 497 | 6,137,61,0,0,24.2,0.151,55,tested_negative 498 | 5,136,84,41,88,35,0.286,35,tested_positive 499 | 9,72,78,25,0,31.6,0.28,38,tested_negative 500 | 5,168,64,0,0,32.9,0.135,41,tested_positive 501 | 2,123,48,32,165,42.1,0.52,26,tested_negative 502 | 4,115,72,0,0,28.9,0.376,46,tested_positive 503 | 0,101,62,0,0,21.9,0.336,25,tested_negative 504 | 8,197,74,0,0,25.9,1.191,39,tested_positive 505 | 1,172,68,49,579,42.4,0.702,28,tested_positive 506 | 6,102,90,39,0,35.7,0.674,28,tested_negative 507 | 1,112,72,30,176,34.4,0.528,25,tested_negative 508 | 1,143,84,23,310,42.4,1.076,22,tested_negative 509 | 1,143,74,22,61,26.2,0.256,21,tested_negative 510 | 0,138,60,35,167,34.6,0.534,21,tested_positive 511 | 3,173,84,33,474,35.7,0.258,22,tested_positive 512 | 1,97,68,21,0,27.2,1.095,22,tested_negative 513 | 4,144,82,32,0,38.5,0.554,37,tested_positive 514 | 1,83,68,0,0,18.2,0.624,27,tested_negative 515 | 3,129,64,29,115,26.4,0.219,28,tested_positive 516 | 1,119,88,41,170,45.3,0.507,26,tested_negative 517 | 2,94,68,18,76,26,0.561,21,tested_negative 518 | 0,102,64,46,78,40.6,0.496,21,tested_negative 519 | 2,115,64,22,0,30.8,0.421,21,tested_negative 520 | 8,151,78,32,210,42.9,0.516,36,tested_positive 521 | 4,184,78,39,277,37,0.264,31,tested_positive 522 | 0,94,0,0,0,0,0.256,25,tested_negative 523 | 1,181,64,30,180,34.1,0.328,38,tested_positive 524 | 0,135,94,46,145,40.6,0.284,26,tested_negative 525 | 1,95,82,25,180,35,0.233,43,tested_positive 526 | 2,99,0,0,0,22.2,0.108,23,tested_negative 527 | 3,89,74,16,85,30.4,0.551,38,tested_negative 528 | 1,80,74,11,60,30,0.527,22,tested_negative 529 | 2,139,75,0,0,25.6,0.167,29,tested_negative 530 | 1,90,68,8,0,24.5,1.138,36,tested_negative 531 | 0,141,0,0,0,42.4,0.205,29,tested_positive 532 | 12,140,85,33,0,37.4,0.244,41,tested_negative 533 | 5,147,75,0,0,29.9,0.434,28,tested_negative 534 | 1,97,70,15,0,18.2,0.147,21,tested_negative 535 | 6,107,88,0,0,36.8,0.727,31,tested_negative 536 | 0,189,104,25,0,34.3,0.435,41,tested_positive 537 | 2,83,66,23,50,32.2,0.497,22,tested_negative 538 | 4,117,64,27,120,33.2,0.23,24,tested_negative 539 | 8,108,70,0,0,30.5,0.955,33,tested_positive 540 | 4,117,62,12,0,29.7,0.38,30,tested_positive 541 | 0,180,78,63,14,59.4,2.42,25,tested_positive 542 | 1,100,72,12,70,25.3,0.658,28,tested_negative 543 | 0,95,80,45,92,36.5,0.33,26,tested_negative 544 | 0,104,64,37,64,33.6,0.51,22,tested_positive 545 | 0,120,74,18,63,30.5,0.285,26,tested_negative 546 | 1,82,64,13,95,21.2,0.415,23,tested_negative 547 | 2,134,70,0,0,28.9,0.542,23,tested_positive 548 | 0,91,68,32,210,39.9,0.381,25,tested_negative 549 | 2,119,0,0,0,19.6,0.832,72,tested_negative 550 | 2,100,54,28,105,37.8,0.498,24,tested_negative 551 | 14,175,62,30,0,33.6,0.212,38,tested_positive 552 | 1,135,54,0,0,26.7,0.687,62,tested_negative 553 | 5,86,68,28,71,30.2,0.364,24,tested_negative 554 | 10,148,84,48,237,37.6,1.001,51,tested_positive 555 | 9,134,74,33,60,25.9,0.46,81,tested_negative 556 | 9,120,72,22,56,20.8,0.733,48,tested_negative 557 | 1,71,62,0,0,21.8,0.416,26,tested_negative 558 | 8,74,70,40,49,35.3,0.705,39,tested_negative 559 | 5,88,78,30,0,27.6,0.258,37,tested_negative 560 | 10,115,98,0,0,24,1.022,34,tested_negative 561 | 0,124,56,13,105,21.8,0.452,21,tested_negative 562 | 0,74,52,10,36,27.8,0.269,22,tested_negative 563 | 0,97,64,36,100,36.8,0.6,25,tested_negative 564 | 8,120,0,0,0,30,0.183,38,tested_positive 565 | 6,154,78,41,140,46.1,0.571,27,tested_negative 566 | 1,144,82,40,0,41.3,0.607,28,tested_negative 567 | 0,137,70,38,0,33.2,0.17,22,tested_negative 568 | 0,119,66,27,0,38.8,0.259,22,tested_negative 569 | 7,136,90,0,0,29.9,0.21,50,tested_negative 570 | 4,114,64,0,0,28.9,0.126,24,tested_negative 571 | 0,137,84,27,0,27.3,0.231,59,tested_negative 572 | 2,105,80,45,191,33.7,0.711,29,tested_positive 573 | 7,114,76,17,110,23.8,0.466,31,tested_negative 574 | 8,126,74,38,75,25.9,0.162,39,tested_negative 575 | 4,132,86,31,0,28,0.419,63,tested_negative 576 | 3,158,70,30,328,35.5,0.344,35,tested_positive 577 | 0,123,88,37,0,35.2,0.197,29,tested_negative 578 | 4,85,58,22,49,27.8,0.306,28,tested_negative 579 | 0,84,82,31,125,38.2,0.233,23,tested_negative 580 | 0,145,0,0,0,44.2,0.63,31,tested_positive 581 | 0,135,68,42,250,42.3,0.365,24,tested_positive 582 | 1,139,62,41,480,40.7,0.536,21,tested_negative 583 | 0,173,78,32,265,46.5,1.159,58,tested_negative 584 | 4,99,72,17,0,25.6,0.294,28,tested_negative 585 | 8,194,80,0,0,26.1,0.551,67,tested_negative 586 | 2,83,65,28,66,36.8,0.629,24,tested_negative 587 | 2,89,90,30,0,33.5,0.292,42,tested_negative 588 | 4,99,68,38,0,32.8,0.145,33,tested_negative 589 | 4,125,70,18,122,28.9,1.144,45,tested_positive 590 | 3,80,0,0,0,0,0.174,22,tested_negative 591 | 6,166,74,0,0,26.6,0.304,66,tested_negative 592 | 5,110,68,0,0,26,0.292,30,tested_negative 593 | 2,81,72,15,76,30.1,0.547,25,tested_negative 594 | 7,195,70,33,145,25.1,0.163,55,tested_positive 595 | 6,154,74,32,193,29.3,0.839,39,tested_negative 596 | 2,117,90,19,71,25.2,0.313,21,tested_negative 597 | 3,84,72,32,0,37.2,0.267,28,tested_negative 598 | 6,0,68,41,0,39,0.727,41,tested_positive 599 | 7,94,64,25,79,33.3,0.738,41,tested_negative 600 | 3,96,78,39,0,37.3,0.238,40,tested_negative 601 | 10,75,82,0,0,33.3,0.263,38,tested_negative 602 | 0,180,90,26,90,36.5,0.314,35,tested_positive 603 | 1,130,60,23,170,28.6,0.692,21,tested_negative 604 | 2,84,50,23,76,30.4,0.968,21,tested_negative 605 | 8,120,78,0,0,25,0.409,64,tested_negative 606 | 12,84,72,31,0,29.7,0.297,46,tested_positive 607 | 0,139,62,17,210,22.1,0.207,21,tested_negative 608 | 9,91,68,0,0,24.2,0.2,58,tested_negative 609 | 2,91,62,0,0,27.3,0.525,22,tested_negative 610 | 3,99,54,19,86,25.6,0.154,24,tested_negative 611 | 3,163,70,18,105,31.6,0.268,28,tested_positive 612 | 9,145,88,34,165,30.3,0.771,53,tested_positive 613 | 7,125,86,0,0,37.6,0.304,51,tested_negative 614 | 13,76,60,0,0,32.8,0.18,41,tested_negative 615 | 6,129,90,7,326,19.6,0.582,60,tested_negative 616 | 2,68,70,32,66,25,0.187,25,tested_negative 617 | 3,124,80,33,130,33.2,0.305,26,tested_negative 618 | 6,114,0,0,0,0,0.189,26,tested_negative 619 | 9,130,70,0,0,34.2,0.652,45,tested_positive 620 | 3,125,58,0,0,31.6,0.151,24,tested_negative 621 | 3,87,60,18,0,21.8,0.444,21,tested_negative 622 | 1,97,64,19,82,18.2,0.299,21,tested_negative 623 | 3,116,74,15,105,26.3,0.107,24,tested_negative 624 | 0,117,66,31,188,30.8,0.493,22,tested_negative 625 | 0,111,65,0,0,24.6,0.66,31,tested_negative 626 | 2,122,60,18,106,29.8,0.717,22,tested_negative 627 | 0,107,76,0,0,45.3,0.686,24,tested_negative 628 | 1,86,66,52,65,41.3,0.917,29,tested_negative 629 | 6,91,0,0,0,29.8,0.501,31,tested_negative 630 | 1,77,56,30,56,33.3,1.251,24,tested_negative 631 | 4,132,0,0,0,32.9,0.302,23,tested_positive 632 | 0,105,90,0,0,29.6,0.197,46,tested_negative 633 | 0,57,60,0,0,21.7,0.735,67,tested_negative 634 | 0,127,80,37,210,36.3,0.804,23,tested_negative 635 | 3,129,92,49,155,36.4,0.968,32,tested_positive 636 | 8,100,74,40,215,39.4,0.661,43,tested_positive 637 | 3,128,72,25,190,32.4,0.549,27,tested_positive 638 | 10,90,85,32,0,34.9,0.825,56,tested_positive 639 | 4,84,90,23,56,39.5,0.159,25,tested_negative 640 | 1,88,78,29,76,32,0.365,29,tested_negative 641 | 8,186,90,35,225,34.5,0.423,37,tested_positive 642 | 5,187,76,27,207,43.6,1.034,53,tested_positive 643 | 4,131,68,21,166,33.1,0.16,28,tested_negative 644 | 1,164,82,43,67,32.8,0.341,50,tested_negative 645 | 4,189,110,31,0,28.5,0.68,37,tested_negative 646 | 1,116,70,28,0,27.4,0.204,21,tested_negative 647 | 3,84,68,30,106,31.9,0.591,25,tested_negative 648 | 6,114,88,0,0,27.8,0.247,66,tested_negative 649 | 1,88,62,24,44,29.9,0.422,23,tested_negative 650 | 1,84,64,23,115,36.9,0.471,28,tested_negative 651 | 7,124,70,33,215,25.5,0.161,37,tested_negative 652 | 1,97,70,40,0,38.1,0.218,30,tested_negative 653 | 8,110,76,0,0,27.8,0.237,58,tested_negative 654 | 11,103,68,40,0,46.2,0.126,42,tested_negative 655 | 11,85,74,0,0,30.1,0.3,35,tested_negative 656 | 6,125,76,0,0,33.8,0.121,54,tested_positive 