├── .gitignore ├── 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 ├── 09. Clustering and Discretization.ipynb ├── 10. Clustering Validity.ipynb ├── 11. More Clustering.ipynb ├── 12. Association Analysis.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 ├── 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 └── statcompare.py /.gitignore: -------------------------------------------------------------------------------- 1 | .DS* 2 | .ipynb_checkpoints 3 | my_model 4 | ICA_assignments 5 | 6 | -------------------------------------------------------------------------------- /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 | "collapsed": false 25 | }, 26 | "outputs": [ 27 | { 28 | "data": { 29 | "text/plain": [ 30 | "array([[ 0.29819302, 0.12958758, 0.84844853],\n", 31 | " [ 0.13821716, 0.54442716, 0.00381985],\n", 32 | " [ 0.97379588, 0.92078769, 0.41873324],\n", 33 | " [ 0.32318138, 0.59510898, 0.68649932],\n", 34 | " [ 0.53962946, 0.07424397, 0.56199502]])" 35 | ] 36 | }, 37 | "execution_count": 1, 38 | "metadata": {}, 39 | "output_type": "execute_result" 40 | } 41 | ], 42 | "source": [ 43 | "import numpy as np\n", 44 | "\n", 45 | "x = np.random.rand(5,3)\n", 46 | "x" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 5, 52 | "metadata": { 53 | "collapsed": false 54 | }, 55 | "outputs": [ 56 | { 57 | "data": { 58 | "text/plain": [ 59 | "(5, 3)" 60 | ] 61 | }, 62 | "execution_count": 5, 63 | "metadata": {}, 64 | "output_type": "execute_result" 65 | } 66 | ], 67 | "source": [ 68 | "x.shape" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 6, 74 | "metadata": { 75 | "collapsed": false 76 | }, 77 | "outputs": [ 78 | { 79 | "data": { 80 | "text/plain": [ 81 | "dtype('float64')" 82 | ] 83 | }, 84 | "execution_count": 6, 85 | "metadata": {}, 86 | "output_type": "execute_result" 87 | } 88 | ], 89 | "source": [ 90 | "x.dtype" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 7, 96 | "metadata": { 97 | "collapsed": false 98 | }, 99 | "outputs": [ 100 | { 101 | "ename": "ValueError", 102 | "evalue": "operands could not be broadcast together with shapes (5,3) (3,4) ", 103 | "output_type": "error", 104 | "traceback": [ 105 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 106 | "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", 107 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 108 | "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (5,3) (3,4) " 109 | ] 110 | } 111 | ], 112 | "source": [ 113 | "y = np.random.rand(3,4)\n", 114 | "z = x*y\n", 115 | "z" 116 | ] 117 | }, 118 | { 119 | "cell_type": "code", 120 | "execution_count": 8, 121 | "metadata": { 122 | "collapsed": false 123 | }, 124 | "outputs": [ 125 | { 126 | "data": { 127 | "text/plain": [ 128 | "array([[ 0.44298211, 1.18307784, 0.27503586, 0.77407891],\n", 129 | " [ 0.63741349, 0.60984212, 0.06343294, 0.49148952],\n", 130 | " [ 1.64095276, 2.01802131, 0.48567654, 1.52936334],\n", 131 | " [ 0.90263812, 1.46950084, 0.26149864, 1.04054875],\n", 132 | " [ 0.51774592, 1.04279372, 0.32313227, 0.71606319]])" 133 | ] 134 | }, 135 | "execution_count": 8, 136 | "metadata": {}, 137 | "output_type": "execute_result" 138 | } 139 | ], 140 | "source": [ 141 | "z = x @ y\n", 142 | "\n", 143 | "z" 144 | ] 145 | }, 146 | { 147 | "cell_type": "code", 148 | "execution_count": null, 149 | "metadata": { 150 | "collapsed": false 151 | }, 152 | "outputs": [], 153 | "source": [ 154 | "x = np.mat(x)\n", 155 | "y = np.mat(y)\n", 156 | "z = x*y\n", 157 | "z" 158 | ] 159 | }, 160 | { 161 | "cell_type": "markdown", 162 | "metadata": {}, 163 | "source": [ 164 | "# Indexing" 165 | ] 166 | }, 167 | { 168 | "cell_type": "code", 169 | "execution_count": 9, 170 | "metadata": { 171 | "collapsed": false 172 | }, 173 | "outputs": [ 174 | { 175 | "data": { 176 | "text/plain": [ 177 | "array([[1, 2, 3],\n", 178 | " [4, 5, 6],\n", 179 | " [7, 8, 9]])" 180 | ] 181 | }, 182 | "execution_count": 9, 183 | "metadata": {}, 184 | "output_type": "execute_result" 185 | } 186 | ], 187 | "source": [ 188 | "x1 = np.array([[1,2,3],\n", 189 | " [4,5,6],\n", 190 | " [7,8,9]])\n", 191 | "x1" 192 | ] 193 | }, 194 | { 195 | "cell_type": "code", 196 | "execution_count": 10, 197 | "metadata": { 198 | "collapsed": false 199 | }, 200 | "outputs": [ 201 | { 202 | "name": "stdout", 203 | "output_type": "stream", 204 | "text": [ 205 | "2\n", 206 | "5\n", 207 | "8\n" 208 | ] 209 | } 210 | ], 211 | "source": [ 212 | "for row in range(x1.shape[0]):\n", 213 | " print (x1[row,1])" 214 | ] 215 | }, 216 | { 217 | "cell_type": "code", 218 | "execution_count": 14, 219 | "metadata": { 220 | "collapsed": false 221 | }, 222 | "outputs": [ 223 | { 224 | "data": { 225 | "text/plain": [ 226 | "array([2, 5, 8])" 227 | ] 228 | }, 229 | "execution_count": 14, 230 | "metadata": {}, 231 | "output_type": "execute_result" 232 | } 233 | ], 234 | "source": [ 235 | "x1[:,1]" 236 | ] 237 | }, 238 | { 239 | "cell_type": "code", 240 | "execution_count": 15, 241 | "metadata": { 242 | "collapsed": false 243 | }, 244 | "outputs": [ 245 | { 246 | "data": { 247 | "text/plain": [ 248 | "array([False, True, True], dtype=bool)" 249 | ] 250 | }, 251 | "execution_count": 15, 252 | "metadata": {}, 253 | "output_type": "execute_result" 254 | } 255 | ], 256 | "source": [ 257 | "x1[:,1]>3" 258 | ] 259 | }, 260 | { 261 | "cell_type": "code", 262 | "execution_count": 16, 263 | "metadata": { 264 | "collapsed": false 265 | }, 266 | "outputs": [ 267 | { 268 | "data": { 269 | "text/plain": [ 270 | "array([[4, 5, 6],\n", 271 | " [7, 8, 9]])" 272 | ] 273 | }, 274 | "execution_count": 16, 275 | "metadata": {}, 276 | "output_type": "execute_result" 277 | } 278 | ], 279 | "source": [ 280 | "x1[ x1[:,1]>3 ]" 281 | ] 282 | }, 283 | { 284 | "cell_type": "code", 285 | "execution_count": null, 286 | "metadata": { 287 | "collapsed": false 288 | }, 289 | "outputs": [], 290 | "source": [ 291 | "x2 = np.array(range(10))\n", 292 | "x2" 293 | ] 294 | }, 295 | { 296 | "cell_type": "code", 297 | "execution_count": null, 298 | "metadata": { 299 | "collapsed": false 300 | }, 301 | "outputs": [], 302 | "source": [ 303 | "x2.shape" 304 | ] 305 | }, 306 | { 307 | "cell_type": "code", 308 | "execution_count": null, 309 | "metadata": { 310 | "collapsed": false 311 | }, 312 | "outputs": [], 313 | "source": [ 314 | "idx = x2>5\n", 315 | "idx" 316 | ] 317 | }, 318 | { 319 | "cell_type": "code", 320 | "execution_count": null, 321 | "metadata": { 322 | "collapsed": false 323 | }, 324 | "outputs": [], 325 | "source": [ 326 | "x2[idx]" 327 | ] 328 | }, 329 | { 330 | "cell_type": "code", 331 | "execution_count": null, 332 | "metadata": { 333 | "collapsed": false 334 | }, 335 | "outputs": [], 336 | "source": [ 337 | "x2[x2>5]" 338 | ] 339 | }, 340 | { 341 | "cell_type": "markdown", 342 | "metadata": {}, 343 | "source": [ 344 | "# Named columns\n", 345 | "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?" 346 | ] 347 | }, 348 | { 349 | "cell_type": "code", 350 | "execution_count": 17, 351 | "metadata": { 352 | "collapsed": false 353 | }, 354 | "outputs": [ 355 | { 356 | "data": { 357 | "text/plain": [ 358 | "array([[ 64, 2100, 1],\n", 359 | " [ 50, 2200, 1],\n", 360 | " [ 48, 2300, 1],\n", 361 | " [ 34, 0, 2],\n", 362 | " [ 30, 100, 2]])" 363 | ] 364 | }, 365 | "execution_count": 17, 366 | "metadata": {}, 367 | "output_type": "execute_result" 368 | } 369 | ], 370 | "source": [ 371 | "col_names = ['temperature','time','day']\n", 372 | "data = np.array([[64,2100,1],\n", 373 | " [50,2200,1],\n", 374 | " [48,2300,1],\n", 375 | " [34,0, 2],\n", 376 | " [30,100, 2]])\n", 377 | "data" 378 | ] 379 | }, 380 | { 381 | "cell_type": "code", 382 | "execution_count": 18, 383 | "metadata": { 384 | "collapsed": false 385 | }, 386 | "outputs": [ 387 | { 388 | "data": { 389 | "text/plain": [ 390 | "array([[ 64, 2100, 1],\n", 391 | " [ 50, 2200, 1],\n", 392 | " [ 48, 2300, 1]])" 393 | ] 394 | }, 395 | "execution_count": 18, 396 | "metadata": {}, 397 | "output_type": "execute_result" 398 | } 399 | ], 400 | "source": [ 401 | "data2 = data[data[:,1]>1500]\n", 402 | "data2" 403 | ] 404 | }, 405 | { 406 | "cell_type": "code", 407 | "execution_count": 19, 408 | "metadata": { 409 | "collapsed": false 410 | }, 411 | "outputs": [ 412 | { 413 | "data": { 414 | "text/html": [ 415 | "
\n", 416 | "\n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | "
temperaturetimeday
06421001
15022001
24823001
33402
4301002
\n", 458 | "
" 459 | ], 460 | "text/plain": [ 461 | " temperature time day\n", 462 | "0 64 2100 1\n", 463 | "1 50 2200 1\n", 464 | "2 48 2300 1\n", 465 | "3 34 0 2\n", 466 | "4 30 100 2" 467 | ] 468 | }, 469 | "execution_count": 19, 470 | "metadata": {}, 471 | "output_type": "execute_result" 472 | } 473 | ], 474 | "source": [ 475 | "# pandas to the rescue\n", 476 | "import pandas as pd\n", 477 | "\n", 478 | "df = pd.DataFrame(data,columns=col_names)\n", 479 | "df" 480 | ] 481 | }, 482 | { 483 | "cell_type": "code", 484 | "execution_count": 20, 485 | "metadata": { 486 | "collapsed": false 487 | }, 488 | "outputs": [ 489 | { 490 | "data": { 491 | "text/html": [ 492 | "
\n", 493 | "\n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | " \n", 502 | " \n", 503 | " \n", 504 | " \n", 505 | " \n", 506 | " \n", 507 | " \n", 508 | " \n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | " \n", 513 | " \n", 514 | " \n", 515 | " \n", 516 | " \n", 517 | " \n", 518 | " \n", 519 | " \n", 520 | " \n", 521 | " \n", 522 | "
temperaturetimeday
06421001
15022001
24823001
\n", 523 | "
" 524 | ], 525 | "text/plain": [ 526 | " temperature time day\n", 527 | "0 64 2100 1\n", 528 | "1 50 2200 1\n", 529 | "2 48 2300 1" 530 | ] 531 | }, 532 | "execution_count": 20, 533 | "metadata": {}, 534 | "output_type": "execute_result" 535 | } 536 | ], 537 | "source": [ 538 | "df[df.time>1500]" 539 | ] 540 | }, 541 | { 542 | "cell_type": "code", 543 | "execution_count": 21, 544 | "metadata": { 545 | "collapsed": false 546 | }, 547 | "outputs": [ 548 | { 549 | "name": "stdout", 550 | "output_type": "stream", 551 | "text": [ 552 | "\n", 553 | "RangeIndex: 5 entries, 0 to 4\n", 554 | "Data columns (total 3 columns):\n", 555 | "temperature 5 non-null int64\n", 556 | "time 5 non-null int64\n", 557 | "day 5 non-null int64\n", 558 | "dtypes: int64(3)\n", 559 | "memory usage: 200.0 bytes\n" 560 | ] 561 | } 562 | ], 563 | "source": [ 564 | "df.info()" 565 | ] 566 | }, 567 | { 568 | "cell_type": "code", 569 | "execution_count": 22, 570 | "metadata": { 571 | "collapsed": true 572 | }, 573 | "outputs": [], 574 | "source": [ 575 | "df.day[df.day==1] = 'Mon'" 576 | ] 577 | }, 578 | { 579 | "cell_type": "code", 580 | "execution_count": 23, 581 | "metadata": { 582 | "collapsed": false 583 | }, 584 | "outputs": [ 585 | { 586 | "data": { 587 | "text/html": [ 588 | "
\n", 589 | "\n", 590 | " \n", 591 | " \n", 592 | " \n", 593 | " \n", 594 | " \n", 595 | " \n", 596 | " \n", 597 | " \n", 598 | " \n", 599 | " \n", 600 | " \n", 601 | " \n", 602 | " \n", 603 | " \n", 604 | " \n", 605 | " \n", 606 | " \n", 607 | " \n", 608 | " \n", 609 | " \n", 610 | " \n", 611 | " \n", 612 | " \n", 613 | " \n", 614 | " \n", 615 | " \n", 616 | " \n", 617 | " \n", 618 | " \n", 619 | " \n", 620 | " \n", 621 | " \n", 622 | " \n", 623 | " \n", 624 | " \n", 625 | " \n", 626 | " \n", 627 | " \n", 628 | " \n", 629 | " \n", 630 | "
temperaturetimeday
0642100Mon
1502200Mon
2482300Mon
33402
4301002
\n", 631 | "
" 632 | ], 633 | "text/plain": [ 634 | " temperature time day\n", 635 | "0 64 2100 Mon\n", 636 | "1 50 2200 Mon\n", 637 | "2 48 2300 Mon\n", 638 | "3 34 0 2\n", 639 | "4 30 100 2" 640 | ] 641 | }, 642 | "execution_count": 23, 643 | "metadata": {}, 644 | "output_type": "execute_result" 645 | } 646 | ], 647 | "source": [ 648 | "df" 649 | ] 650 | }, 651 | { 652 | "cell_type": "code", 653 | "execution_count": 24, 654 | "metadata": { 655 | "collapsed": false 656 | }, 657 | "outputs": [ 658 | { 659 | "data": { 660 | "text/html": [ 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 | " \n", 695 | " \n", 696 | " \n", 697 | " \n", 698 | " \n", 699 | " \n", 700 | " \n", 701 | " \n", 702 | " \n", 703 | "
temperaturetimeday
0642100Mon
1502200Mon
2482300Mon
3340Tues
430100Tues
\n", 704 | "
" 705 | ], 706 | "text/plain": [ 707 | " temperature time day\n", 708 | "0 64 2100 Mon\n", 709 | "1 50 2200 Mon\n", 710 | "2 48 2300 Mon\n", 711 | "3 34 0 Tues\n", 712 | "4 30 100 Tues" 713 | ] 714 | }, 715 | "execution_count": 24, 716 | "metadata": {}, 717 | "output_type": "execute_result" 718 | } 719 | ], 720 | "source": [ 721 | "df.day.replace(to_replace=range(7),\n", 722 | " value=['Su','Mon','Tues','Wed','Th','Fri','Sat'],\n", 723 | " inplace=True)\n", 724 | "df" 725 | ] 726 | }, 727 | { 728 | "cell_type": "code", 729 | "execution_count": 25, 730 | "metadata": { 731 | "collapsed": false 732 | }, 733 | "outputs": [ 734 | { 735 | "name": "stdout", 736 | "output_type": "stream", 737 | "text": [ 738 | "\n", 739 | "RangeIndex: 5 entries, 0 to 4\n", 740 | "Data columns (total 3 columns):\n", 741 | "temperature 5 non-null int64\n", 742 | "time 5 non-null int64\n", 743 | "day 5 non-null object\n", 744 | "dtypes: int64(2), object(1)\n", 745 | "memory usage: 200.0+ bytes\n" 746 | ] 747 | } 748 | ], 749 | "source": [ 750 | "df.info()" 751 | ] 752 | }, 753 | { 754 | "cell_type": "code", 755 | "execution_count": null, 756 | "metadata": { 757 | "collapsed": true 758 | }, 759 | "outputs": [], 760 | "source": [] 761 | } 762 | ], 763 | "metadata": { 764 | "anaconda-cloud": {}, 765 | "kernelspec": { 766 | "display_name": "Python [MLEnv]", 767 | "language": "python", 768 | "name": "Python [MLEnv]" 769 | }, 770 | "language_info": { 771 | "codemirror_mode": { 772 | "name": "ipython", 773 | "version": 3 774 | }, 775 | "file_extension": ".py", 776 | "mimetype": "text/x-python", 777 | "name": "python", 778 | "nbconvert_exporter": "python", 779 | "pygments_lexer": "ipython3", 780 | "version": "3.5.2" 781 | } 782 | }, 783 | "nbformat": 4, 784 | "nbformat_minor": 0 785 | } 786 | -------------------------------------------------------------------------------- /ICA1_DataMining-PartA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [ 10 | { 11 | "data": { 12 | "text/html": [ 13 | "" 14 | ], 15 | "text/plain": [ 16 | "" 17 | ] 18 | }, 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "output_type": "execute_result" 22 | } 23 | ], 24 | "source": [ 25 | "# Ebnable HTML/CSS \n", 26 | "from IPython.core.display import HTML\n", 27 | "HTML(\"\")" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "___\n", 35 | "Enter Team Member Names here (double click to edit):\n", 36 | "\n", 37 | "- Name 1:\n", 38 | "- Name 2:\n", 39 | "- Name 3:\n" 40 | ] 41 | }, 42 | { 43 | "cell_type": "markdown", 44 | "metadata": {}, 45 | "source": [ 46 | "# In Class Assignment One\n", 47 | "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", 48 | "\n", 49 | "## Contents\n", 50 | "* Loading the Data\n", 51 | "* Linear Regression\n", 52 | "\n", 53 | "**These portions are not yet accessible until the start of class:**\n", 54 | "* Using Scikit Learn for Regression\n", 55 | "* Linear Classification\n", 56 | "\n", 57 | "________________________________________________________________________________________________________\n", 58 | "\n", 59 | "\n", 60 | "## Loading the Data\n", 61 | "Please run the following code to read in the \"diabetes\" dataset from sklearn's data loading module. \n", 62 | "\n", 63 | "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. " 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": 17, 69 | "metadata": { 70 | "collapsed": false 71 | }, 72 | "outputs": [ 73 | { 74 | "name": "stdout", 75 | "output_type": "stream", 76 | "text": [ 77 | "features shape: (442, 10) format is: ('rows', 'columns')\n", 78 | "range of target: 25.0 346.0\n" 79 | ] 80 | } 81 | ], 82 | "source": [ 83 | "from sklearn.datasets import load_diabetes\n", 84 | "import numpy as np\n", 85 | "from __future__ import print_function\n", 86 | "\n", 87 | "\n", 88 | "ds = load_diabetes()\n", 89 | "\n", 90 | "# this holds the continuous feature data\n", 91 | "# because ds.data is a matrix, there are some special properties we can access (like 'shape')\n", 92 | "print('features shape:', ds.data.shape, 'format is:', ('rows','columns')) # there are 442 instances and 10 features per instance\n", 93 | "print('range of target:', np.min(ds.target),np.max(ds.target))" 94 | ] 95 | }, 96 | { 97 | "cell_type": "code", 98 | "execution_count": 11, 99 | "metadata": { 100 | "collapsed": false 101 | }, 102 | "outputs": [ 103 | { 104 | "name": "stdout", 105 | "output_type": "stream", 106 | "text": [ 107 | "array([[ 0.03807591, 0.05068012, 0.06169621, ..., -0.00259226,\n", 108 | " 0.01990842, -0.01764613],\n", 109 | " [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n", 110 | " -0.06832974, -0.09220405],\n", 111 | " [ 0.08529891, 0.05068012, 0.04445121, ..., -0.00259226,\n", 112 | " 0.00286377, -0.02593034],\n", 113 | " ..., \n", 114 | " [ 0.04170844, 0.05068012, -0.01590626, ..., -0.01107952,\n", 115 | " -0.04687948, 0.01549073],\n", 116 | " [-0.04547248, -0.04464164, 0.03906215, ..., 0.02655962,\n", 117 | " 0.04452837, -0.02593034],\n", 118 | " [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n", 119 | " -0.00421986, 0.00306441]])\n", 120 | "array([ 151., 75., 141., 206., 135., 97., 138., 63., 110.,\n", 121 | " 310., 101., 69., 179., 185., 118., 171., 166., 144.,\n", 122 | " 97., 168., 68., 49., 68., 245., 184., 202., 137.,\n", 123 | " 85., 131., 283., 129., 59., 341., 87., 65., 102.,\n", 124 | " 265., 276., 252., 90., 100., 55., 61., 92., 259.,\n", 125 | " 53., 190., 142., 75., 142., 155., 225., 59., 104.,\n", 126 | " 182., 128., 52., 37., 170., 170., 61., 144., 52.,\n", 127 | " 128., 71., 163., 150., 97., 160., 178., 48., 270.,\n", 128 | " 202., 111., 85., 42., 170., 200., 252., 113., 143.,\n", 129 | " 51., 52., 210., 65., 141., 55., 134., 42., 111.,\n", 130 | " 98., 164., 48., 96., 90., 162., 150., 279., 92.,\n", 131 | " 83., 128., 102., 302., 198., 95., 53., 134., 144.,\n", 132 | " 232., 81., 104., 59., 246., 297., 258., 229., 275.,\n", 133 | " 281., 179., 200., 200., 173., 180., 84., 121., 161.,\n", 134 | " 99., 109., 115., 268., 274., 158., 107., 83., 103.,\n", 135 | " 272., 85., 280., 336., 281., 118., 317., 235., 60.,\n", 136 | " 174., 259., 178., 128., 96., 126., 288., 88., 292.,\n", 137 | " 71., 197., 186., 25., 84., 96., 195., 53., 217.,\n", 138 | " 172., 131., 214., 59., 70., 220., 268., 152., 47.,\n", 139 | " 74., 295., 101., 151., 127., 237., 225., 81., 151.,\n", 140 | " 107., 64., 138., 185., 265., 101., 137., 143., 141.,\n", 141 | " 79., 292., 178., 91., 116., 86., 122., 72., 129.,\n", 142 | " 142., 90., 158., 39., 196., 222., 277., 99., 196.,\n", 143 | " 202., 155., 77., 191., 70., 73., 49., 65., 263.,\n", 144 | " 248., 296., 214., 185., 78., 93., 252., 150., 77.,\n", 145 | " 208., 77., 108., 160., 53., 220., 154., 259., 90.,\n", 146 | " 246., 124., 67., 72., 257., 262., 275., 177., 71.,\n", 147 | " 47., 187., 125., 78., 51., 258., 215., 303., 243.,\n", 148 | " 91., 150., 310., 153., 346., 63., 89., 50., 39.,\n", 149 | " 103., 308., 116., 145., 74., 45., 115., 264., 87.,\n", 150 | " 202., 127., 182., 241., 66., 94., 283., 64., 102.,\n", 151 | " 200., 265., 94., 230., 181., 156., 233., 60., 219.,\n", 152 | " 80., 68., 332., 248., 84., 200., 55., 85., 89.,\n", 153 | " 31., 129., 83., 275., 65., 198., 236., 253., 124.,\n", 154 | " 44., 172., 114., 142., 109., 180., 144., 163., 147.,\n", 155 | " 97., 220., 190., 109., 191., 122., 230., 242., 248.,\n", 156 | " 249., 192., 131., 237., 78., 135., 244., 199., 270.,\n", 157 | " 164., 72., 96., 306., 91., 214., 95., 216., 263.,\n", 158 | " 178., 113., 200., 139., 139., 88., 148., 88., 243.,\n", 159 | " 71., 77., 109., 272., 60., 54., 221., 90., 311.,\n", 160 | " 281., 182., 321., 58., 262., 206., 233., 242., 123.,\n", 161 | " 167., 63., 197., 71., 168., 140., 217., 121., 235.,\n", 162 | " 245., 40., 52., 104., 132., 88., 69., 219., 72.,\n", 163 | " 201., 110., 51., 277., 63., 118., 69., 273., 258.,\n", 164 | " 43., 198., 242., 232., 175., 93., 168., 275., 293.,\n", 165 | " 281., 72., 140., 189., 181., 209., 136., 261., 113.,\n", 166 | " 131., 174., 257., 55., 84., 42., 146., 212., 233.,\n", 167 | " 91., 111., 152., 120., 67., 310., 94., 183., 66.,\n", 168 | " 173., 72., 49., 64., 48., 178., 104., 132., 220., 57.])