657 | 0,198,66,32,274,41.3,0.502,28,tested_positive 658 | 1,87,68,34,77,37.6,0.401,24,tested_negative 659 | 6,99,60,19,54,26.9,0.497,32,tested_negative 660 | 0,91,80,0,0,32.4,0.601,27,tested_negative 661 | 2,95,54,14,88,26.1,0.748,22,tested_negative 662 | 1,99,72,30,18,38.6,0.412,21,tested_negative 663 | 6,92,62,32,126,32,0.085,46,tested_negative 664 | 4,154,72,29,126,31.3,0.338,37,tested_negative 665 | 0,121,66,30,165,34.3,0.203,33,tested_positive 666 | 3,78,70,0,0,32.5,0.27,39,tested_negative 667 | 2,130,96,0,0,22.6,0.268,21,tested_negative 668 | 3,111,58,31,44,29.5,0.43,22,tested_negative 669 | 2,98,60,17,120,34.7,0.198,22,tested_negative 670 | 1,143,86,30,330,30.1,0.892,23,tested_negative 671 | 1,119,44,47,63,35.5,0.28,25,tested_negative 672 | 6,108,44,20,130,24,0.813,35,tested_negative 673 | 2,118,80,0,0,42.9,0.693,21,tested_positive 674 | 10,133,68,0,0,27,0.245,36,tested_negative 675 | 2,197,70,99,0,34.7,0.575,62,tested_positive 676 | 0,151,90,46,0,42.1,0.371,21,tested_positive 677 | 6,109,60,27,0,25,0.206,27,tested_negative 678 | 12,121,78,17,0,26.5,0.259,62,tested_negative 679 | 8,100,76,0,0,38.7,0.19,42,tested_negative 680 | 8,124,76,24,600,28.7,0.687,52,tested_positive 681 | 1,93,56,11,0,22.5,0.417,22,tested_negative 682 | 8,143,66,0,0,34.9,0.129,41,tested_positive 683 | 6,103,66,0,0,24.3,0.249,29,tested_negative 684 | 3,176,86,27,156,33.3,1.154,52,tested_positive 685 | 0,73,0,0,0,21.1,0.342,25,tested_negative 686 | 11,111,84,40,0,46.8,0.925,45,tested_positive 687 | 2,112,78,50,140,39.4,0.175,24,tested_negative 688 | 3,132,80,0,0,34.4,0.402,44,tested_positive 689 | 2,82,52,22,115,28.5,1.699,25,tested_negative 690 | 6,123,72,45,230,33.6,0.733,34,tested_negative 691 | 0,188,82,14,185,32,0.682,22,tested_positive 692 | 0,67,76,0,0,45.3,0.194,46,tested_negative 693 | 1,89,24,19,25,27.8,0.559,21,tested_negative 694 | 1,173,74,0,0,36.8,0.088,38,tested_positive 695 | 1,109,38,18,120,23.1,0.407,26,tested_negative 696 | 1,108,88,19,0,27.1,0.4,24,tested_negative 697 | 6,96,0,0,0,23.7,0.19,28,tested_negative 698 | 1,124,74,36,0,27.8,0.1,30,tested_negative 699 | 7,150,78,29,126,35.2,0.692,54,tested_positive 700 | 4,183,0,0,0,28.4,0.212,36,tested_positive 701 | 1,124,60,32,0,35.8,0.514,21,tested_negative 702 | 1,181,78,42,293,40,1.258,22,tested_positive 703 | 1,92,62,25,41,19.5,0.482,25,tested_negative 704 | 0,152,82,39,272,41.5,0.27,27,tested_negative 705 | 1,111,62,13,182,24,0.138,23,tested_negative 706 | 3,106,54,21,158,30.9,0.292,24,tested_negative 707 | 3,174,58,22,194,32.9,0.593,36,tested_positive 708 | 7,168,88,42,321,38.2,0.787,40,tested_positive 709 | 6,105,80,28,0,32.5,0.878,26,tested_negative 710 | 11,138,74,26,144,36.1,0.557,50,tested_positive 711 | 3,106,72,0,0,25.8,0.207,27,tested_negative 712 | 6,117,96,0,0,28.7,0.157,30,tested_negative 713 | 2,68,62,13,15,20.1,0.257,23,tested_negative 714 | 9,112,82,24,0,28.2,1.282,50,tested_positive 715 | 0,119,0,0,0,32.4,0.141,24,tested_positive 716 | 2,112,86,42,160,38.4,0.246,28,tested_negative 717 | 2,92,76,20,0,24.2,1.698,28,tested_negative 718 | 6,183,94,0,0,40.8,1.461,45,tested_negative 719 | 0,94,70,27,115,43.5,0.347,21,tested_negative 720 | 2,108,64,0,0,30.8,0.158,21,tested_negative 721 | 4,90,88,47,54,37.7,0.362,29,tested_negative 722 | 0,125,68,0,0,24.7,0.206,21,tested_negative 723 | 0,132,78,0,0,32.4,0.393,21,tested_negative 724 | 5,128,80,0,0,34.6,0.144,45,tested_negative 725 | 4,94,65,22,0,24.7,0.148,21,tested_negative 726 | 7,114,64,0,0,27.4,0.732,34,tested_positive 727 | 0,102,78,40,90,34.5,0.238,24,tested_negative 728 | 2,111,60,0,0,26.2,0.343,23,tested_negative 729 | 1,128,82,17,183,27.5,0.115,22,tested_negative 730 | 10,92,62,0,0,25.9,0.167,31,tested_negative 731 | 13,104,72,0,0,31.2,0.465,38,tested_positive 732 | 5,104,74,0,0,28.8,0.153,48,tested_negative 733 | 2,94,76,18,66,31.6,0.649,23,tested_negative 734 | 7,97,76,32,91,40.9,0.871,32,tested_positive 735 | 1,100,74,12,46,19.5,0.149,28,tested_negative 736 | 0,102,86,17,105,29.3,0.695,27,tested_negative 737 | 4,128,70,0,0,34.3,0.303,24,tested_negative 738 | 6,147,80,0,0,29.5,0.178,50,tested_positive 739 | 4,90,0,0,0,28,0.61,31,tested_negative 740 | 3,103,72,30,152,27.6,0.73,27,tested_negative 741 | 2,157,74,35,440,39.4,0.134,30,tested_negative 742 | 1,167,74,17,144,23.4,0.447,33,tested_positive 743 | 0,179,50,36,159,37.8,0.455,22,tested_positive 744 | 11,136,84,35,130,28.3,0.26,42,tested_positive 745 | 0,107,60,25,0,26.4,0.133,23,tested_negative 746 | 1,91,54,25,100,25.2,0.234,23,tested_negative 747 | 1,117,60,23,106,33.8,0.466,27,tested_negative 748 | 5,123,74,40,77,34.1,0.269,28,tested_negative 749 | 2,120,54,0,0,26.8,0.455,27,tested_negative 750 | 1,106,70,28,135,34.2,0.142,22,tested_negative 751 | 2,155,52,27,540,38.7,0.24,25,tested_positive 752 | 2,101,58,35,90,21.8,0.155,22,tested_negative 753 | 1,120,80,48,200,38.9,1.162,41,tested_negative 754 | 11,127,106,0,0,39,0.19,51,tested_negative 755 | 3,80,82,31,70,34.2,1.292,27,tested_positive 756 | 10,162,84,0,0,27.7,0.182,54,tested_negative 757 | 1,199,76,43,0,42.9,1.394,22,tested_positive 758 | 8,167,106,46,231,37.6,0.165,43,tested_positive 759 | 9,145,80,46,130,37.9,0.637,40,tested_positive 760 | 6,115,60,39,0,33.7,0.245,40,tested_positive 761 | 1,112,80,45,132,34.8,0.217,24,tested_negative 762 | 4,145,82,18,0,32.5,0.235,70,tested_positive 763 | 10,111,70,27,0,27.5,0.141,40,tested_positive 764 | 6,98,58,33,190,34,0.43,43,tested_negative 765 | 9,154,78,30,100,30.9,0.164,45,tested_negative 766 | 6,165,68,26,168,33.6,0.631,49,tested_negative 767 | 1,99,58,10,0,25.4,0.551,21,tested_negative 768 | 10,68,106,23,49,35.5,0.285,47,tested_negative 769 | 3,123,100,35,240,57.3,0.88,22,tested_negative 770 | 8,91,82,0,0,35.6,0.587,68,tested_negative 771 | 6,195,70,0,0,30.9,0.328,31,tested_positive 772 | 9,156,86,0,0,24.8,0.23,53,tested_positive 773 | 0,93,60,0,0,35.3,0.263,25,tested_negative 774 | 3,121,52,0,0,36,0.127,25,tested_positive 775 | 2,101,58,17,265,24.2,0.614,23,tested_negative 776 | 2,56,56,28,45,24.2,0.332,22,tested_negative 777 | 0,162,76,36,0,49.6,0.364,26,tested_positive 778 | 0,95,64,39,105,44.6,0.366,22,tested_negative 779 | 4,125,80,0,0,32.3,0.536,27,tested_positive 780 | 5,136,82,0,0,0,0.64,69,tested_negative 781 | 2,129,74,26,205,33.2,0.591,25,tested_negative 782 | 3,130,64,0,0,23.1,0.314,22,tested_negative 783 | 1,107,50,19,0,28.3,0.181,29,tested_negative 784 | 1,140,74,26,180,24.1,0.828,23,tested_negative 785 | 1,144,82,46,180,46.1,0.335,46,tested_positive 786 | 8,107,80,0,0,24.6,0.856,34,tested_negative 787 | 13,158,114,0,0,42.3,0.257,44,tested_positive 788 | 2,121,70,32,95,39.1,0.886,23,tested_negative 789 | 7,129,68,49,125,38.5,0.439,43,tested_positive 790 | 2,90,60,0,0,23.5,0.191,25,tested_negative 791 | 7,142,90,24,480,30.4,0.128,43,tested_positive 792 | 3,169,74,19,125,29.9,0.268,31,tested_positive 793 | 0,99,0,0,0,25,0.253,22,tested_negative 794 | 4,127,88,11,155,34.5,0.598,28,tested_negative 795 | 4,118,70,0,0,44.5,0.904,26,tested_negative 796 | 2,122,76,27,200,35.9,0.483,26,tested_negative 797 | 6,125,78,31,0,27.6,0.565,49,tested_positive 798 | 1,168,88,29,0,35,0.905,52,tested_positive 799 | 2,129,0,0,0,38.5,0.304,41,tested_negative 800 | 4,110,76,20,100,28.4,0.118,27,tested_negative 801 | 6,80,80,36,0,39.8,0.177,28,tested_negative 802 | 10,115,0,0,0,0,0.261,30,tested_positive 803 | 2,127,46,21,335,34.4,0.176,22,tested_negative 804 | 9,164,78,0,0,32.8,0.148,45,tested_positive 805 | 2,93,64,32,160,38,0.674,23,tested_positive 806 | 3,158,64,13,387,31.2,0.295,24,tested_negative 807 | 5,126,78,27,22,29.6,0.439,40,tested_negative 808 | 10,129,62,36,0,41.2,0.441,38,tested_positive 809 | 0,134,58,20,291,26.4,0.352,21,tested_negative 810 | 3,102,74,0,0,29.5,0.121,32,tested_negative 811 | 7,187,50,33,392,33.9,0.826,34,tested_positive 812 | 3,173,78,39,185,33.8,0.97,31,tested_positive 813 | 10,94,72,18,0,23.1,0.595,56,tested_negative 814 | 1,108,60,46,178,35.5,0.415,24,tested_negative 815 | 5,97,76,27,0,35.6,0.378,52,tested_positive 