\n" 169 | ] 170 | } 171 | ], 172 | "source": [ 173 | "from pprint import pprint\n", 174 | "\n", 175 | "# we can set the fields inside of ds and set them to new variables in python\n", 176 | "pprint(ds.data) # prints out elements of the matrix\n", 177 | "pprint(ds.target) # prints the vector (all 442 items)" 178 | ] 179 | }, 180 | { 181 | "cell_type": "markdown", 182 | "metadata": {}, 183 | "source": [ 184 | "________________________________________________________________________________________________________\n", 185 | "\n", 186 | "## Using Linear Regression \n", 187 | "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", 188 | "\n", 189 | "$$ w = (X^TX)^{-1}X^Ty $$\n", 190 | "\n", 191 | "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", 192 | "\n", 193 | "$$ X=\\begin{bmatrix}\n", 194 | " & \\vdots & & 1 \\\\\n", 195 | " \\dotsb & \\text{ds.data} & \\dotsb & \\vdots\\\\\n", 196 | " & \\vdots & & 1\\\\\n", 197 | " \\end{bmatrix}\n", 198 | "$$\n", 199 | "\n", 200 | "**Question 1:** For the diabetes dataset, how many elements will the vector $w$ contain?" 201 | ] 202 | }, 203 | { 204 | "cell_type": "code", 205 | "execution_count": 7, 206 | "metadata": { 207 | "collapsed": false 208 | }, 209 | "outputs": [], 210 | "source": [ 211 | "# Enter your answer here (or write code to calculate it)\n", 212 | " \n", 213 | "\n", 214 | "#" 215 | ] 216 | }, 217 | { 218 | "cell_type": "markdown", 219 | "metadata": {}, 220 | "source": [ 221 | "________________________________________________________________________________________________________\n", 222 | "\n", 223 | "**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", 224 | "\n", 225 | "- `np.hstack((mat1,mat2))` stack two matrices horizontally, to create a new matrix\n", 226 | "- `np.ones((rows,cols))` create a matrix full of ones\n", 227 | "- `my_mat.T` takes transpose of numpy matrix named `my_mat`\n", 228 | "- `np.dot(mat1,mat2)` or `mat1 @ mat2` is matrix multiplication for two matrices\n", 229 | "- `np.linalg.inv(mat)` gets the inverse of the variable `mat`" 230 | ] 231 | }, 232 | { 233 | "cell_type": "code", 234 | "execution_count": 21, 235 | "metadata": { 236 | "collapsed": false 237 | }, 238 | "outputs": [ 239 | { 240 | "name": "stdout", 241 | "output_type": "stream", 242 | "text": [ 243 | "[[ -10.01219782]\n", 244 | " [-239.81908937]\n", 245 | " [ 519.83978679]\n", 246 | " [ 324.39042769]\n", 247 | " [-792.18416163]\n", 248 | " [ 476.74583782]\n", 249 | " [ 101.04457032]\n", 250 | " [ 177.06417623]\n", 251 | " [ 751.27932109]\n", 252 | " [ 67.62538639]\n", 253 | " [ 152.13348416]]\n" 254 | ] 255 | } 256 | ], 257 | "source": [ 258 | "# Write you code here, print the values of the regression weights using the 'print()' function in python\n" 259 | ] 260 | }, 261 | { 262 | "cell_type": "code", 263 | "execution_count": null, 264 | "metadata": { 265 | "collapsed": false 266 | }, 267 | "outputs": [], 268 | "source": [] 269 | } 270 | ], 271 | "metadata": { 272 | "anaconda-cloud": {}, 273 | "kernelspec": { 274 | "display_name": "Python [conda env:MLEnv]", 275 | "language": "python", 276 | "name": "conda-env-MLEnv-py" 277 | }, 278 | "language_info": { 279 | "codemirror_mode": { 280 | "name": "ipython", 281 | "version": 3 282 | }, 283 | "file_extension": ".py", 284 | "mimetype": "text/x-python", 285 | "name": "python", 286 | "nbconvert_exporter": "python", 287 | "pygments_lexer": "ipython3", 288 | "version": "3.5.2" 289 | } 290 | }, 291 | "nbformat": 4, 292 | "nbformat_minor": 0 293 | } 294 | -------------------------------------------------------------------------------- /ICA2_DataMining-PartA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [ 10 | { 11 | "data": { 12 | "text/html": [ 13 | "" 14 | ], 15 | "text/plain": [ 16 | "" 17 | ] 18 | }, 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "output_type": "execute_result" 22 | } 23 | ], 24 | "source": [ 25 | "# Ebnable HTML/CSS \n", 26 | "from IPython.core.display import HTML\n", 27 | "HTML(\"\")" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "\n", 35 | "___\n", 36 | "Enter Team Member Names here (double click to edit):\n", 37 | "\n", 38 | "- Name 1:\n", 39 | "- Name 2:\n", 40 | "- Name 3:\n", 41 | "\n" 42 | ] 43 | }, 44 | { 45 | "cell_type": "markdown", 46 | "metadata": {}, 47 | "source": [ 48 | "________\n", 49 | "\n", 50 | "# Live Session Assignment Two\n", 51 | "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", 52 | "\n", 53 | "## Contents\n", 54 | "* Loading the Classification Data\n", 55 | "* Using Decision Trees - Gini\n", 56 | "\n", 57 | "**These contents will become available during the live session: **\n", 58 | "* Using Decision Trees - Entropy\n", 59 | "* Multi-way Splits\n", 60 | "* Decision Trees in Scikit-Learn\n", 61 | "\n", 62 | "________________________________________________________________________________________________________\n", 63 | "\n", 64 | "Back to Top\n", 65 | "## Loading the Classification Data\n", 66 | "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", 67 | "\n" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 15, 73 | "metadata": { 74 | "collapsed": false 75 | }, 76 | "outputs": [ 77 | { 78 | "name": "stdout", 79 | "output_type": "stream", 80 | "text": [ 81 | "features shape: (1797, 64)\n", 82 | "target shape: (1797,)\n", 83 | "range of target: 0 9\n" 84 | ] 85 | } 86 | ], 87 | "source": [ 88 | "from sklearn.datasets import load_digits\n", 89 | "import numpy as np\n", 90 | "from __future__ import print_function\n", 91 | "\n", 92 | "ds = load_digits()\n", 93 | "\n", 94 | "# this holds the continuous feature data\n", 95 | "print('features shape:', ds.data.shape) # there are 1797 instances and 64 features per instance\n", 96 | "print('target shape:', ds.target.shape )\n", 97 | "print('range of target:', np.min(ds.target),np.max(ds.target))" 98 | ] 99 | }, 100 | { 101 | "cell_type": "markdown", 102 | "metadata": {}, 103 | "source": [ 104 | "________________________________________________________________________________________________________\n", 105 | "\n", 106 | "Back to Top\n", 107 | "## Using Decision Trees\n", 108 | "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", 109 | "\n", 110 | "**Question 1:** For the digits dataset, what are the type(s) of the attributes? How many attributes are there? What do they represent?\n" 111 | ] 112 | }, 113 | { 114 | "cell_type": "code", 115 | "execution_count": 3, 116 | "metadata": { 117 | "collapsed": false 118 | }, 119 | "outputs": [], 120 | "source": [ 121 | "## Enter your comments here\n", 122 | "\n", 123 | "\n", 124 | "\n", 125 | "## Enter comments here" 126 | ] 127 | }, 128 | { 129 | "cell_type": "markdown", 130 | "metadata": {}, 131 | "source": [ 132 | "___\n", 133 | "## Using the gini coefficient\n", 134 | "We talked about the gini index in the videos. The gini coefficient for a **given split** is given by:\n", 135 | "$$Gini=\\sum_{t=1}^T \\frac{n_t}{N}gini(t)$$\n", 136 | "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", 137 | "$$gini(t)=1-\\sum_{j=0}^{C-1} p(j|t)^2$$\n", 138 | "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", 139 | "$$ p(j|t) = \\frac{n_j}{n_t}$$ \n", 140 | "\n", 141 | "For the given dataset, $gini(t)$ has been programmed for you in the function `gini_index`. \n", 142 | "\n", 143 | "* `def gini_index(classes_in_split):`\n", 144 | " * 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" 145 | ] 146 | }, 147 | { 148 | "cell_type": "code", 149 | "execution_count": 16, 150 | "metadata": { 151 | "collapsed": false 152 | }, 153 | "outputs": [], 154 | "source": [ 155 | "# compute the gini of several examples for the starting dataset\n", 156 | "# This function \"gini_index\" is written for you. Once you run this block, you \n", 157 | "# will have access to the function for the notebook. You do not need to know \n", 158 | "# how this function works--only what it returns \n", 159 | "# This function returns the gini index for an array of classes in a node.\n", 160 | "def gini_index(classes_in_split):\n", 161 | " # pay no attention to this code in the function-- it just computes the gini for a given split \n", 162 | " classes_in_split = np.reshape(classes_in_split,(len(classes_in_split),-1))\n", 163 | " unique_classes = np.unique(classes_in_split)\n", 164 | " gini = 1\n", 165 | " for c in unique_classes:\n", 166 | " gini -= (np.sum(classes_in_split==c) / float(len(classes_in_split)))**2\n", 167 | " \n", 168 | " return gini" 169 | ] 170 | }, 171 | { 172 | "cell_type": "markdown", 173 | "metadata": {}, 174 | "source": [ 175 | "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", 176 | "- Feature 28 is saved into a separate variable `feature28 = ds.data[:,28]`\n", 177 | "- First all the target classes for the first node are calculated using `numpy` indexing `ds.target[feature28>2.5]` \n", 178 | " - 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", 179 | "- 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", 180 | " - `gini_r = gini_index(ds.target[feature28>2.5])`\n", 181 | "- 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", 182 | " - `gini_l = gini_index(ds.target[feature28<=2.5])`\n", 183 | "- Combining the gini indices is left as an exercise in the next section" 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": 17, 189 | "metadata": { 190 | "collapsed": false 191 | }, 192 | "outputs": [ 193 | { 194 | "name": "stdout", 195 | "output_type": "stream", 196 | "text": [ 197 | "gini for right node of split: 0.884585786767\n", 198 | "gini for left node of split: 0.711540756654\n" 199 | ] 200 | } 201 | ], 202 | "source": [ 203 | "#==========================Use the gini_index Example===============\n", 204 | "# get the value for this feature as a column vector \n", 205 | "# (this is like grabbing one column of the record table)\n", 206 | "feature28 = ds.data[:,28]\n", 207 | "\n", 208 | "# if we split on the value of 2.5, then this is the gini for each resulting node:\n", 209 | "gini_gr = gini_index(ds.target[feature28>2.5]) # just like in pandas, we are sending in the rows where feature28>2.5\n", 210 | "gini_lte = gini_index(ds.target[feature28<=2.5]) # and sending the rows where feature28<=2.5\n", 211 | "\n", 212 | "# compute gini example. This splits on attribute '28' with a value of 2.5\n", 213 | "print('gini for right node of split:', gini_gr)\n", 214 | "print('gini for left node of split:', gini_lte)" 215 | ] 216 | }, 217 | { 218 | "cell_type": "markdown", 219 | "metadata": {}, 220 | "source": [ 221 | "**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", 222 | "\n", 223 | "`counts = sum(some_array>5)` \n", 224 | " \n", 225 | "You will need to use this syntax to count the values in each node as a result of splitting. " 226 | ] 227 | }, 228 | { 229 | "cell_type": "code", 230 | "execution_count": 21, 231 | "metadata": { 232 | "collapsed": false 233 | }, 234 | "outputs": [ 235 | { 236 | "name": "stdout", 237 | "output_type": "stream", 238 | "text": [ 239 | "The total gini of the split for a threshold of 2.5 is: ??