816 | 4,83,86,19,0,29.3,0.317,34,tested_negative 817 | 1,114,66,36,200,38.1,0.289,21,tested_negative 818 | 1,149,68,29,127,29.3,0.349,42,tested_positive 819 | 5,117,86,30,105,39.1,0.251,42,tested_negative 820 | 1,111,94,0,0,32.8,0.265,45,tested_negative 821 | 4,112,78,40,0,39.4,0.236,38,tested_negative 822 | 1,116,78,29,180,36.1,0.496,25,tested_negative 823 | 0,141,84,26,0,32.4,0.433,22,tested_negative 824 | 2,175,88,0,0,22.9,0.326,22,tested_negative 825 | 2,92,52,0,0,30.1,0.141,22,tested_negative 826 | 3,130,78,23,79,28.4,0.323,34,tested_positive 827 | 8,120,86,0,0,28.4,0.259,22,tested_positive 828 | 2,174,88,37,120,44.5,0.646,24,tested_positive 829 | 2,106,56,27,165,29,0.426,22,tested_negative 830 | 2,105,75,0,0,23.3,0.56,53,tested_negative 831 | 4,95,60,32,0,35.4,0.284,28,tested_negative 832 | 0,126,86,27,120,27.4,0.515,21,tested_negative 833 | 8,65,72,23,0,32,0.6,42,tested_negative 834 | 2,99,60,17,160,36.6,0.453,21,tested_negative 835 | 1,102,74,0,0,39.5,0.293,42,tested_positive 836 | 11,120,80,37,150,42.3,0.785,48,tested_positive 837 | 3,102,44,20,94,30.8,0.4,26,tested_negative 838 | 1,109,58,18,116,28.5,0.219,22,tested_negative 839 | 9,140,94,0,0,32.7,0.734,45,tested_positive 840 | 13,153,88,37,140,40.6,1.174,39,tested_negative 841 | 12,100,84,33,105,30,0.488,46,tested_negative 842 | 1,147,94,41,0,49.3,0.358,27,tested_positive 843 | 1,81,74,41,57,46.3,1.096,32,tested_negative 844 | 3,187,70,22,200,36.4,0.408,36,tested_positive 845 | 6,162,62,0,0,24.3,0.178,50,tested_positive 846 | 4,136,70,0,0,31.2,1.182,22,tested_positive 847 | 1,121,78,39,74,39,0.261,28,tested_negative 848 | 3,108,62,24,0,26,0.223,25,tested_negative 849 | 0,181,88,44,510,43.3,0.222,26,tested_positive 850 | 8,154,78,32,0,32.4,0.443,45,tested_positive 851 | 1,128,88,39,110,36.5,1.057,37,tested_positive 852 | 7,137,90,41,0,32,0.391,39,tested_negative 853 | 0,123,72,0,0,36.3,0.258,52,tested_positive 854 | 1,106,76,0,0,37.5,0.197,26,tested_negative 855 | 6,190,92,0,0,35.5,0.278,66,tested_positive 856 | 2,88,58,26,16,28.4,0.766,22,tested_negative 857 | 9,170,74,31,0,44,0.403,43,tested_positive 858 | 9,89,62,0,0,22.5,0.142,33,tested_negative 859 | 10,101,76,48,180,32.9,0.171,63,tested_negative 860 | 2,122,70,27,0,36.8,0.34,27,tested_negative 861 | 5,121,72,23,112,26.2,0.245,30,tested_negative 862 | 1,126,60,0,0,30.1,0.349,47,tested_positive 863 | 1,93,70,31,0,30.4,0.315,23,tested_negative -------------------------------------------------------------------------------- /data/guido.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jakemdrew/DataMiningNotebooks/5f1fa5d53dbe558224889e87e84fd784d7fba8b6/data/guido.png -------------------------------------------------------------------------------- /data/heart_disease.csv: -------------------------------------------------------------------------------- 1 | site,age,is_male,chest_pain,rest_blood_press,cholesterol,high_blood_sugar,rest_ecg,max_heart_rate,exer_angina,ST_depression,Peak_ST_seg,major_vessels,thal,has_heart_disease 2 | cleve,63,1,1,145,233,1,2,150,0,2.3,3,0,6,0 3 | cleve,67,1,4,160,286,0,2,108,1,1.5,2,3,3,2 4 | cleve,67,1,4,120,229,0,2,129,1,2.6,2,2,7,1 5 | cleve,37,1,3,130,250,0,0,187,0,3.5,3,0,3,0 6 | cleve,41,0,2,130,204,0,2,172,0,1.4,1,0,3,0 7 | cleve,56,1,2,120,236,0,0,178,0,0.8,1,0,3,0 8 | cleve,62,0,4,140,268,0,2,160,0,3.6,3,2,3,3 9 | cleve,57,0,4,120,354,0,0,163,1,0.6,1,0,3,0 10 | cleve,63,1,4,130,254,0,2,147,0,1.4,2,1,7,2 11 | cleve,53,1,4,140,203,1,2,155,1,3.1,3,0,7,1 12 | cleve,57,1,4,140,192,0,0,148,0,0.4,2,0,6,0 13 | cleve,56,0,2,140,294,0,2,153,0,1.3,2,0,3,0 14 | cleve,56,1,3,130,256,1,2,142,1,0.6,2,1,6,2 15 | cleve,44,1,2,120,263,0,0,173,0,0,1,0,7,0 16 | cleve,52,1,3,172,199,1,0,162,0,0.5,1,0,7,0 17 | cleve,57,1,3,150,168,0,0,174,0,1.6,1,0,3,0 18 | cleve,48,1,2,110,229,0,0,168,0,1,3,0,7,1 19 | cleve,54,1,4,140,239,0,0,160,0,1.2,1,0,3,0 20 | cleve,48,0,3,130,275,0,0,139,0,0.2,1,0,3,0 21 | cleve,49,1,2,130,266,0,0,171,0,0.6,1,0,3,0 22 | cleve,64,1,1,110,211,0,2,144,1,1.8,2,0,3,0 23 | cleve,58,0,1,150,283,1,2,162,0,1,1,0,3,0 24 | cleve,58,1,2,120,284,0,2,160,0,1.8,2,0,3,1 25 | cleve,58,1,3,132,224,0,2,173,0,3.2,1,2,7,3 26 | cleve,60,1,4,130,206,0,2,132,1,2.4,2,2,7,4 27 | cleve,50,0,3,120,219,0,0,158,0,1.6,2,0,3,0 28 | cleve,58,0,3,120,340,0,0,172,0,0,1,0,3,0 29 | cleve,66,0,1,150,226,0,0,114,0,2.6,3,0,3,0 30 | cleve,43,1,4,150,247,0,0,171,0,1.5,1,0,3,0 31 | cleve,40,1,4,110,167,0,2,114,1,2,2,0,7,3 32 | cleve,69,0,1,140,239,0,0,151,0,1.8,1,2,3,0 33 | cleve,60,1,4,117,230,1,0,160,1,1.4,1,2,7,2 34 | cleve,64,1,3,140,335,0,0,158,0,0,1,0,3,1 35 | cleve,59,1,4,135,234,0,0,161,0,0.5,2,0,7,0 36 | cleve,44,1,3,130,233,0,0,179,1,0.4,1,0,3,0 37 | cleve,42,1,4,140,226,0,0,178,0,0,1,0,3,0 38 | cleve,43,1,4,120,177,0,2,120,1,2.5,2,0,7,3 39 | cleve,57,1,4,150,276,0,2,112,1,0.6,2,1,6,1 40 | cleve,55,1,4,132,353,0,0,132,1,1.2,2,1,7,3 41 | cleve,61,1,3,150,243,1,0,137,1,1,2,0,3,0 42 | cleve,65,0,4,150,225,0,2,114,0,1,2,3,7,4 43 | cleve,40,1,1,140,199,0,0,178,1,1.4,1,0,7,0 44 | cleve,71,0,2,160,302,0,0,162,0,0.4,1,2,3,0 45 | cleve,59,1,3,150,212,1,0,157,0,1.6,1,0,3,0 46 | cleve,61,0,4,130,330,0,2,169,0,0,1,0,3,1 47 | cleve,58,1,3,112,230,0,2,165,0,2.5,2,1,7,4 48 | cleve,51,1,3,110,175,0,0,123,0,0.6,1,0,3,0 49 | cleve,50,1,4,150,243,0,2,128,0,2.6,2,0,7,4 50 | cleve,65,0,3,140,417,1,2,157,0,0.8,1,1,3,0 51 | cleve,53,1,3,130,197,1,2,152,0,1.2,3,0,3,0 52 | cleve,41,0,2,105,198,0,0,168,0,0,1,1,3,0 53 | cleve,65,1,4,120,177,0,0,140,0,0.4,1,0,7,0 54 | cleve,44,1,4,112,290,0,2,153,0,0,1,1,3,2 55 | cleve,44,1,2,130,219,0,2,188,0,0,1,0,3,0 56 | cleve,60,1,4,130,253,0,0,144,1,1.4,1,1,7,1 57 | cleve,54,1,4,124,266,0,2,109,1,2.2,2,1,7,1 58 | cleve,50,1,3,140,233,0,0,163,0,0.6,2,1,7,1 59 | cleve,41,1,4,110,172,0,2,158,0,0,1,0,7,1 60 | cleve,54,1,3,125,273,0,2,152,0,0.5,3,1,3,0 61 | cleve,51,1,1,125,213,0,2,125,1,1.4,1,1,3,0 62 | cleve,51,0,4,130,305,0,0,142,1,1.2,2,0,7,2 63 | cleve,46,0,3,142,177,0,2,160,1,1.4,3,0,3,0 64 | cleve,58,1,4,128,216,0,2,131,1,2.2,2,3,7,1 65 | cleve,54,0,3,135,304,1,0,170,0,0,1,0,3,0 66 | cleve,54,1,4,120,188,0,0,113,0,1.4,2,1,7,2 67 | cleve,60,1,4,145,282,0,2,142,1,2.8,2,2,7,2 68 | cleve,60,1,3,140,185,0,2,155,0,3,2,0,3,1 69 | cleve,54,1,3,150,232,0,2,165,0,1.6,1,0,7,0 70 | cleve,59,1,4,170,326,0,2,140,1,3.4,3,0,7,2 71 | cleve,46,1,3,150,231,0,0,147,0,3.6,2,0,3,1 72 | cleve,65,0,3,155,269,0,0,148,0,0.8,1,0,3,0 73 | cleve,67,1,4,125,254,1,0,163,0,0.2,2,2,7,3 74 | cleve,62,1,4,120,267,0,0,99,1,1.8,2,2,7,1 75 | cleve,65,1,4,110,248,0,2,158,0,0.6,1,2,6,1 76 | cleve,44,1,4,110,197,0,2,177,0,0,1,1,3,1 77 | cleve,65,0,3,160,360,0,2,151,0,0.8,1,0,3,0 78 | cleve,60,1,4,125,258,0,2,141,1,2.8,2,1,7,1 79 | cleve,51,0,3,140,308,0,2,142,0,1.5,1,1,3,0 80 | cleve,48,1,2,130,245,0,2,180,0,0.2,2,0,3,0 81 | cleve,58,1,4,150,270,0,2,111,1,0.8,1,0,7,3 82 | cleve,45,1,4,104,208,0,2,148,1,3,2,0,3,0 83 | cleve,53,0,4,130,264,0,2,143,0,0.4,2,0,3,0 84 | cleve,39,1,3,140,321,0,2,182,0,0,1,0,3,0 85 | cleve,68,1,3,180,274,1,2,150,1,1.6,2,0,7,3 86 | cleve,52,1,2,120,325,0,0,172,0,0.2,1,0,3,0 87 | cleve,44,1,3,140,235,0,2,180,0,0,1,0,3,0 88 | cleve,47,1,3,138,257,0,2,156,0,0,1,0,3,0 89 | cleve,53,0,3,128,216,0,2,115,0,0,1,0,?,0 90 | cleve,53,0,4,138,234,0,2,160,0,0,1,0,3,0 91 | cleve,51,0,3,130,256,0,2,149,0,0.5,1,0,3,0 92 | cleve,66,1,4,120,302,0,2,151,0,0.4,2,0,3,0 93 | cleve,62,0,4,160,164,0,2,145,0,6.2,3,3,7,3 94 | cleve,62,1,3,130,231,0,0,146,0,1.8,2,3,7,0 95 | cleve,44,0,3,108,141,0,0,175,0,0.6,2,0,3,0 96 | cleve,63,0,3,135,252,0,2,172,0,0,1,0,3,0 97 | cleve,52,1,4,128,255,0,0,161,1,0,1,1,7,1 98 | cleve,59,1,4,110,239,0,2,142,1,1.2,2,1,7,2 99 | cleve,60,0,4,150,258,0,2,157,0,2.6,2,2,7,3 100 | cleve,52,1,2,134,201,0,0,158,0,0.8,1,1,3,0 101 | cleve,48,1,4,122,222,0,2,186,0,0,1,0,3,0 102 | cleve,45,1,4,115,260,0,2,185,0,0,1,0,3,0 103 | cleve,34,1,1,118,182,0,2,174,0,0,1,0,3,0 104 | cleve,57,0,4,128,303,0,2,159,0,0,1,1,3,0 105 | cleve,71,0,3,110,265,1,2,130,0,0,1,1,3,0 106 | cleve,49,1,3,120,188,0,0,139,0,2,2,3,7,3 107 | cleve,54,1,2,108,309,0,0,156,0,0,1,0,7,0 108 | cleve,59,1,4,140,177,0,0,162,1,0,1,1,7,2 109 | cleve,57,1,3,128,229,0,2,150,0,0.4,2,1,7,1 110 | cleve,61,1,4,120,260,0,0,140,1,3.6,2,1,7,2 111 | cleve,39,1,4,118,219,0,0,140,0,1.2,2,0,7,3 112 | cleve,61,0,4,145,307,0,2,146,1,1,2,0,7,1 113 | cleve,56,1,4,125,249,1,2,144,1,1.2,2,1,3,1 114 | cleve,52,1,1,118,186,0,2,190,0,0,2,0,6,0 115 | cleve,43,0,4,132,341,1,2,136,1,3,2,0,7,2 116 | cleve,62,0,3,130,263,0,0,97,0,1.2,2,1,7,2 117 | cleve,41,1,2,135,203,0,0,132,0,0,2,0,6,0 118 | cleve,58,1,3,140,211,1,2,165,0,0,1,0,3,0 119 | cleve,35,0,4,138,183,0,0,182,0,1.4,1,0,3,0 120 | cleve,63,1,4,130,330,1,2,132,1,1.8,1,3,7,3 121 | cleve,65,1,4,135,254,0,2,127,0,2.8,2,1,7,2 122 | cleve,48,1,4,130,256,1,2,150,1,0,1,2,7,3 123 | cleve,63,0,4,150,407,0,2,154,0,4,2,3,7,4 124 | cleve,51,1,3,100,222,0,0,143,1,1.2,2,0,3,0 125 | cleve,55,1,4,140,217,0,0,111,1,5.6,3,0,7,3 126 | cleve,65,1,1,138,282,1,2,174,0,1.4,2,1,3,1 127 | cleve,45,0,2,130,234,0,2,175,0,0.6,2,0,3,0 128 | cleve,56,0,4,200,288,1,2,133,1,4,3,2,7,3 129 | cleve,54,1,4,110,239,0,0,126,1,2.8,2,1,7,3 130 | cleve,44,1,2,120,220,0,0,170,0,0,1,0,3,0 131 | cleve,62,0,4,124,209,0,0,163,0,0,1,0,3,0 132 | cleve,54,1,3,120,258,0,2,147,0,0.4,2,0,7,0 133 | cleve,51,1,3,94,227,0,0,154,1,0,1,1,7,0 134 | cleve,29,1,2,130,204,0,2,202,0,0,1,0,3,0 135 | cleve,51,1,4,140,261,0,2,186,1,0,1,0,3,0 136 | cleve,43,0,3,122,213,0,0,165,0,0.2,2,0,3,0 137 | cleve,55,0,2,135,250,0,2,161,0,1.4,2,0,3,0 138 | cleve,70,1,4,145,174,0,0,125,1,2.6,3,0,7,4 139 | cleve,62,1,2,120,281,0,2,103,0,1.4,2,1,7,3 140 | cleve,35,1,4,120,198,0,0,130,1,1.6,2,0,7,1 141 | cleve,51,1,3,125,245,1,2,166,0,2.4,2,0,3,0 142 | cleve,59,1,2,140,221,0,0,164,1,0,1,0,3,0 143 | cleve,59,1,1,170,288,0,2,159,0,0.2,2,0,7,1 144 | cleve,52,1,2,128,205,1,0,184,0,0,1,0,3,0 145 | cleve,64,1,3,125,309,0,0,131,1,1.8,2,0,7,1 146 | cleve,58,1,3,105,240,0,2,154,1,0.6,2,0,7,0 147 | cleve,47,1,3,108,243,0,0,152,0,0,1,0,3,1 148 | cleve,57,1,4,165,289,1,2,124,0,1,2,3,7,4 149 | cleve,41,1,3,112,250,0,0,179,0,0,1,0,3,0 150 | cleve,45,1,2,128,308,0,2,170,0,0,1,0,3,0 151 | cleve,60,0,3,102,318,0,0,160,0,0,1,1,3,0 152 | cleve,52,1,1,152,298,1,0,178,0,1.2,2,0,7,0 153 | cleve,42,0,4,102,265,0,2,122,0,0.6,2,0,3,0 154 | cleve,67,0,3,115,564,0,2,160,0,1.6,2,0,7,0 155 | cleve,55,1,4,160,289,0,2,145,1,0.8,2,1,7,4 156 | cleve,64,1,4,120,246,0,2,96,1,2.2,3,1,3,3 157 | cleve,70,1,4,130,322,0,2,109,0,2.4,2,3,3,1 158 | cleve,51,1,4,140,299,0,0,173,1,1.6,1,0,7,1 159 | cleve,58,1,4,125,300,0,2,171,0,0,1,2,7,1 160 | cleve,60,1,4,140,293,0,2,170,0,1.2,2,2,7,2 161 | cleve,68,1,3,118,277,0,0,151,0,1,1,1,7,0 162 | cleve,46,1,2,101,197,1,0,156,0,0,1,0,7,0 163 | cleve,77,1,4,125,304,0,2,162,1,0,1,3,3,4 164 | cleve,54,0,3,110,214,0,0,158,0,1.6,2,0,3,0 165 | cleve,58,0,4,100,248,0,2,122,0,1,2,0,3,0 166 | cleve,48,1,3,124,255,1,0,175,0,0,1,2,3,0 167 | cleve,57,1,4,132,207,0,0,168,1,0,1,0,7,0 168 | cleve,52,1,3,138,223,0,0,169,0,0,1,?,3,0 169 | cleve,54,0,2,132,288,1,2,159,1,0,1,1,3,0 170 | cleve,35,1,4,126,282,0,2,156,1,0,1,0,7,1 171 | cleve,45,0,2,112,160,0,0,138,0,0,2,0,3,0 172 | cleve,70,1,3,160,269,0,0,112,1,2.9,2,1,7,3 173 | cleve,53,1,4,142,226,0,2,111,1,0,1,0,7,0 174 | cleve,59,0,4,174,249,0,0,143,1,0,2,0,3,1 175 | cleve,62,0,4,140,394,0,2,157,0,1.2,2,0,3,0 176 | cleve,64,1,4,145,212,0,2,132,0,2,2,2,6,4 177 | cleve,57,1,4,152,274,0,0,88,1,1.2,2,1,7,1 178 | cleve,52,1,4,108,233,1,0,147,0,0.1,1,3,7,0 179 | cleve,56,1,4,132,184,0,2,105,1,2.1,2,1,6,1 180 | cleve,43,1,3,130,315,0,0,162,0,1.9,1,1,3,0 181 | cleve,53,1,3,130,246,1,2,173,0,0,1,3,3,0 182 | cleve,48,1,4,124,274,0,2,166,0,0.5,2,0,7,3 183 | cleve,56,0,4,134,409,0,2,150,1,1.9,2,2,7,2 184 | cleve,42,1,1,148,244,0,2,178,0,0.8,1,2,3,0 185 | cleve,59,1,1,178,270,0,2,145,0,4.2,3,0,7,0 186 | cleve,60,0,4,158,305,0,2,161,0,0,1,0,3,1 187 | cleve,63,0,2,140,195,0,0,179,0,0,1,2,3,0 188 | cleve,42,1,3,120,240,1,0,194,0,0.8,3,0,7,0 189 | cleve,66,1,2,160,246,0,0,120,1,0,2,3,6,2 190 | cleve,54,1,2,192,283,0,2,195,0,0,1,1,7,1 191 | cleve,69,1,3,140,254,0,2,146,0,2,2,3,7,2 192 | cleve,50,1,3,129,196,0,0,163,0,0,1,0,3,0 193 | cleve,51,1,4,140,298,0,0,122,1,4.2,2,3,7,3 194 | cleve,43,1,4,132,247,1,2,143,1,0.1,2,?,7,1 195 | cleve,62,0,4,138,294,1,0,106,0,1.9,2,3,3,2 196 | cleve,68,0,3,120,211,0,2,115,0,1.5,2,0,3,0 197 | cleve,67,1,4,100,299,0,2,125,1,0.9,2,2,3,3 198 | cleve,69,1,1,160,234,1,2,131,0,0.1,2,1,3,0 199 | cleve,45,0,4,138,236,0,2,152,1,0.2,2,0,3,0 200 | cleve,50,0,2,120,244,0,0,162,0,1.1,1,0,3,0 201 | cleve,59,1,1,160,273,0,2,125,0,0,1,0,3,1 202 | cleve,50,0,4,110,254,0,2,159,0,0,1,0,3,0 203 | cleve,64,0,4,180,325,0,0,154,1,0,1,0,3,0 204 | cleve,57,1,3,150,126,1,0,173,0,0.2,1,1,7,0 205 | cleve,64,0,3,140,313,0,0,133,0,0.2,1,0,7,0 206 | cleve,43,1,4,110,211,0,0,161,0,0,1,0,7,0 207 | cleve,45,1,4,142,309,0,2,147,1,0,2,3,7,3 208 | cleve,58,1,4,128,259,0,2,130,1,3,2,2,7,3 209 | cleve,50,1,4,144,200,0,2,126,1,0.9,2,0,7,3 210 | cleve,55,1,2,130,262,0,0,155,0,0,1,0,3,0 211 | cleve,62,0,4,150,244,0,0,154,1,1.4,2,0,3,1 212 | cleve,37,0,3,120,215,0,0,170,0,0,1,0,3,0 213 | cleve,38,1,1,120,231,0,0,182,1,3.8,2,0,7,4 214 | cleve,41,1,3,130,214,0,2,168,0,2,2,0,3,0 215 | cleve,66,0,4,178,228,1,0,165,1,1,2,2,7,3 216 | cleve,52,1,4,112,230,0,0,160,0,0,1,1,3,1 217 | cleve,56,1,1,120,193,0,2,162,0,1.9,2,0,7,0 218 | cleve,46,0,2,105,204,0,0,172,0,0,1,0,3,0 219 | cleve,46,0,4,138,243,0,2,152,1,0,2,0,3,0 220 | cleve,64,0,4,130,303,0,0,122,0,2,2,2,3,0 221 | cleve,59,1,4,138,271,0,2,182,0,0,1,0,3,0 222 | cleve,41,0,3,112,268,0,2,172,1,0,1,0,3,0 223 | cleve,54,0,3,108,267,0,2,167,0,0,1,0,3,0 224 | cleve,39,0,3,94,199,0,0,179,0,0,1,0,3,0 225 | cleve,53,1,4,123,282,0,0,95,1,2,2,2,7,3 226 | cleve,63,0,4,108,269,0,0,169,1,1.8,2,2,3,1 227 | cleve,34,0,2,118,210,0,0,192,0,0.7,1,0,3,0 228 | cleve,47,1,4,112,204,0,0,143,0,0.1,1,0,3,0 229 | cleve,67,0,3,152,277,0,0,172,0,0,1,1,3,0 230 | cleve,54,1,4,110,206,0,2,108,1,0,2,1,3,3 231 | cleve,66,1,4,112,212,0,2,132,1,0.1,1,1,3,2 232 | cleve,52,0,3,136,196,0,2,169,0,0.1,2,0,3,0 233 | cleve,55,0,4,180,327,0,1,117,1,3.4,2,0,3,2 234 | cleve,49,1,3,118,149,0,2,126,0,0.8,1,3,3,1 235 | cleve,74,0,2,120,269,0,2,121,1,0.2,1,1,3,0 236 | cleve,54,0,3,160,201,0,0,163,0,0,1,1,3,0 237 | cleve,54,1,4,122,286,0,2,116,1,3.2,2,2,3,3 238 | cleve,56,1,4,130,283,1,2,103,1,1.6,3,0,7,2 239 | cleve,46,1,4,120,249,0,2,144,0,0.8,1,0,7,1 240 | cleve,49,0,2,134,271,0,0,162,0,0,2,0,3,0 241 | cleve,42,1,2,120,295,0,0,162,0,0,1,0,3,0 242 | cleve,41,1,2,110,235,0,0,153,0,0,1,0,3,0 243 | cleve,41,0,2,126,306,0,0,163,0,0,1,0,3,0 244 | cleve,49,0,4,130,269,0,0,163,0,0,1,0,3,0 245 | cleve,61,1,1,134,234,0,0,145,0,2.6,2,2,3,2 246 | cleve,60,0,3,120,178,1,0,96,0,0,1,0,3,0 247 | cleve,67,1,4,120,237,0,0,71,0,1,2,0,3,2 248 | cleve,58,1,4,100,234,0,0,156,0,0.1,1,1,7,2 249 | cleve,47,1,4,110,275,0,2,118,1,1,2,1,3,1 250 | cleve,52,1,4,125,212,0,0,168,0,1,1,2,7,3 251 | cleve,62,1,2,128,208,1,2,140,0,0,1,0,3,0 252 | cleve,57,1,4,110,201,0,0,126,1,1.5,2,0,6,0 253 | cleve,58,1,4,146,218,0,0,105,0,2,2,1,7,1 254 | cleve,64,1,4,128,263,0,0,105,1,0.2,2,1,7,0 255 | cleve,51,0,3,120,295,0,2,157,0,0.6,1,0,3,0 256 | cleve,43,1,4,115,303,0,0,181,0,1.2,2,0,3,0 257 | cleve,42,0,3,120,209,0,0,173,0,0,2,0,3,0 258 | cleve,67,0,4,106,223,0,0,142,0,0.3,1,2,3,0 259 | cleve,76,0,3,140,197,0,1,116,0,1.1,2,0,3,0 260 | cleve,70,1,2,156,245,0,2,143,0,0,1,0,3,0 261 | cleve,57,1,2,124,261,0,0,141,0,0.3,1,0,7,1 262 | cleve,44,0,3,118,242,0,0,149,0,0.3,2,1,3,0 263 | cleve,58,0,2,136,319,1,2,152,0,0,1,2,3,3 264 | cleve,60,0,1,150,240,0,0,171,0,0.9,1,0,3,0 265 | cleve,44,1,3,120,226,0,0,169,0,0,1,0,3,0 266 | cleve,61,1,4,138,166,0,2,125,1,3.6,2,1,3,4 267 | cleve,42,1,4,136,315,0,0,125,1,1.8,2,0,6,2 268 | cleve,52,1,4,128,204,1,0,156,1,1,2,0,?,2 269 | cleve,59,1,3,126,218,1,0,134,0,2.2,2,1,6,2 270 | cleve,40,1,4,152,223,0,0,181,0,0,1,0,7,1 271 | cleve,42,1,3,130,180,0,0,150,0,0,1,0,3,0 272 | cleve,61,1,4,140,207,0,2,138,1,1.9,1,1,7,1 273 | cleve,66,1,4,160,228,0,2,138,0,2.3,1,0,6,0 274 | cleve,46,1,4,140,311,0,0,120,1,1.8,2,2,7,2 275 | cleve,71,0,4,112,149,0,0,125,0,1.6,2,0,3,0 276 | cleve,59,1,1,134,204,0,0,162,0,0.8,1,2,3,1 277 | cleve,64,1,1,170,227,0,2,155,0,0.6,2,0,7,0 278 | cleve,66,0,3,146,278,0,2,152,0,0,2,1,3,0 279 | cleve,39,0,3,138,220,0,0,152,0,0,2,0,3,0 280 | cleve,57,1,2,154,232,0,2,164,0,0,1,1,3,1 281 | cleve,58,0,4,130,197,0,0,131,0,0.6,2,0,3,0 282 | cleve,57,1,4,110,335,0,0,143,1,3,2,1,7,2 283 | cleve,47,1,3,130,253,0,0,179,0,0,1,0,3,0 284 | cleve,55,0,4,128,205,0,1,130,1,2,2,1,7,3 285 | cleve,35,1,2,122,192,0,0,174,0,0,1,0,3,0 286 | cleve,61,1,4,148,203,0,0,161,0,0,1,1,7,2 287 | cleve,58,1,4,114,318,0,1,140,0,4.4,3,3,6,4 288 | cleve,58,0,4,170,225,1,2,146,1,2.8,2,2,6,2 289 | cleve,58,1,2,125,220,0,0,144,0,0.4,2,?,7,0 290 | cleve,56,1,2,130,221,0,2,163,0,0,1,0,7,0 291 | cleve,56,1,2,120,240,0,0,169,0,0,3,0,3,0 292 | cleve,67,1,3,152,212,0,2,150,0,0.8,2,0,7,1 293 | cleve,55,0,2,132,342,0,0,166,0,1.2,1,0,3,0 294 | cleve,44,1,4,120,169,0,0,144,1,2.8,3,0,6,2 295 | cleve,63,1,4,140,187,0,2,144,1,4,1,2,7,2 296 | cleve,63,0,4,124,197,0,0,136,1,0,2,0,3,1 297 | cleve,41,1,2,120,157,0,0,182,0,0,1,0,3,0 298 | cleve,59,1,4,164,176,1,2,90,0,1,2,2,6,3 299 | cleve,57,0,4,140,241,0,0,123,1,0.2,2,0,7,1 300 | cleve,45,1,1,110,264,0,0,132,0,1.2,2,0,7,1 301 | cleve,68,1,4,144,193,1,0,141,0,3.4,2,2,7,2 302 | cleve,57,1,4,130,131,0,0,115,1,1.2,2,1,7,3 303 | cleve,57,0,2,130,236,0,2,174,0,0,2,1,3,1 304 | cleve,38,1,3,138,175,0,0,173,0,0,1,?,3,0 305 | swiss,32,1,1,95,0,?,0,127,0,0.7,1,?,?,1 306 | swiss,34,1,4,115,0,?,?,154,0,0.2,1,?,?,1 307 | swiss,35,1,4,?,0,?,0,130,1,?,?,?,7,3 308 | swiss,36,1,4,110,0,?,0,125,1,1,2,?,6,1 309 | swiss,38,0,4,105,0,?,0,166,0,2.8,1,?,?,2 310 | swiss,38,0,4,110,0,0,0,156,0,0,2,?,3,1 311 | swiss,38,1,3,100,0,?,0,179,0,-1.1,1,?,?,0 312 | swiss,38,1,3,115,0,0,0,128,1,0,2,?,7,1 313 | swiss,38,1,4,135,0,?,0,150,0,0,?,?,3,2 314 | swiss,38,1,4,150,0,?,0,120,1,?,?,?,3,1 315 | swiss,40,1,4,95,0,?,1,144,0,0,1,?,?,2 316 | swiss,41,1,4,125,0,?,0,176,0,1.6,1,?,?,2 317 | swiss,42,1,4,105,0,?,0,128,1,-1.5,3,?,?,1 318 | swiss,42,1,4,145,0,0,0,99,1,0,2,?,?,2 319 | swiss,43,1,4,100,0,?,0,122,0,1.5,3,?,?,3 320 | swiss,43,1,4,115,0,0,0,145,1,2,2,?,7,4 321 | swiss,43,1,4,140,0,0,1,140,1,0.5,1,?,7,2 322 | swiss,45,1,3,110,0,?,0,138,0,-0.1,1,?,?,0 323 | swiss,46,1,4,100,0,?,1,133,0,-2.6,2,?,?,1 324 | swiss,46,1,4,115,0,0,0,113,1,1.5,2,?,7,1 325 | swiss,47,1,3,110,0,?,0,120,1,0,?,?,3,1 326 | swiss,47,1,3,155,0,0,0,118,1,1,2,?,3,3 327 | swiss,47,1,4,110,0,?,1,149,0,2.1,1,?,?,2 328 | swiss,47,1,4,160,0,0,0,124,1,0,2,?,7,1 329 | swiss,48,1,4,115,0,?,0,128,0,0,2,?,6,2 330 | swiss,50,0,4,160,0,?,0,110,0,0,?,?,3,1 331 | swiss,50,1,4,115,0,0,0,120,1,0.5,2,?,6,3 332 | swiss,50,1,4,120,0,0,1,156,1,0,1,?,6,3 333 | swiss,50,1,4,145,0,?,0,139,1,0.7,2,?,?,1 334 | swiss,51,0,4,120,0,?,0,127,1,1.5,1,?,?,2 335 | swiss,51,1,4,110,0,?,0,92,0,0,2,?,?,4 336 | swiss,51,1,4,120,0,1,0,104,0,0,2,?,3,3 337 | swiss,51,1,4,130,0,?,0,170,0,-0.7,1,?,?,2 338 | swiss,51,1,4,130,0,?,1,163,0,0,?,?,7,1 339 | swiss,51,1,4,140,0,0,0,60,0,0,2,?,3,2 340 | swiss,51,1,4,95,0,?,0,126,0,2.2,2,?,?,2 341 | swiss,52,1,4,130,0,?,0,120,0,0,2,?,7,2 342 | swiss,52,1,4,135,0,?,0,128,1,2,2,?,7,2 343 | swiss,52,1,4,165,0,?,0,122,1,1,1,?,7,2 344 | swiss,52,1,4,95,0,?,0,82,1,?,?,?,?,2 345 | swiss,53,1,2,120,0,0,0,95,0,0,2,?,3,3 346 | swiss,53,1,2,130,0,?,1,120,0,0.7,3,?,?,0 347 | swiss,53,1,3,105,0,0,0,115,0,0,2,?,7,1 348 | swiss,53,1,3,160,0,?,2,122,1,0,?,?,7,1 349 | swiss,53,1,4,120,0,?,0,120,0,0,2,?,7,1 350 | swiss,53,1,4,125,0,?,0,120,0,1.5,1,?,?,4 351 | swiss,53,1,4,130,0,0,2,135,1,1,2,?,7,2 352 | swiss,53,1,4,80,0,?,0,141,1,2,3,?,?,0 353 | swiss,54,1,4,120,0,0,0,155,0,0,2,?,7,2 354 | swiss,54,1,4,130,0,?,0,110,1,3,2,?,7,3 355 | swiss,54,1,4,180,0,?,0,150,0,1.5,2,?,7,1 356 | swiss,55,1,2,140,0,?,1,150,0,0.2,1,?,?,0 357 | swiss,55,1,4,115,0,?,0,155,0,0.1,2,?,?,1 358 | swiss,55,1,4,120,0,0,1,92,0,0.3,1,?,7,4 359 | swiss,55,1,4,140,0,0,0,83,0,0,2,?,7,2 360 | swiss,56,1,3,120,0,0,0,97,0,0,2,?,7,0 361 | swiss,56,1,3,125,0,?,0,98,0,-2,2,?,7,2 362 | swiss,56,1,3,155,0,0,1,99,0,0,2,?,3,2 363 | swiss,56,1,4,115,0,?,1,82,0,-1,1,?,?,1 364 | swiss,56,1,4,120,0,0,1,100,1,-1,3,?,7,2 365 | swiss,56,1,4,120,0,0,1,148,0,0,2,?,?,2 366 | swiss,56,1,4,125,0,1,0,103,1,1,2,?,7,3 367 | swiss,56,1,4,140,0,?,0,121,1,1.8,1,?,?,1 368 | swiss,57,1,3,105,0,?,0,148,0,0.3,2,?,?,1 369 | swiss,57,1,4,110,0,?,1,131,1,1.4,1,1,?,3 370 | swiss,57,1,4,140,0,0,0,120,1,2,2,?,6,2 371 | swiss,57,1,4,140,0,?,0,100,1,0,?,?,6,3 372 | swiss,57,1,4,160,0,?,0,98,1,2,2,?,7,2 373 | swiss,57,1,4,95,0,?,0,182,0,0.7,3,?,?,1 374 | swiss,58,1,4,115,0,?,0,138,0,0.5,1,?,?,1 375 | swiss,58,1,4,130,0,0,1,100,1,1,2,?,6,4 376 | swiss,58,1,4,170,0,?,1,105,1,0,?,?,3,1 377 | swiss,59,1,3,125,0,?,0,175,0,2.6,2,?,?,1 378 | swiss,59,1,4,110,0,?,0,94,0,0,?,?,6,3 379 | swiss,59,1,4,120,0,0,0,115,0,0,2,?,3,2 380 | swiss,59,1,4,125,0,?,0,119,1,0.9,1,?,?,1 381 | swiss,59,1,4,135,0,0,0,115,1,1,2,?,7,1 382 | swiss,60,1,3,115,0,?,0,143,0,2.4,1,?,?,1 383 | swiss,60,1,4,125,0,?,0,110,0,0.1,1,2,?,3 384 | swiss,60,1,4,130,0,?,1,130,1,1.1,3,1,?,1 385 | swiss,60,1,4,135,0,0,0,63,1,0.5,1,?,7,3 386 | swiss,60,1,4,160,0,0,1,99,1,0.5,2,?,7,3 387 | swiss,60,1,4,160,0,?,0,149,0,0.4,2,?,?,1 388 | swiss,61,1,3,200,0,?,1,70,0,0,?,?,3,3 389 | swiss,61,1,4,105,0,?,0,110,1,1.5,1,?,?,1 390 | swiss,61,1,4,110,0,?,0,113,0,1.4,2,?,?,1 391 | swiss,61,1,4,125,0,0,0,105,1,0,3,?,7,3 392 | swiss,61,1,4,130,0,0,2,115,0,0,2,?,7,3 393 | swiss,61,1,4,130,0,?,0,77,0,2.5,2,?,?,3 394 | swiss,61,1,4,150,0,0,0,105,1,0,2,?,7,1 395 | swiss,61,1,4,150,0,0,0,117,1,2,2,?,7,2 396 | swiss,61,1,4,160,0,1,1,145,0,1,2,?,7,2 397 | swiss,62,0,1,140,0,?,0,143,0,0,?,?,3,2 398 | swiss,62,0,4,120,0,?,1,123,1,1.7,3,?,?,1 399 | swiss,62,1,1,120,0,?,2,134,0,-0.8,2,2,?,1 400 | swiss,62,1,3,160,0,0,0,72,1,0,2,?,3,3 401 | swiss,62,1,4,115,0,?,0,128,1,2.5,3,?,?,2 402 | swiss,62,1,4,115,0,?,0,72,1,-0.5,2,?,3,1 403 | swiss,62,1,4,150,0,?,1,78,0,2,2,?,7,3 404 | swiss,63,1,4,100,0,?,0,109,0,-0.9,2,?,?,1 405 | swiss,63,1,4,140,0,?,2,149,0,2,1,?,?,2 406 | swiss,63,1,4,150,0,0,0,86,1,2,2,?,?,3 407 | swiss,63,1,4,150,0,?,1,154,0,3.7,1,?,?,3 408 | swiss,63,1,4,185,0,0,0,98,1,0,1,?,7,1 409 | swiss,64,0,4,200,0,0,0,140,1,1,2,?,3,3 410 | swiss,64,0,4,95,0,?,0,145,0,1.1,3,?,?,1 411 | swiss,64,1,4,110,0,?,0,114,1,1.3,3,?,?,1 412 | swiss,65,1,4,115,0,0,0,93,1,0,2,?,7,1 413 | swiss,65,1,4,145,0,?,1,67,0,?,?,?,6,3 414 | swiss,65,1,4,155,0,?,0,154,0,1,1,?,?,0 415 | swiss,65,1,4,160,0,1,1,122,0,?,?,?,7,3 416 | swiss,66,0,4,155,0,?,0,90,0,0,?,?,7,1 417 | swiss,66,1,4,150,0,0,0,108,1,2,2,?,7,3 418 | swiss,67,1,1,145,0,0,2,125,0,0,2,?,3,2 419 | swiss,68,1,4,135,0,0,1,120,1,0,1,?,7,3 420 | swiss,68,1,4,145,0,?,0,136,0,1.8,1,?,?,1 421 | swiss,69,1,4,135,0,0,0,130,0,0,2,?,6,1 422 | swiss,69,1,4,?,0,0,1,?,?,?,?,?,7,3 423 | swiss,70,1,4,115,0,0,1,92,1,0,2,?,7,1 424 | swiss,70,1,4,140,0,1,0,157,1,2,2,?,7,3 425 | swiss,72,1,3,160,0,?,2,114,0,1.6,2,2,?,0 426 | swiss,73,0,3,160,0,0,1,121,0,0,1,?,3,1 427 | swiss,74,1,2,145,0,?,1,123,0,1.3,1,?,?,1 428 | va,63,1,4,140,260,0,1,112,1,3,2,?,?,2 429 | va,44,1,4,130,209,0,1,127,0,0,?,?,?,0 430 | va,60,1,4,132,218,0,1,140,1,1.5,3,?,?,2 431 | va,55,1,4,142,228,0,1,149,1,2.5,1,?,?,1 432 | va,66,1,3,110,213,1,2,99,1,1.3,2,?,?,0 433 | va,66,1,3,120,0,0,1,120,0,-0.5,1,?,?,0 434 | va,65,1,4,150,236,1,1,105,1,0,?,?,?,3 435 | va,60,1,3,180,0,0,1,140,1,1.5,2,?,?,0 436 | va,60,1,3,120,0,?,0,141,1,2,1,?,?,3 437 | va,60,1,2,160,267,1,1,157,0,0.5,2,?,?,1 438 | va,56,1,2,126,166,0,1,140,0,0,?,?,?,0 439 | va,59,1,4,140,0,0,1,117,1,1,2,?,?,1 440 | va,62,1,4,110,0,0,0,120,1,0.5,2,?,3,1 441 | va,63,1,3,?,0,0,2,?,?,?,?,?,?,1 442 | va,57,1,4,128,0,1,1,148,1,1,2,?,?,1 443 | va,62,1,4,120,220,0,1,86,0,0,?,?,?,0 444 | va,63,1,4,170,177,0,0,84,1,2.5,3,?,?,4 445 | va,46,1,4,110,236,0,0,125,1,2,2,?,?,1 