\n" 240 | ] 241 | } 242 | ], 243 | "source": [ 244 | "## Enter your code here\n", 245 | "\n", 246 | "\n", 247 | "## Enter your code here\n", 248 | "print('The total gini of the split for a threshold of 2.5 is:',\"??\")" 249 | ] 250 | }, 251 | { 252 | "cell_type": "code", 253 | "execution_count": null, 254 | "metadata": { 255 | "collapsed": false 256 | }, 257 | "outputs": [], 258 | "source": [] 259 | }, 260 | { 261 | "cell_type": "code", 262 | "execution_count": null, 263 | "metadata": { 264 | "collapsed": true 265 | }, 266 | "outputs": [], 267 | "source": [] 268 | } 269 | ], 270 | "metadata": { 271 | "anaconda-cloud": {}, 272 | "kernelspec": { 273 | "display_name": "Python [conda env:MLEnv]", 274 | "language": "python", 275 | "name": "conda-env-MLEnv-py" 276 | }, 277 | "language_info": { 278 | "codemirror_mode": { 279 | "name": "ipython", 280 | "version": 3 281 | }, 282 | "file_extension": ".py", 283 | "mimetype": "text/x-python", 284 | "name": "python", 285 | "nbconvert_exporter": "python", 286 | "pygments_lexer": "ipython3", 287 | "version": "3.5.2" 288 | } 289 | }, 290 | "nbformat": 4, 291 | "nbformat_minor": 0 292 | } 293 | -------------------------------------------------------------------------------- /ICA3_DataMining-PartA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [ 10 | { 11 | "data": { 12 | "text/html": [ 13 | "" 14 | ], 15 | "text/plain": [ 16 | "" 17 | ] 18 | }, 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "output_type": "execute_result" 22 | } 23 | ], 24 | "source": [ 25 | "# Enable HTML/CSS \n", 26 | "from IPython.core.display import HTML\n", 27 | "HTML(\"\")" 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "___\n", 35 | "Enter Team Member Names here (double click to edit):\n", 36 | "\n", 37 | "- Name 1:\n", 38 | "- Name 2:\n", 39 | "- Name 3:\n", 40 | "\n", 41 | "________\n", 42 | "\n", 43 | "# In Class Assignment Three\n", 44 | "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", 45 | "\n", 46 | "\n", 47 | "## Contents\n", 48 | "* Loading the Data\n", 49 | "* Measuring Distances\n", 50 | "\n", 51 | "** Available after class begins: **\n", 52 | "* K-Nearest Neighbors\n", 53 | "* Naive Bayes\n", 54 | "\n", 55 | "________________________________________________________________________________________________________\n", 56 | "\n", 57 | "Back to Top\n", 58 | "## Downloading the Document Data\n", 59 | "Please run the following code to read in the \"20 newsgroups\" dataset from sklearn's data loading module." 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 2, 65 | "metadata": { 66 | "collapsed": false 67 | }, 68 | "outputs": [ 69 | { 70 | "name": "stdout", 71 | "output_type": "stream", 72 | "text": [ 73 | "features shape: (11314, 130107)\n", 74 | "target shape: (11314,)\n", 75 | "range of target: 0 19\n", 76 | "Data type is 0.1214353154362896 % of the data is non-zero\n" 77 | ] 78 | } 79 | ], 80 | "source": [ 81 | "from sklearn.datasets import fetch_20newsgroups_vectorized\n", 82 | "import numpy as np\n", 83 | "from __future__ import print_function\n", 84 | "\n", 85 | "# this takes about 30 seconds to compute, read the next section while this downloads\n", 86 | "ds = fetch_20newsgroups_vectorized(subset='train')\n", 87 | "\n", 88 | "# this holds the continuous feature data (which is tfidf)\n", 89 | "print('features shape:', ds.data.shape) # there are ~11000 instances and ~130k features per instance\n", 90 | "print('target shape:', ds.target.shape) \n", 91 | "print('range of target:', np.min(ds.target),np.max(ds.target))\n", 92 | "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')" 93 | ] 94 | }, 95 | { 96 | "cell_type": "markdown", 97 | "metadata": {}, 98 | "source": [ 99 | "## Understanding the Dataset\n", 100 | "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", 101 | "\n", 102 | "**Question Set 1**:\n", 103 | "- How many instances are in the dataset? \n", 104 | "- What does each instance represent? \n", 105 | "- How many classes are in the dataset and what does each class represent?\n", 106 | "- Would you expect a classifier trained on this data would generalize to documents written in the past week? Why or why not?\n", 107 | "- Is the data represented as a sparse or dense matrix?" 108 | ] 109 | }, 110 | { 111 | "cell_type": "markdown", 112 | "metadata": {}, 113 | "source": [ 114 | "___\n", 115 | "Enter your answer here:\n", 116 | "\n", 117 | "*Double click to edit*\n", 118 | "\n", 119 | "\n", 120 | "\n" 121 | ] 122 | }, 123 | { 124 | "cell_type": "markdown", 125 | "metadata": {}, 126 | "source": [ 127 | "___\n", 128 | "\n", 129 | "Back to Top\n", 130 | "## Measures of Distance\n", 131 | "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", 132 | "- (1) Euclidean distance\n", 133 | "- (2) Cosine distance \n", 134 | "- (3) Jaccard similarity \n", 135 | "\n", 136 | "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", 137 | "\n", 138 | "**Question for part 2**: Which distance seems more appropriate to use for this data? **Why**?" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 3, 144 | "metadata": { 145 | "collapsed": false 146 | }, 147 | "outputs": [ 148 | { 149 | "name": "stdout", 150 | "output_type": "stream", 151 | "text": [ 152 | "Instance A is from class comp.graphics\n", 153 | "Instance B is from class rec.autos\n", 154 | "Instance C is from class rec.motorcycles\n", 155 | "\n", 156 | "\n", 157 | "Euclidean Distance\n", 158 | " ab: Placeholder ac: Placeholder bc: Placeholder\n", 159 | "Cosine Distance\n", 160 | " ab: Placeholder ac: Placeholder bc: Placeholder\n", 161 | "Jaccard Dissimilarity (vectors should be boolean values)\n", 162 | " ab: Placeholder ac: Placeholder bc: Placeholder\n", 163 | "\n", 164 | "\n", 165 | "The most appropriate distance is...\n", 166 | "Placeholder\n" 167 | ] 168 | } 169 | ], 170 | "source": [ 171 | "from scipy.spatial.distance import cosine\n", 172 | "from scipy.spatial.distance import euclidean\n", 173 | "from scipy.spatial.distance import jaccard\n", 174 | "import numpy as np\n", 175 | "\n", 176 | "# get first instance (comp)\n", 177 | "idx = 550\n", 178 | "a = ds.data[idx].todense()\n", 179 | "a_class = ds.target_names[ds.target[idx]]\n", 180 | "print('Instance A is from class', a_class)\n", 181 | "\n", 182 | "# get second instance (autos)\n", 183 | "idx = 4000\n", 184 | "b = ds.data[idx].todense()\n", 185 | "b_class = ds.target_names[ds.target[idx]]\n", 186 | "print('Instance B is from class', b_class)\n", 187 | "\n", 188 | "# get third instance (motorcycle)\n", 189 | "idx = 7000\n", 190 | "c = ds.data[idx].todense()\n", 191 | "c_class = ds.target_names[ds.target[idx]]\n", 192 | "print('Instance C is from class', c_class)\n", 193 | "\n", 194 | "# Enter distance comparison below for each pair of vectors:\n", 195 | "p = 'Placeholder'\n", 196 | "print('\\n\\nEuclidean Distance\\n ab:', p, 'ac:', p, 'bc:',p)\n", 197 | "print('Cosine Distance\\n ab:', p, 'ac:', p, 'bc:', p)\n", 198 | "print('Jaccard Dissimilarity (vectors should be boolean values)\\n ab:', p, 'ac:', p, 'bc:', p)\n", 199 | "\n", 200 | "print('\\n\\nThe most appropriate distance is...')\n", 201 | "print(p)" 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": null, 207 | "metadata": { 208 | "collapsed": true 209 | }, 210 | "outputs": [], 211 | "source": [] 212 | } 213 | ], 214 | "metadata": { 215 | "anaconda-cloud": {}, 216 | "kernelspec": { 217 | "display_name": "Python [conda env:MLEnv]", 218 | "language": "python", 219 | "name": "conda-env-MLEnv-py" 220 | }, 221 | "language_info": { 222 | "codemirror_mode": { 223 | "name": "ipython", 224 | "version": 3 225 | }, 226 | "file_extension": ".py", 227 | "mimetype": "text/x-python", 228 | "name": "python", 229 | "nbconvert_exporter": "python", 230 | "pygments_lexer": "ipython3", 231 | "version": "3.5.2" 232 | } 233 | }, 234 | "nbformat": 4, 235 | "nbformat_minor": 0 236 | } 237 | -------------------------------------------------------------------------------- /ICA5-PartA/titanic2.raw.rdata: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eclarson/DataMiningNotebooks/d36923731a764ee246e4e343a3cba780ff3c8ab4/ICA5-PartA/titanic2.raw.rdata -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DataMiningNotebooks 2 | 3 | Please see the following data mining example notebooks that go along with my Data Mining 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/eclarson/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 Larson (eclarson@smu.edu) -------------------------------------------------------------------------------- /Syllabus.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eclarson/DataMiningNotebooks/d36923731a764ee246e4e343a3cba780ff3c8ab4/Syllabus.pdf -------------------------------------------------------------------------------- /data/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eclarson/DataMiningNotebooks/d36923731a764ee246e4e343a3cba780ff3c8ab4/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/eclarson/DataMiningNotebooks/d36923731a764ee246e4e343a3cba780ff3c8ab4/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/eclarson/DataMiningNotebooks/d36923731a764ee246e4e343a3cba780ff3c8ab4/data/heart_disease_sql -------------------------------------------------------------------------------- /data/python_ranking.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eclarson/DataMiningNotebooks/d36923731a764ee246e4e343a3cba780ff3c8ab4/data/python_ranking.png -------------------------------------------------------------------------------- /data/titanic.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C 3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S 4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S 5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S 6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q 7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S 8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S 9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S 10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C 11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S 12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S 13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S 14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S 15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S 16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S 17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q 18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S 19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S 20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C 21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S 22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S 23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q 24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S 25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S 26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S 27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C 28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S 29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q 30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S 31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C 32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C 33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q 34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S 35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C 36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S 37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C 38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S 39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S 40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C 41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S 42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S 43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C 44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C 45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q 46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S 47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q 48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q 49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C 50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S 51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S 52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S 53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C 54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S 55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C 56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S 57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S 58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C 59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S 60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S 61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C 62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28, 63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S 64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S 65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C 66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C 67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S 68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S 69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S 70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S 71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S 72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S 73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S 74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C 75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S 76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S 77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S 78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S 79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S 80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S 81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S 82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S 83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q 84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S 85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S 86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S 87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S 88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S 89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S 90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S 91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S 92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S 93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S 94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S 95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S 96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S 97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C 98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C 99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S 100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S 101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S 102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S 103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S 104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S 105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S 106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S 107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S 108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S 109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S 110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q 111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S 112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C 113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S 114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S 115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C 116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S 117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q 118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S 119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C 120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S 121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S 122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S 123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C 124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S 125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S 126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C 127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q 128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S 129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C 130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S 131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C 132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S 133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S 134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S 135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S 136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C 137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S 138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S 139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S 140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C 141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C 142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S 143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S 144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q 145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S 146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S 147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S 148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S 149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S 150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S 151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S 152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S 153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S 154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S 155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S 156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C 157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q 158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S 159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S 160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S 161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S 162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S 163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S 164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S 165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S 166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S 167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S 168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S 169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S 170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S 171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S 172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q 173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S 174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S 175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C 176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S 177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S 178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C 179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S 