446 | va,63,1,4,126,0,0,1,120,0,1.5,3,?,?,0 447 | va,60,1,4,152,0,0,1,118,1,0,?,?,7,0 448 | va,58,1,4,116,0,0,0,124,0,1,1,?,?,2 449 | va,64,1,4,120,0,1,1,106,0,2,2,?,?,1 450 | va,63,1,3,130,0,0,1,111,1,0,?,?,?,3 451 | va,74,1,3,?,0,0,0,?,?,?,?,?,?,0 452 | va,52,1,3,128,0,0,1,180,0,3,1,?,?,2 453 | va,69,1,4,130,0,1,1,129,0,1,2,?,6,2 454 | va,51,1,4,?,0,1,1,?,?,?,?,?,?,2 455 | va,60,1,4,130,186,1,1,140,1,0.5,2,?,?,1 456 | va,56,1,4,120,100,0,0,120,1,1.5,2,0,7,1 457 | va,55,1,3,?,228,0,1,?,?,?,?,?,?,3 458 | va,54,1,4,?,0,0,1,?,?,?,?,?,?,3 459 | va,77,1,4,124,171,0,1,110,1,2,1,?,?,3 460 | va,63,1,4,160,230,1,0,105,1,1,2,?,?,3 461 | va,55,1,3,0,0,0,0,155,0,1.5,2,?,?,3 462 | va,52,1,3,122,0,0,0,110,1,2,3,?,?,2 463 | va,64,1,4,144,0,0,1,122,1,1,2,?,?,3 464 | va,60,1,4,?,281,0,1,?,?,?,?,?,?,2 465 | va,60,1,4,120,0,0,0,133,1,2,1,?,7,0 466 | va,58,1,4,?,203,1,0,?,?,?,?,?,?,1 467 | va,59,1,4,154,0,0,1,131,1,1.5,?,0,?,0 468 | va,61,1,3,120,0,0,0,80,1,0,2,?,?,3 469 | va,40,1,4,125,0,1,0,165,0,0,?,?,7,1 470 | va,61,1,4,?,0,1,1,86,0,1.5,2,?,7,3 471 | va,41,1,4,104,0,0,1,111,0,0,?,?,?,0 472 | va,57,1,4,?,277,1,1,?,?,?,?,?,?,4 473 | va,63,1,4,136,0,0,0,84,1,0,?,?,7,2 474 | va,59,1,4,122,233,0,0,117,1,1.3,3,?,?,1 475 | va,51,1,4,128,0,0,0,107,0,0,?,?,?,0 476 | va,59,1,3,?,0,0,0,128,1,2,3,?,?,2 477 | va,42,1,3,134,240,?,0,160,0,0,?,?,?,0 478 | va,55,1,3,120,0,0,1,125,1,2.5,2,?,7,1 479 | va,63,0,2,?,0,0,0,?,?,?,?,?,?,0 480 | va,62,1,4,152,153,0,1,97,1,1.6,1,?,7,2 481 | va,56,1,2,124,224,1,0,161,0,2,2,?,?,0 482 | va,53,1,4,126,0,0,0,106,0,0,?,?,?,1 483 | va,68,1,4,138,0,0,0,130,1,3,2,?,?,2 484 | va,53,1,4,154,0,?,1,140,1,1.5,2,?,?,2 485 | va,60,1,3,?,316,1,1,?,?,?,?,?,?,3 486 | va,62,1,2,?,0,0,0,?,?,?,?,?,?,0 487 | va,59,1,4,178,0,1,2,120,1,0,?,?,7,1 488 | va,51,1,4,?,218,1,2,?,?,?,?,?,?,0 489 | va,61,1,4,110,0,?,0,108,1,2,3,?,?,2 490 | va,57,1,4,130,311,?,1,148,1,2,2,?,?,1 491 | va,56,1,3,170,0,0,2,123,1,2.5,?,?,?,4 492 | va,58,1,2,126,0,1,0,110,1,2,2,?,?,2 493 | va,69,1,3,140,0,?,1,118,0,2.5,3,?,?,2 494 | va,67,1,1,142,270,1,0,125,0,2.5,1,?,?,3 495 | va,58,1,4,120,0,0,2,106,1,1.5,3,?,7,1 496 | va,65,1,4,?,0,0,0,?,?,?,?,?,?,1 497 | va,63,1,2,?,217,1,1,?,?,?,?,?,?,1 498 | va,55,1,2,110,214,1,1,180,0,?,?,?,?,0 499 | va,57,1,4,140,214,0,1,144,1,2,2,?,6,2 500 | va,65,1,1,?,252,0,0,?,?,?,?,?,?,0 501 | va,54,1,4,136,220,0,0,140,1,3,2,?,?,3 502 | va,72,1,3,120,214,0,0,102,1,1,2,?,?,3 503 | va,75,1,4,170,203,1,1,108,0,0,?,?,7,1 504 | va,49,1,1,130,0,0,1,145,0,3,2,?,?,2 505 | va,51,1,3,?,339,0,0,?,?,?,?,?,?,3 506 | va,60,1,4,142,216,0,0,110,1,2.5,2,?,?,2 507 | va,64,0,4,142,276,0,0,140,1,1,2,?,7,1 508 | va,58,1,4,132,458,1,0,69,0,1,3,?,?,0 509 | va,61,1,4,146,241,0,0,148,1,3,3,?,?,2 510 | va,67,1,4,160,384,1,1,130,1,0,2,?,?,2 511 | va,62,1,4,135,297,0,0,130,1,1,2,?,?,2 512 | va,65,1,4,136,248,0,0,140,1,4,3,?,?,4 513 | va,63,1,4,130,308,0,0,138,1,2,2,?,?,2 514 | va,69,1,4,140,208,0,1,140,1,2,?,?,?,3 515 | va,51,1,4,?,227,1,1,?,?,?,?,?,?,0 516 | va,62,1,4,158,210,1,0,112,1,3,3,?,?,1 517 | va,55,1,3,?,245,1,1,?,?,?,?,?,?,1 518 | va,75,1,4,136,225,0,0,112,1,3,2,?,?,3 519 | va,40,1,3,106,240,0,0,80,1,0,?,?,7,0 520 | va,67,1,4,120,0,1,0,150,0,1.5,3,?,?,3 521 | va,58,1,4,110,198,0,0,110,0,0,?,?,?,1 522 | va,60,1,4,?,195,0,0,?,?,?,?,?,?,0 523 | va,63,1,4,160,267,1,1,88,1,2,?,?,?,3 524 | va,35,1,3,?,161,0,1,?,?,?,?,?,?,0 525 | va,62,1,1,112,258,0,1,150,1,?,?,?,?,1 526 | va,43,1,4,122,0,0,0,120,0,0.5,1,?,?,1 527 | va,63,1,3,130,0,1,1,160,0,3,2,?,?,0 528 | va,68,1,3,150,195,1,0,132,0,0,?,?,6,1 529 | va,65,1,4,150,235,0,0,120,1,1.5,2,?,?,3 530 | va,48,1,3,102,0,?,1,110,1,1,3,?,?,1 531 | va,63,1,4,96,305,0,1,121,1,1,1,?,?,1 532 | va,64,1,4,130,223,0,1,128,0,0.5,2,?,?,0 533 | va,61,1,4,120,282,0,1,135,1,4,3,?,6,3 534 | va,50,1,4,144,349,0,2,120,1,1,1,?,7,1 535 | va,59,1,4,124,?,0,0,117,1,1,2,?,?,1 536 | va,55,1,4,150,160,0,1,150,0,0,?,?,?,0 537 | va,45,1,3,?,236,0,0,?,?,?,?,?,?,0 538 | va,65,1,4,?,312,0,2,?,?,?,?,?,?,3 539 | va,61,1,2,?,283,0,0,?,?,?,?,?,?,0 540 | va,49,1,3,?,142,0,0,?,?,?,?,?,?,3 541 | va,72,1,4,?,211,0,0,?,?,?,?,?,?,1 542 | va,50,1,4,?,218,0,0,?,?,?,?,?,?,1 543 | va,64,1,4,?,306,1,1,?,?,?,?,?,?,3 544 | va,55,1,4,116,186,1,1,102,0,0,?,?,?,2 545 | va,63,1,4,110,252,0,1,140,1,2,2,?,?,2 546 | va,59,1,4,125,222,0,0,135,1,2.5,3,?,?,3 547 | va,56,1,4,?,0,0,2,?,?,?,?,?,?,1 548 | va,62,1,3,?,0,1,1,?,?,?,?,?,?,2 549 | va,74,1,4,150,258,1,1,130,1,4,3,?,?,3 550 | va,54,1,4,130,202,1,0,112,1,2,2,?,?,1 551 | va,57,1,4,110,197,0,2,100,0,0,?,?,?,0 552 | va,62,1,3,?,204,0,1,?,?,?,?,?,?,1 553 | va,76,1,3,104,?,0,2,120,0,3.5,3,?,?,4 554 | va,54,0,4,138,274,0,0,105,1,1.5,2,?,?,1 555 | va,70,1,4,170,192,0,1,129,1,3,3,?,?,2 556 | va,61,0,2,140,298,1,0,120,1,0,?,?,7,0 557 | va,48,1,4,?,272,0,1,?,?,?,?,?,?,0 558 | va,48,1,3,132,220,1,1,162,0,0,?,?,6,1 559 | va,61,1,1,142,200,1,1,100,0,1.5,3,?,?,3 560 | va,66,1,4,112,261,0,0,140,0,1.5,1,?,?,1 561 | va,68,1,1,?,181,1,1,?,?,?,?,?,?,0 562 | va,55,1,4,172,260,0,0,73,0,2,?,?,?,3 563 | va,62,1,3,120,220,0,2,86,0,0,?,?,?,0 564 | va,71,1,3,?,221,0,0,?,?,?,?,?,?,3 565 | va,74,1,1,?,216,1,0,?,?,?,?,?,?,3 566 | va,53,1,3,155,175,1,1,160,0,?,?,?,6,0 567 | va,58,1,3,150,219,0,1,118,1,0,?,?,?,2 568 | va,75,1,4,160,310,1,0,112,1,2,3,?,7,0 569 | va,56,1,3,?,208,1,1,?,?,?,?,?,?,4 570 | va,58,1,3,?,232,0,1,?,?,?,?,?,?,2 571 | va,64,1,4,134,273,0,0,102,1,4,3,?,?,4 572 | va,54,1,3,?,203,0,1,?,?,?,?,?,?,0 573 | va,54,1,2,?,182,0,1,?,?,?,?,?,?,0 574 | va,59,1,4,140,274,0,0,154,1,2,2,?,?,0 575 | va,55,1,4,?,204,1,1,?,?,?,?,?,?,1 576 | va,57,1,4,144,270,1,1,160,1,2,2,?,?,3 577 | va,61,1,4,?,292,0,1,?,?,?,?,?,?,3 578 | va,41,1,4,150,171,0,0,128,1,1.5,2,?,?,0 579 | va,71,1,4,130,221,0,1,115,1,0,?,?,?,3 580 | va,38,1,4,110,289,0,0,105,1,1.5,3,?,?,1 581 | va,55,1,4,158,217,0,0,110,1,2.5,2,?,?,1 582 | va,56,1,4,128,223,0,1,119,1,2,3,?,?,2 583 | va,69,1,4,?,?,1,0,?,?,?,?,?,?,2 584 | va,64,1,4,150,193,0,1,135,1,0.5,2,?,?,2 585 | va,72,1,4,160,?,1,2,130,0,1.5,?,?,?,2 586 | va,69,1,4,?,210,1,1,?,?,?,?,?,?,2 587 | va,56,1,4,?,282,1,0,?,?,?,?,?,?,1 588 | va,62,1,4,?,170,0,1,120,1,3,?,?,?,4 589 | va,67,1,4,?,369,0,0,?,?,?,?,?,?,3 590 | va,57,1,4,156,173,0,2,119,1,3,3,?,?,3 591 | va,69,1,4,?,289,1,1,?,?,?,?,?,?,3 592 | va,51,1,4,?,?,1,2,?,?,?,?,?,7,1 593 | va,48,1,4,140,?,0,0,159,1,1.5,1,?,?,3 594 | va,69,1,4,122,216,1,2,84,1,0,?,?,7,2 595 | va,69,1,3,?,271,0,2,?,?,?,?,?,?,0 596 | va,64,1,4,?,244,1,1,?,?,?,?,?,?,2 597 | va,57,1,2,180,285,1,1,120,0,0.8,?,?,?,1 598 | va,53,1,4,124,243,0,0,122,1,2,2,?,7,1 599 | va,37,1,3,118,240,0,2,165,0,1,2,?,3,0 600 | va,67,1,4,140,219,0,1,122,1,2,2,?,7,3 601 | va,74,1,3,140,237,1,0,94,0,0,?,?,?,1 602 | va,63,1,2,?,165,0,1,?,?,?,?,?,?,0 603 | va,58,1,4,100,213,0,1,110,0,0,?,?,?,0 604 | va,61,1,4,190,287,1,2,150,1,2,3,?,?,4 605 | va,64,1,4,130,258,1,2,130,0,0,?,?,6,2 606 | va,58,1,4,160,256,1,2,113,1,1,1,?,?,3 607 | va,60,1,4,130,186,1,2,140,1,0.5,2,?,?,1 608 | va,57,1,4,122,264,0,2,100,0,0,?,?,?,1 609 | va,55,1,3,?,?,0,1,?,?,?,?,?,?,0 610 | va,55,1,4,120,226,0,2,127,1,1.7,3,?,7,1 611 | va,56,1,4,130,203,1,0,98,0,1.5,2,?,7,1 612 | va,57,1,4,130,207,0,1,96,1,1,2,?,?,0 613 | va,61,1,3,?,284,0,0,?,?,?,?,?,?,1 614 | va,61,1,3,120,337,0,0,98,1,0,?,?,?,3 615 | va,58,1,3,150,219,0,1,118,1,0,?,?,?,2 616 | va,74,1,4,155,310,0,0,112,1,1.5,3,?,?,2 617 | va,68,1,3,134,254,1,0,151,1,0,?,?,3,0 618 | va,51,0,4,114,258,1,2,96,0,1,1,?,?,0 619 | va,62,1,4,160,254,1,1,108,1,3,2,?,?,4 620 | va,53,1,4,144,300,1,1,128,1,1.5,2,?,?,3 621 | va,62,1,4,158,170,0,1,138,1,0,?,?,?,1 622 | va,46,1,4,134,310,0,0,126,0,0,?,?,3,2 623 | va,54,0,4,127,333,1,1,154,0,0,?,?,?,1 624 | va,62,1,1,?,139,0,1,?,?,?,?,?,?,0 625 | va,55,1,4,122,223,1,1,100,0,0,?,?,6,2 626 | va,58,1,4,?,385,1,2,?,?,?,?,?,?,0 627 | va,62,1,2,120,254,0,2,93,1,0,?,?,?,1 628 | hungary,28,1,2,130,132,0,2,185,0,0,?,?,?,0 629 | hungary,29,1,2,120,243,0,0,160,0,0,?,?,?,0 630 | hungary,29,1,2,140,?,0,0,170,0,0,?,?,?,0 631 | hungary,30,0,1,170,237,0,1,170,0,0,?,?,6,0 632 | hungary,31,0,2,100,219,0,1,150,0,0,?,?,?,0 633 | hungary,32,0,2,105,198,0,0,165,0,0,?,?,?,0 634 | hungary,32,1,2,110,225,0,0,184,0,0,?,?,?,0 635 | hungary,32,1,2,125,254,0,0,155,0,0,?,?,?,0 636 | hungary,33,1,3,120,298,0,0,185,0,0,?,?,?,0 637 | hungary,34,0,2,130,161,0,0,190,0,0,?,?,?,0 638 | hungary,34,1,2,150,214,0,1,168,0,0,?,?,?,0 639 | hungary,34,1,2,98,220,0,0,150,0,0,?,?,?,0 640 | hungary,35,0,1,120,160,0,1,185,0,0,?,?,?,0 641 | hungary,35,0,4,140,167,0,0,150,0,0,?,?,?,0 642 | hungary,35,1,2,120,308,0,2,180,0,0,?,?,?,0 643 | hungary,35,1,2,150,264,0,0,168,0,0,?,?,?,0 644 | hungary,36,1,2,120,166,0,0,180,0,0,?,?,?,0 645 | hungary,36,1,3,112,340,0,0,184,0,1,2,?,3,0 646 | hungary,36,1,3,130,209,0,0,178,0,0,?,?,?,0 647 | hungary,36,1,3,150,160,0,0,172,0,0,?,?,?,0 648 | hungary,37,0,2,120,260,0,0,130,0,0,?,?,?,0 649 | hungary,37,0,3,130,211,0,0,142,0,0,?,?,?,0 650 | hungary,37,0,4,130,173,0,1,184,0,0,?,?,?,0 651 | hungary,37,1,2,130,283,0,1,98,0,0,?,?,?,0 652 | hungary,37,1,3,130,194,0,0,150,0,0,?,?,?,0 653 | hungary,37,1,4,120,223,0,0,168,0,0,?,?,3,0 654 | hungary,37,1,4,130,315,0,0,158,0,0,?,?,?,0 655 | hungary,38,0,2,120,275,?,0,129,0,0,?,?,?,0 656 | hungary,38,1,2,140,297,0,0,150,0,0,?,?,?,0 657 | hungary,38,1,3,145,292,0,0,130,0,0,?,?,?,0 658 | hungary,39,0,3,110,182,0,1,180,0,0,?,?,?,0 659 | hungary,39,1,2,120,?,0,1,146,0,2,1,?,?,0 660 | hungary,39,1,2,120,200,0,0,160,1,1,2,?,?,0 661 | hungary,39,1,2,120,204,0,0,145,0,0,?,?,?,0 662 | hungary,39,1,2,130,?,0,0,120,0,0,?,?,?,0 663 | hungary,39,1,2,190,241,0,0,106,0,0,?,?,?,0 664 | hungary,39,1,3,120,339,0,0,170,0,0,?,?,?,0 665 | hungary,39,1,3,160,147,1,0,160,0,0,?,?,?,0 666 | hungary,39,1,4,110,273,0,0,132,0,0,?,?,?,0 667 | hungary,39,1,4,130,307,0,0,140,0,0,?,?,?,0 668 | hungary,40,1,2,130,275,0,0,150,0,0,?,?,?,0 669 | hungary,40,1,2,140,289,0,0,172,0,0,?,?,?,0 670 | hungary,40,1,3,130,215,0,0,138,0,0,?,?,?,0 671 | hungary,40,1,3,130,281,0,0,167,0,0,?,?,?,0 672 | hungary,40,1,3,140,?,0,0,188,0,0,?,?,?,0 673 | hungary,41,0,2,110,250,0,1,142,0,0,?,?,?,0 674 | hungary,41,0,2,125,184,0,0,180,0,0,?,?,?,0 675 | hungary,41,0,2,130,245,0,0,150,0,0,?,?,?,0 676 | hungary,41,1,2,120,291,0,1,160,0,0,?,?,?,0 677 | hungary,41,1,2,120,295,0,0,170,0,0,?,?,?,0 678 | hungary,41,1,2,125,269,0,0,144,0,0,?,?,?,0 679 | hungary,41,1,4,112,250,0,0,142,0,0,?,?,?,0 680 | hungary,42,0,3,115,211,0,1,137,0,0,?,?,?,0 681 | hungary,42,1,2,120,196,0,0,150,0,0,?,?,?,0 682 | hungary,42,1,2,120,198,0,0,155,0,0,?,?,?,0 683 | hungary,42,1,2,150,268,0,0,136,0,0,?,?,?,0 684 | hungary,42,1,3,120,228,0,0,152,1,1.5,2,?,?,0 685 | hungary,42,1,3,160,147,0,0,146,0,0,?,?,?,0 686 | hungary,42,1,4,140,358,0,0,170,0,0,?,?,?,0 687 | hungary,43,0,1,100,223,0,0,142,0,0,?,?,?,0 688 | hungary,43,0,2,120,201,0,0,165,0,0,?,?,?,0 689 | hungary,43,0,2,120,215,0,1,175,0,0,?,?,?,0 690 | hungary,43,0,2,120,249,0,1,176,0,0,?,?,?,0 691 | hungary,43,0,2,120,266,0,0,118,0,0,?,?,?,0 692 | hungary,43,0,2,150,186,0,0,154,0,0,?,?,?,0 693 | hungary,43,0,3,150,?,0,0,175,0,0,?,?,3,0 694 | hungary,43,1,2,142,207,0,0,138,0,0,?,?,?,0 695 | hungary,44,0,4,120,218,0,1,115,0,0,?,?,?,0 696 | hungary,44,1,2,120,184,0,0,142,0,1,2,?,?,0 697 | hungary,44,1,2,130,215,0,0,135,0,0,?,?,?,0 698 | hungary,44,1,4,150,412,0,0,170,0,0,?,?,?,0 699 | hungary,45,0,2,130,237,0,0,170,0,0,?,?,?,0 700 | hungary,45,0,2,180,?,0,0,180,0,0,?,?,?,0 701 | hungary,45,0,4,132,297,0,0,144,0,0,?,?,?,0 702 | hungary,45,1,2,140,224,1,0,122,0,0,?,?,?,0 703 | hungary,45,1,3,135,?,0,0,110,0,0,?,?,?,0 704 | hungary,45,1,4,120,225,0,0,140,0,0,?,?,?,0 705 | hungary,45,1,4,140,224,0,0,144,0,0,?,?,?,0 706 | hungary,46,0,4,130,238,0,0,90,0,0,?,?,?,0 707 | hungary,46,1,2,140,275,0,0,165,1,0,?,?,?,0 708 | hungary,46,1,3,120,230,0,0,150,0,0,?,?,?,0 709 | hungary,46,1,3,150,163,?,0,116,0,0,?,?,?,0 710 | hungary,46,1,4,110,238,0,1,140,1,1,2,?,3,0 711 | hungary,46,1,4,110,240,0,1,140,0,0,?,?,3,0 712 | hungary,46,1,4,180,280,0,1,120,0,0,?,?,?,0 713 | hungary,47,0,2,140,257,0,0,135,0,1,1,?,?,0 714 | hungary,47,0,3,130,?,0,0,145,0,2,2,?,?,0 715 | hungary,47,1,1,110,249,0,0,150,0,0,?,?,?,0 716 | hungary,47,1,2,160,263,0,0,174,0,0,?,?,?,0 717 | hungary,47,1,4,140,276,1,0,125,1,0,?,?,?,0 718 | hungary,48,0,2,?,308,0,1,?,?,2,1,?,?,0 719 | hungary,48,0,2,120,?,1,1,148,0,0,?,?,?,0 720 | hungary,48,0,2,120,284,0,0,120,0,0,?,?,?,0 721 | hungary,48,0,3,120,195,0,0,125,0,0,?,?,?,0 722 | hungary,48,0,4,108,163,0,0,175,0,2,1,?,?,0 723 | hungary,48,0,4,120,254,0,1,110,0,0,?,?,?,0 724 | hungary,48,0,4,150,227,0,0,130,1,1,2,?,?,0 725 | hungary,48,1,2,100,?,0,0,100,0,0,?,?,?,0 726 | hungary,48,1,2,130,245,0,0,160,0,0,?,?,?,0 727 | hungary,48,1,2,140,238,0,0,118,0,0,?,?,?,0 728 | hungary,48,1,3,110,211,0,0,138,0,0,?,?,6,0 729 | hungary,49,0,2,110,?,0,0,160,0,0,?,?,?,0 730 | hungary,49,0,2,110,?,0,0,160,0,0,?,?,?,0 731 | hungary,49,0,2,124,201,0,0,164,0,0,?,?,?,0 732 | hungary,49,0,3,130,207,0,1,135,0,0,?,?,?,0 733 | hungary,49,1,2,100,253,0,0,174,0,0,?,?,?,0 734 | hungary,49,1,3,140,187,0,0,172,0,0,?,?,?,0 735 | hungary,49,1,4,120,297,?,0,132,0,1,2,?,?,0 736 | hungary,49,1,4,140,?,0,0,130,0,0,?,?,?,0 737 | hungary,50,0,2,110,202,0,0,145,0,0,?,?,?,0 738 | hungary,50,0,4,120,328,0,0,110,1,1,2,?,?,0 739 | hungary,50,1,2,120,168,0,0,160,0,0,?,0,?,0 740 | hungary,50,1,2,140,216,0,0,170,0,0,?,?,3,0 741 | hungary,50,1,2,170,209,0,1,116,0,0,?,?,?,0 742 | hungary,50,1,4,140,129,0,0,135,0,0,?,?,?,0 743 | hungary,50,1,4,150,215,0,0,140,1,0,?,?,?,0 744 | hungary,51,0,2,160,194,0,0,170,0,0,?,?,?,0 745 | hungary,51,0,3,110,190,0,0,120,0,0,?,?,?,0 746 | hungary,51,0,3,130,220,0,0,160,1,2,1,?,?,0 747 | hungary,51,0,3,150,200,0,0,120,0,0.5,1,?,?,0 748 | hungary,51,1,2,125,188,0,0,145,0,0,?,?,?,0 749 | hungary,51,1,2,130,224,0,0,150,0,0,?,?,?,0 750 | hungary,51,1,4,130,179,0,0,100,0,0,?,?,7,0 751 | hungary,52,0,2,120,210,0,0,148,0,0,?,?,?,0 752 | hungary,52,0,2,140,?,0,0,140,0,0,?,?,?,0 753 | hungary,52,0,3,125,272,0,0,139,0,0,?,?,?,0 754 | hungary,52,0,4,130,180,0,0,140,1,1.5,2,?,?,0 755 | hungary,52,1,2,120,284,0,0,118,0,0,?,?,?,0 756 | hungary,52,1,2,140,100,0,0,138,1,0,?,?,?,0 757 | hungary,52,1,2,160,196,0,0,165,0,0,?,?,?,0 758 | hungary,52,1,3,140,259,0,1,170,0,0,?,?,?,0 759 | hungary,53,0,2,113,468,?,0,127,0,0,?,?,?,0 760 | hungary,53,0,2,140,216,0,0,142,1,2,2,?,?,0 761 | hungary,53,0,3,120,274,0,0,130,0,0,?,?,?,0 762 | hungary,53,1,2,120,?,0,0,132,0,0,?,?,?,0 763 | hungary,53,1,2,140,320,0,0,162,0,0,?,?,?,0 764 | hungary,53,1,3,120,195,0,0,140,0,0,?,?,?,0 765 | hungary,53,1,4,124,260,0,1,112,1,3,2,?,?,0 766 | hungary,53,1,4,130,182,0,0,148,0,0,?,?,?,0 767 | hungary,53,1,4,140,243,0,0,155,0,0,?,?,?,0 768 | hungary,54,0,2,120,221,0,0,138,0,1,1,?,?,0 769 | hungary,54,0,2,120,230,1,0,140,0,0,?,?,?,0 770 | hungary,54,0,2,120,273,0,0,150,0,1.5,2,?,?,0 771 | hungary,54,0,2,130,253,0,1,155,0,0,?,?,?,0 772 | hungary,54,0,2,140,309,?,1,140,0,0,?,?,?,0 773 | hungary,54,0,2,150,230,0,0,130,0,0,?,?,?,0 774 | hungary,54,0,2,160,312,0,0,130,0,0,?,?,?,0 775 | hungary,54,1,1,120,171,0,0,137,0,2,1,?,?,0 776 | hungary,54,1,2,110,208,0,0,142,0,0,?,?,?,0 777 | hungary,54,1,2,120,238,0,0,154,0,0,?,?,?,0 778 | hungary,54,1,2,120,246,0,0,110,0,0,?,?,?,0 779 | hungary,54,1,2,160,195,0,1,130,0,1,1,?,?,0 780 | hungary,54,1,2,160,305,0,0,175,0,0,?,?,?,0 781 | hungary,54,1,3,120,217,0,0,137,0,0,?,?,?,0 782 | hungary,54,1,3,150,?,0,0,122,0,0,?,?,?,0 783 | hungary,54,1,4,150,365,0,1,134,0,1,1,?,?,0 784 | hungary,55,0,2,110,344,0,1,160,0,0,?,?,?,0 785 | hungary,55,0,2,122,320,0,0,155,0,0,?,?,?,0 786 | hungary,55,0,2,130,394,0,2,150,0,0,?,?,?,0 787 | hungary,55,1,2,120,256,1,0,137,0,0,?,?,7,0 788 | hungary,55,1,2,140,196,0,0,150,0,0,?,?,7,0 789 | hungary,55,1,2,145,326,0,0,155,0,0,?,?,?,0 790 | hungary,55,1,3,110,277,0,0,160,0,0,?,?,?,0 791 | hungary,55,1,3,120,220,0,2,134,0,0,?,?,?,0 792 | hungary,55,1,4,120,270,0,0,140,0,0,?,?,?,0 793 | hungary,55,1,4,140,229,0,0,110,1,0.5,2,?,?,0 794 | hungary,56,0,3,130,219,?,1,164,0,0,?,?,7,0 795 | hungary,56,1,2,130,184,0,0,100,0,0,?,?,?,0 796 | hungary,56,1,3,130,?,0,0,114,0,0,?,?,?,0 797 | hungary,56,1,3,130,276,0,0,128,1,1,1,?,6,0 798 | hungary,56,1,4,120,85,0,0,140,0,0,?,?,?,0 799 | hungary,57,0,1,130,308,0,0,98,0,1,2,?,?,0 800 | hungary,57,0,4,180,347,0,1,126,1,0.8,2,?,?,0 801 | hungary,57,1,2,140,260,1,0,140,0,0,?,?,6,0 802 | hungary,58,1,2,130,230,0,0,150,0,0,?,?,?,0 803 | hungary,58,1,2,130,251,0,0,110,0,0,?,?,?,0 804 | hungary,58,1,3,140,179,0,0,160,0,0,?,?,?,0 805 | hungary,58,1,4,135,222,0,0,100,0,0,?,?,?,0 806 | hungary,59,0,2,130,188,0,0,124,0,1,2,?,?,0 807 | hungary,59,1,2,140,287,0,0,150,0,0,?,?,?,0 808 | hungary,59,1,3,130,318,0,0,120,1,1,2,?,3,0 809 | hungary,59,1,3,180,213,0,0,100,0,0,?,?,?,0 810 | hungary,59,1,4,140,?,0,0,140,0,0,?,0,?,0 811 | hungary,60,1,3,120,246,0,2,135,0,0,?,?,?,0 812 | hungary,61,0,4,130,294,0,1,120,1,1,2,?,?,0 813 | hungary,61,1,4,125,292,0,1,115,1,0,?,?,?,0 814 | hungary,62,0,1,160,193,0,0,116,0,0,?,?,?,0 815 | hungary,62,1,2,140,271,0,0,152,0,1,1,?,?,0 816 | hungary,31,1,4,120,270,0,0,153,1,1.5,2,?,?,1 817 | hungary,33,0,4,100,246,0,0,150,1,1,2,?,?,1 818 | hungary,34,1,1,140,156,0,0,180,0,0,?,?,?,1 819 | hungary,35,1,2,110,257,0,0,140,0,0,?,?,?,1 820 | hungary,36,1,2,120,267,0,0,160,0,3,2,?,?,1 821 | hungary,37,1,4,140,207,0,0,130,1,1.5,2,?,?,1 822 | hungary,38,1,4,110,196,0,0,166,0,0,?,?,?,1 823 | hungary,38,1,4,120,282,0,0,170,0,0,?,?,?,1 824 | hungary,38,1,4,92,117,0,0,134,1,2.5,2,?,?,1 825 | hungary,40,1,4,120,466,?,0,152,1,1,2,?,6,1 826 | hungary,41,1,4,110,289,0,0,170,0,0,?,?,6,1 827 | hungary,41,1,4,120,237,?,0,138,1,1,2,?,?,1 828 | hungary,43,1,4,150,247,0,0,130,1,2,2,?,?,1 829 | hungary,46,1,4,110,202,0,0,150,1,0,?,?,?,1 830 | hungary,46,1,4,118,186,0,0,124,0,0,?,?,7,1 831 | hungary,46,1,4,120,277,0,0,125,1,1,2,?,?,1 832 | hungary,47,1,3,140,193,0,0,145,1,1,2,?,?,1 833 | hungary,47,1,4,150,226,0,0,98,1,1.5,2,0,7,1 834 | hungary,48,1,4,106,263,1,0,110,0,0,?,?,?,1 835 | hungary,48,1,4,120,260,0,0,115,0,2,2,?,?,1 836 | hungary,48,1,4,160,268,0,0,103,1,1,2,?,?,1 837 | hungary,49,0,3,160,180,0,0,156,0,1,2,?,?,1 838 | hungary,49,1,3,115,265,0,0,175,0,0,?,?,?,1 839 | hungary,49,1,4,130,206,0,0,170,0,0,?,?,?,1 840 | hungary,50,0,3,140,288,0,0,140,1,0,?,?,7,1 841 | hungary,50,1,4,145,264,0,0,150,0,0,?,?,?,1 842 | hungary,51,0,4,160,303,0,0,150,1,1,2,?,?,1 843 | hungary,52,1,4,130,225,0,0,120,1,2,2,?,?,1 844 | hungary,54,1,4,125,216,0,0,140,0,0,?,?,?,1 845 | hungary,54,1,4,125,224,0,0,122,0,2,2,?,?,1 846 | hungary,55,1,4,140,201,0,0,130,1,3,2,?,?,1 847 | hungary,57,1,2,140,265,0,1,145,1,1,2,?,?,1 848 | hungary,58,1,3,130,213,0,1,140,0,0,?,?,6,1 849 | hungary,59,0,4,130,338,1,1,130,1,1.5,2,?,?,1 850 | hungary,60,1,4,100,248,0,0,125,0,1,2,?,?,1 851 | hungary,63,1,4,150,223,0,0,115,0,0,?,?,?,1 852 | hungary,65,1,4,140,306,1,0,87,1,1.5,2,?,?,1 853 | hungary,32,1,4,118,529,0,0,130,0,0,?,?,?,1 854 | hungary,38,1,4,110,?,0,0,150,1,1,2,?,?,1 855 | hungary,39,1,4,110,280,0,0,150,0,0,?,?,6,1 856 | hungary,40,0,4,150,392,0,0,130,0,2,2,?,6,1 857 | hungary,43,1,1,120,291,0,1,155,0,0,?,?,?,1 858 | hungary,45,1,4,130,219,0,1,130,1,1,2,?,?,1 859 | hungary,46,1,4,120,231,0,0,115,1,0,?,?,?,1 860 | hungary,46,1,4,130,222,0,0,112,0,0,?,?,?,1 861 | hungary,48,1,4,122,275,1,1,150,1,2,3,?,?,1 862 | hungary,48,1,4,160,193,0,0,102,1,3,2,?,?,1 863 | hungary,48,1,4,160,329,0,0,92,1,1.5,2,?,?,1 864 | hungary,48,1,4,160,355,0,0,99,1,2,2,?,?,1 865 | hungary,50,1,4,130,233,0,0,121,1,2,2,?,7,1 866 | hungary,52,1,4,120,182,0,0,150,0,0,?,?,?,1 867 | hungary,52,1,4,170,?,0,0,126,1,1.5,2,?,?,1 868 | hungary,53,1,4,120,246,0,0,116,1,0,?,?,?,1 869 | hungary,54,1,3,120,237,0,0,150,1,1.5,?,?,7,1 870 | hungary,54,1,4,130,242,0,0,91,1,1,2,?,?,1 871 | hungary,54,1,4,130,603,1,0,125,1,1,2,?,?,1 872 | hungary,54,1,4,140,?,0,0,118,1,0,?,?,?,1 873 | hungary,54,1,4,200,198,0,0,142,1,2,2,?,?,1 874 | hungary,55,1,4,140,268,0,0,128,1,1.5,2,?,?,1 875 | hungary,56,1,4,150,213,1,0,125,1,1,2,?,?,1 876 | hungary,57,1,4,150,255,0,0,92,1,3,2,?,?,1 877 | hungary,58,1,3,160,211,1,1,92,0,0,?,?,?,1 878 | hungary,58,1,4,130,263,0,0,140,1,2,2,?,?,1 879 | hungary,41,1,4,130,172,0,1,130,0,2,2,?,?,1 880 | hungary,43,1,4,120,175,0,0,120,1,1,2,?,7,1 881 | hungary,44,1,2,150,288,0,0,150,1,3,2,?,?,1 882 | hungary,44,1,4,130,290,0,0,100,1,2,2,?,?,1 883 | hungary,46,1,1,140,272,1,0,175,0,2,2,?,?,1 884 | hungary,47,0,3,135,248,1,0,170,0,0,?,?,?,1 885 | hungary,48,0,4,138,214,0,0,108,1,1.5,2,?,?,1 886 | hungary,49,1,4,130,341,0,0,120,1,1,2,?,?,1 887 | hungary,49,1,4,140,234,0,0,140,1,1,2,?,?,1 888 | hungary,51,1,3,135,160,0,0,150,0,2,2,?,?,1 889 | hungary,52,1,4,112,342,0,1,96,1,1,2,?,?,1 890 | hungary,52,1,4,130,298,0,0,110,1,1,2,?,?,1 891 | hungary,52,1,4,140,404,0,0,124,1,2,2,?,?,1 892 | hungary,52,1,4,160,246,0,1,82,1,4,2,?,?,1 893 | hungary,53,1,3,145,518,0,0,130,0,0,?,?,?,1 894 | hungary,53,1,4,180,285,0,1,120,1,1.5,2,?,?,1 895 | hungary,54,1,4,140,216,0,0,105,0,1.5,2,?,?,1 896 | hungary,55,1,1,140,295,0,?,136,0,0,?,?,?,1 897 | hungary,55,1,2,160,292,1,0,143,1,2,2,?,?,1 898 | hungary,55,1,4,145,248,0,0,96,1,2,2,?,?,1 899 | hungary,56,0,2,120,279,0,0,150,0,1,2,?,?,1 900 | hungary,56,1,4,150,230,0,1,124,1,1.5,2,?,?,1 901 | hungary,56,1,4,170,388,0,1,122,1,2,2,?,?,1 902 | hungary,58,1,2,136,164,0,1,99,1,2,2,?,?,1 903 | hungary,59,1,4,130,?,0,0,125,0,0,?,?,?,1 904 | hungary,59,1,4,140,264,1,2,119,1,0,?,?,?,1 905 | hungary,65,1,4,170,263,1,0,112,1,2,2,?,?,1 906 | hungary,66,1,4,140,?,0,0,94,1,1,2,?,?,1 907 | hungary,41,1,4,120,336,0,0,118,1,3,2,?,?,1 908 | hungary,43,1,4,140,288,0,0,135,1,2,2,?,?,1 909 | hungary,44,1,4,135,491,0,0,135,0,0,?,?,?,1 910 | hungary,47,0,4,120,205,0,0,98,1,2,2,?,6,1 911 | hungary,47,1,4,160,291,0,1,158,1,3,2,?,?,1 912 | hungary,49,1,4,128,212,0,0,96,1,0,?,?,?,1 913 | hungary,49,1,4,150,222,0,0,122,0,2,2,?,?,1 914 | hungary,50,1,4,140,231,0,1,140,1,5,2,?,?,1 915 | hungary,50,1,4,140,341,0,1,125,1,2.5,2,?,?,1 916 | hungary,52,1,4,140,266,0,0,134,1,2,2,?,?,1 917 | hungary,52,1,4,160,331,0,0,94,1,2.5,?,?,?,1 918 | hungary,54,0,3,130,294,0,1,100,1,0,2,?,?,1 919 | hungary,56,1,4,155,342,1,0,150,1,3,2,?,?,1 920 | hungary,58,0,2,180,393,0,0,110,1,1,2,?,7,1 921 | hungary,65,1,4,130,275,0,1,115,1,1,2,?,?,1 -------------------------------------------------------------------------------- /data/heart_disease_sql: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jakemdrew/DataMiningNotebooks/5f1fa5d53dbe558224889e87e84fd784d7fba8b6/data/heart_disease_sql -------------------------------------------------------------------------------- /data/python_ranking.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jakemdrew/DataMiningNotebooks/5f1fa5d53dbe558224889e87e84fd784d7fba8b6/data/python_ranking.png -------------------------------------------------------------------------------- /statcompare.py: -------------------------------------------------------------------------------- 1 | clf1 = Pipeline( 2 | [('PCA',PCA(n_components=100,svd_solver='randomized')), 3 | ('CLF',GaussianNB())] 4 | ) 5 | clf2 = Pipeline( 6 | [('PCA',PCA(n_components=500,svd_solver='randomized')), 7 | ('CLF',GaussianNB())] 8 | ) 9 | 10 | 11 | from sklearn.model_selection import cross_val_score 12 | # is clf1 better or worse than clf2? 13 | cv=StratifiedKFold(n_splits=10) 14 | acc1 = cross_val_score(clf1, X, y=y, cv=cv) 15 | acc2 = cross_val_score(clf2, X, y=y, cv=cv) 16 | 17 | #================================= 18 | 19 | t = 2.26 / np.sqrt(10) 20 | 21 | e = (1-acc1)-(1-acc2) 22 | # std1 = np.std(acc1) 23 | # std2 = np.std(acc2) 24 | stdtot = np.std(e) 25 | 26 | dbar = np.mean(e) 27 | print ('Range of:', dbar-t*stdtot,dbar+t*stdtot ) 28 | print (np.mean(acc1), np.mean(acc2)) 29 | 30 | 31 | #=============================== 32 | cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] 33 | plt.imshow(cm_normalized,cmap=plt.get_cmap('Reds'),aspect='auto') 34 | plt.grid(False) --------------------------------------------------------------------------------