180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S 181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S 182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C 183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S 184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S 185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S 186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S 187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q 188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S 189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q 190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S 191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S 192,0,2,"Carbines, Mr. William",male,19,0,0,28424,13,,S 193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S 194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S 195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44,0,0,PC 17610,27.7208,B4,C 196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C 197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q 198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S 199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q 200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S 201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S 202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S 203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S 204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C 205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S 206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S 207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S 208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C 209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q 210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C 211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S 212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S 213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S 214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S 215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q 216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C 217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S 218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S 219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C 220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S 221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S 222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S 223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S 224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S 225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S 226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S 227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S 228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S 229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S 230,0,3,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S 231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S 232,0,3,"Larsson, Mr. Bengt Edvin",male,29,0,0,347067,7.775,,S 233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S 234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S 235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S 236,0,3,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S 237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S 238,1,2,"Collyer, Miss. Marjorie ""Lottie""",female,8,0,2,C.A. 31921,26.25,,S 239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S 240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S 241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C 242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q 243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S 244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S 245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C 246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q 247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S 248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S 249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S 250,0,2,"Carter, Rev. Ernest Courtenay",male,54,1,0,244252,26,,S 251,0,3,"Reed, Mr. James George",male,,0,0,362316,7.25,,S 252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S 253,0,1,"Stead, Mr. William Thomas",male,62,0,0,113514,26.55,C87,S 254,0,3,"Lobb, Mr. William Arthur",male,30,1,0,A/5. 3336,16.1,,S 255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S 256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C 257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C 258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S 259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C 260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S 261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q 262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S 263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S 264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S 265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q 266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S 267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S 268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S 269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S 270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S 271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S 272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S 273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S 274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C 275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q 276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S 277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S 278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S 279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q 280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S 281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q 282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S 283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S 284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S 285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S 286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C 287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S 288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S 289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S 290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q 291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S 292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C 293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C 294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S 295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S 296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C 297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C 298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S 299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S 300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C 301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q 302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q 303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S 304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q 305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S 306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S 307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C 308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C 309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C 310,1,1,"Francatelli, Miss. Laura Mabel",female,30,0,0,PC 17485,56.9292,E36,C 311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C 312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C 313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S 314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S 315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S 316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S 317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S 318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S 319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S 320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C 321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S 322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S 323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q 324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S 325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S 326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C 327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S 328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S 329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S 330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C 331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q 332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S 333,0,1,"Graham, Mr. George Edward",male,38,0,1,PC 17582,153.4625,C91,S 334,0,3,"Vander Planke, Mr. Leo Edmondus",male,16,2,0,345764,18,,S 335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S 336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S 337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S 338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C 339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S 340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S 341,1,2,"Navratil, Master. Edmond Roger",male,2,1,1,230080,26,F2,S 342,1,1,"Fortune, Miss. Alice Elizabeth",female,24,3,2,19950,263,C23 C25 C27,S 343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S 344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S 345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S 346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S 347,1,2,"Smith, Miss. Marion Elsie",female,40,0,0,31418,13,,S 348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S 349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S 350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S 351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S 352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S 353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C 354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S 355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C 356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S 357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S 358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S 359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q 360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q 361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S 362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C 363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C 364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S 365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q 366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S 367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C 368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C 369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q 370,1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3,B35,C 371,1,1,"Harder, Mr. George Achilles",male,25,1,0,11765,55.4417,E50,C 372,0,3,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S 373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S 374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C 375,0,3,"Palsson, Miss. Stina Viola",female,3,3,1,349909,21.075,,S 376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C 377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S 378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C 379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C 380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S 381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C 382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C 383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S 384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S 385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S 386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S 387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S 388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S 389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q 390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C 391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S 392,1,3,"Jansson, Mr. Carl Olof",male,21,0,0,350034,7.7958,,S 393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S 394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C 395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24,0,2,PP 9549,16.7,G6,S 396,0,3,"Johansson, Mr. Erik",male,22,0,0,350052,7.7958,,S 397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S 398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S 399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S 400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S 401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S 402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S 403,0,3,"Jussila, Miss. Mari Aina",female,21,1,0,4137,9.825,,S 404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S 405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S 406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S 407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S 408,1,2,"Richards, Master. William Rowe",male,3,1,1,29106,18.75,,S 409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S 410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S 411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S 412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q 413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q 414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S 415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S 416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S 417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S 418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S 419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S 420,0,3,"Van Impe, Miss. Catharina",female,10,0,2,345773,24.15,,S 421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C 422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q 423,0,3,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.875,,S 424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S 425,0,3,"Rosblom, Mr. Viktor Richard",male,18,1,1,370129,20.2125,,S 426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S 427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28,1,0,2003,26,,S 428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S 429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q 430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32,0,0,SOTON/O.Q. 392078,8.05,E10,S 431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28,0,0,110564,26.55,C52,S 432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S 433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42,1,0,SC/AH 3085,26,,S 434,0,3,"Kallio, Mr. Nikolai Erland",male,17,0,0,STON/O 2. 3101274,7.125,,S 435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S 436,1,1,"Carter, Miss. Lucile Polk",female,14,1,2,113760,120,B96 B98,S 437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21,2,2,W./C. 6608,34.375,,S 438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S 439,0,1,"Fortune, Mr. Mark",male,64,1,4,19950,263,C23 C25 C27,S 440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31,0,0,C.A. 18723,10.5,,S 441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S 442,0,3,"Hampe, Mr. Leon",male,20,0,0,345769,9.5,,S 443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S 444,1,2,"Reynaldo, Ms. Encarnacion",female,28,0,0,230434,13,,S 445,1,3,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S 446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S 447,1,2,"Mellinger, Miss. Madeleine Violet",female,13,0,1,250644,19.5,,S 448,1,1,"Seward, Mr. Frederic Kimber",male,34,0,0,113794,26.55,,S 449,1,3,"Baclini, Miss. Marie Catherine",female,5,2,1,2666,19.2583,,C 450,1,1,"Peuchen, Major. Arthur Godfrey",male,52,0,0,113786,30.5,C104,S 451,0,2,"West, Mr. Edwy Arthur",male,36,1,2,C.A. 34651,27.75,,S 452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S 453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C 454,1,1,"Goldenberg, Mr. Samuel L",male,49,1,0,17453,89.1042,C92,C 455,0,3,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S 456,1,3,"Jalsevac, Mr. Ivan",male,29,0,0,349240,7.8958,,C 457,0,1,"Millet, Mr. Francis Davis",male,65,0,0,13509,26.55,E38,S 458,1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S 459,1,2,"Toomey, Miss. Ellen",female,50,0,0,F.C.C. 13531,10.5,,S 460,0,3,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q 461,1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.55,E12,S 462,0,3,"Morley, Mr. William",male,34,0,0,364506,8.05,,S 463,0,1,"Gee, Mr. Arthur H",male,47,0,0,111320,38.5,E63,S 464,0,2,"Milling, Mr. Jacob Christian",male,48,0,0,234360,13,,S 465,0,3,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S 466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38,0,0,SOTON/O.Q. 3101306,7.05,,S 467,0,2,"Campbell, Mr. William",male,,0,0,239853,0,,S 468,0,1,"Smart, Mr. John Montgomery",male,56,0,0,113792,26.55,,S 469,0,3,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q 470,1,3,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C 471,0,3,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S 472,0,3,"Cacic, Mr. Luka",male,38,0,0,315089,8.6625,,S 473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33,1,2,C.A. 34651,27.75,,S 474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23,0,0,SC/AH Basle 541,13.7917,D,C 475,0,3,"Strandberg, Miss. Ida Sofia",female,22,0,0,7553,9.8375,,S 476,0,1,"Clifford, Mr. George Quincy",male,,0,0,110465,52,A14,S 477,0,2,"Renouf, Mr. Peter Henry",male,34,1,0,31027,21,,S 478,0,3,"Braund, Mr. Lewis Richard",male,29,1,0,3460,7.0458,,S 479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S 480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S 481,0,3,"Goodwin, Master. Harold Victor",male,9,5,2,CA 2144,46.9,,S 482,0,2,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0,,S 483,0,3,"Rouse, Mr. Richard Henry",male,50,0,0,A/5 3594,8.05,,S 484,1,3,"Turkula, Mrs. (Hedwig)",female,63,0,0,4134,9.5875,,S 485,1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C 486,0,3,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S 487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35,1,0,19943,90,C93,S 488,0,1,"Kent, Mr. Edward Austin",male,58,0,0,11771,29.7,B37,C 489,0,3,"Somerton, Mr. Francis William",male,30,0,0,A.5. 18509,8.05,,S 490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9,1,1,C.A. 37671,15.9,,S 491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S 492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S 493,0,1,"Molson, Mr. Harry Markland",male,55,0,0,113787,30.5,C30,S 494,0,1,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C 495,0,3,"Stanley, Mr. Edward Roland",male,21,0,0,A/4 45380,8.05,,S 496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C 497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54,1,0,36947,78.2667,D20,C 498,0,3,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S 499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.55,C22 C26,S 500,0,3,"Svensson, Mr. Olof",male,24,0,0,350035,7.7958,,S 501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S 502,0,3,"Canavan, Miss. Mary",female,21,0,0,364846,7.75,,Q 503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q 504,0,3,"Laitinen, Miss. Kristina Sofia",female,37,0,0,4135,9.5875,,S 505,1,1,"Maioni, Miss. Roberta",female,16,0,0,110152,86.5,B79,S 506,0,1,"Penasco y Castellana, Mr. Victor de Satode",male,18,1,0,PC 17758,108.9,C65,C 507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33,0,2,26360,26,,S 508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S 509,0,3,"Olsen, Mr. Henry Margido",male,28,0,0,C 4001,22.525,,S 510,1,3,"Lang, Mr. Fang",male,26,0,0,1601,56.4958,,S 511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q 512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S 513,1,1,"McGough, Mr. James Robert",male,36,0,0,PC 17473,26.2875,E25,S 514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54,1,0,PC 17603,59.4,,C 515,0,3,"Coleff, Mr. Satio",male,24,0,0,349209,7.4958,,S 516,0,1,"Walker, Mr. William Anderson",male,47,0,0,36967,34.0208,D46,S 517,1,2,"Lemore, Mrs. (Amelia Milley)",female,34,0,0,C.A. 34260,10.5,F33,S 518,0,3,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q 519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36,1,0,226875,26,,S 520,0,3,"Pavlovic, Mr. Stefo",male,32,0,0,349242,7.8958,,S 521,1,1,"Perreault, Miss. Anne",female,30,0,0,12749,93.5,B73,S 522,0,3,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S 523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C 524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44,0,1,111361,57.9792,B18,C 525,0,3,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C 526,0,3,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q 527,1,2,"Ridsdale, Miss. Lucy",female,50,0,0,W./C. 14258,10.5,,S 528,0,1,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S 529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S 530,0,2,"Hocking, Mr. Richard George",male,23,2,1,29104,11.5,,S 531,1,2,"Quick, Miss. Phyllis May",female,2,1,1,26360,26,,S 532,0,3,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C 533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C 534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C 535,0,3,"Cacic, Miss. Marija",female,30,0,0,315084,8.6625,,S 536,1,2,"Hart, Miss. Eva Miriam",female,7,0,2,F.C.C. 13529,26.25,,S 537,0,1,"Butt, Major. Archibald Willingham",male,45,0,0,113050,26.55,B38,S 538,1,1,"LeRoy, Miss. Bertha",female,30,0,0,PC 17761,106.425,,C 539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S 540,1,1,"Frolicher, Miss. Hedwig Margaritha",female,22,0,2,13568,49.5,B39,C 541,1,1,"Crosby, Miss. Harriet R",female,36,0,2,WE/P 5735,71,B22,S 542,0,3,"Andersson, Miss. Ingeborg Constanzia",female,9,4,2,347082,31.275,,S 543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S 544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S 545,0,1,"Douglas, Mr. Walter Donald",male,50,1,0,PC 17761,106.425,C86,C 546,0,1,"Nicholson, Mr. Arthur Ernest",male,64,0,0,693,26,,S 547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",female,19,1,0,2908,26,,S 548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C 549,0,3,"Goldsmith, Mr. Frank John",male,33,1,1,363291,20.525,,S 550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S 551,1,1,"Thayer, Mr. John Borland Jr",male,17,0,2,17421,110.8833,C70,C 552,0,2,"Sharp, Mr. Percival James R",male,27,0,0,244358,26,,S 553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q 554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22,0,0,2620,7.225,,C 555,1,3,"Ohman, Miss. Velin",female,22,0,0,347085,7.775,,S 556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S 557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48,1,0,11755,39.6,A16,C 558,0,1,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C 559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39,1,1,110413,79.65,E67,S 560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36,1,0,345572,17.4,,S 561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q 562,0,3,"Sivic, Mr. Husein",male,40,0,0,349251,7.8958,,S 563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S 564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S 565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S 566,0,3,"Davies, Mr. Alfred J",male,24,2,0,A/4 48871,24.15,,S 567,0,3,"Stoytcheff, Mr. Ilia",male,19,0,0,349205,7.8958,,S 568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29,0,4,349909,21.075,,S 569,0,3,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C 570,1,3,"Jonsson, Mr. Carl",male,32,0,0,350417,7.8542,,S 571,1,2,"Harris, Mr. George",male,62,0,0,S.W./PP 752,10.5,,S 572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S 573,1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36,0,0,PC 17474,26.3875,E25,S 574,1,3,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q 575,0,3,"Rush, Mr. Alfred George John",male,16,0,0,A/4. 20589,8.05,,S 576,0,3,"Patchett, Mr. George",male,19,0,0,358585,14.5,,S 577,1,2,"Garside, Miss. Ethel",female,34,0,0,243880,13,,S 578,1,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39,1,0,13507,55.9,E44,S 579,0,3,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C 580,1,3,"Jussila, Mr. Eiriik",male,32,0,0,STON/O 2. 3101286,7.925,,S 581,1,2,"Christy, Miss. Julie Rachel",female,25,1,1,237789,30,,S 582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39,1,1,17421,110.8833,C68,C 583,0,2,"Downton, Mr. William James",male,54,0,0,28403,26,,S 584,0,1,"Ross, Mr. John Hugo",male,36,0,0,13049,40.125,A10,C 585,0,3,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C 586,1,1,"Taussig, Miss. Ruth",female,18,0,2,110413,79.65,E68,S 587,0,2,"Jarvis, Mr. John Denzil",male,47,0,0,237565,15,,S 588,1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60,1,1,13567,79.2,B41,C 589,0,3,"Gilinski, Mr. Eliezer",male,22,0,0,14973,8.05,,S 590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S 591,0,3,"Rintamaki, Mr. Matti",male,35,0,0,STON/O 2. 3101273,7.125,,S 592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52,1,0,36947,78.2667,D20,C 593,0,3,"Elsbury, Mr. William James",male,47,0,0,A/5 3902,7.25,,S 594,0,3,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q 595,0,2,"Chapman, Mr. John Henry",male,37,1,0,SC/AH 29037,26,,S 596,0,3,"Van Impe, Mr. Jean Baptiste",male,36,1,1,345773,24.15,,S 597,1,2,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33,,S 598,0,3,"Johnson, Mr. Alfred",male,49,0,0,LINE,0,,S 599,0,3,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C 600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49,1,0,PC 17485,56.9292,A20,C 601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24,2,1,243847,27,,S 602,0,3,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S 603,0,1,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S 604,0,3,"Torber, Mr. Ernst William",male,44,0,0,364511,8.05,,S 605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35,0,0,111426,26.55,,C 606,0,3,"Lindell, Mr. Edvard Bengtsson",male,36,1,0,349910,15.55,,S 607,0,3,"Karaic, Mr. Milan",male,30,0,0,349246,7.8958,,S 608,1,1,"Daniel, Mr. Robert Williams",male,27,0,0,113804,30.5,,S 609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22,1,2,SC/Paris 2123,41.5792,,C 610,1,1,"Shutes, Miss. Elizabeth W",female,40,0,0,PC 17582,153.4625,C125,S 611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39,1,5,347082,31.275,,S 612,0,3,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S 613,1,3,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q 614,0,3,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q 615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S 616,1,2,"Herman, Miss. Alice",female,24,1,2,220845,65,,S 617,0,3,"Danbom, Mr. Ernst Gilbert",male,34,1,1,347080,14.4,,S 618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26,1,0,A/5. 3336,16.1,,S 619,1,2,"Becker, Miss. Marion Louise",female,4,2,1,230136,39,F4,S 620,0,2,"Gavey, Mr. Lawrence",male,26,0,0,31028,10.5,,S 621,0,3,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C 622,1,1,"Kimball, Mr. Edwin Nelson Jr",male,42,1,0,11753,52.5542,D19,S 623,1,3,"Nakid, Mr. Sahid",male,20,1,1,2653,15.7417,,C 624,0,3,"Hansen, Mr. Henry Damsgaard",male,21,0,0,350029,7.8542,,S 625,0,3,"Bowen, Mr. David John ""Dai""",male,21,0,0,54636,16.1,,S 626,0,1,"Sutton, Mr. Frederick",male,61,0,0,36963,32.3208,D50,S 627,0,2,"Kirkland, Rev. Charles Leonard",male,57,0,0,219533,12.35,,Q 628,1,1,"Longley, Miss. Gretchen Fiske",female,21,0,0,13502,77.9583,D9,S 629,0,3,"Bostandyeff, Mr. Guentcho",male,26,0,0,349224,7.8958,,S 630,0,3,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q 631,1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30,A23,S 632,0,3,"Lundahl, Mr. Johan Svensson",male,51,0,0,347743,7.0542,,S 633,1,1,"Stahelin-Maeglin, Dr. Max",male,32,0,0,13214,30.5,B50,C 634,0,1,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0,,S 635,0,3,"Skoog, Miss. Mabel",female,9,3,2,347088,27.9,,S 636,1,2,"Davis, Miss. Mary",female,28,0,0,237668,13,,S 637,0,3,"Leinonen, Mr. Antti Gustaf",male,32,0,0,STON/O 2. 3101292,7.925,,S 638,0,2,"Collyer, Mr. Harvey",male,31,1,1,C.A. 31921,26.25,,S 639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41,0,5,3101295,39.6875,,S 640,0,3,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S 641,0,3,"Jensen, Mr. Hans Peder",male,20,0,0,350050,7.8542,,S 642,1,1,"Sagesser, Mlle. Emma",female,24,0,0,PC 17477,69.3,B35,C 643,0,3,"Skoog, Miss. Margit Elizabeth",female,2,3,2,347088,27.9,,S 644,1,3,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S 645,1,3,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C 646,1,1,"Harper, Mr. Henry Sleeper",male,48,1,0,PC 17572,76.7292,D33,C 647,0,3,"Cor, Mr. Liudevit",male,19,0,0,349231,7.8958,,S 648,1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56,0,0,13213,35.5,A26,C 649,0,3,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S 650,1,3,"Stanley, Miss. Amy Zillah Elsie",female,23,0,0,CA. 2314,7.55,,S 651,0,3,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S 652,1,2,"Doling, Miss. Elsie",female,18,0,1,231919,23,,S 653,0,3,"Kalvik, Mr. Johannes Halvorsen",male,21,0,0,8475,8.4333,,S 654,1,3,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q 655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18,0,0,365226,6.75,,Q 656,0,2,"Hickman, Mr. Leonard Mark",male,24,2,0,S.O.C. 14879,73.5,,S 657,0,3,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S 658,0,3,"Bourke, Mrs. John (Catherine)",female,32,1,1,364849,15.5,,Q 659,0,2,"Eitemiller, Mr. George Floyd",male,23,0,0,29751,13,,S 660,0,1,"Newell, Mr. Arthur Webster",male,58,0,2,35273,113.275,D48,C 661,1,1,"Frauenthal, Dr. Henry William",male,50,2,0,PC 17611,133.65,,S 662,0,3,"Badt, Mr. Mohamed",male,40,0,0,2623,7.225,,C 663,0,1,"Colley, Mr. Edward Pomeroy",male,47,0,0,5727,25.5875,E58,S 664,0,3,"Coleff, Mr. Peju",male,36,0,0,349210,7.4958,,S 665,1,3,"Lindqvist, Mr. Eino William",male,20,1,0,STON/O 2. 3101285,7.925,,S 666,0,2,"Hickman, Mr. Lewis",male,32,2,0,S.O.C. 14879,73.5,,S 667,0,2,"Butler, Mr. Reginald Fenton",male,25,0,0,234686,13,,S 668,0,3,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S 669,0,3,"Cook, Mr. Jacob",male,43,0,0,A/5 3536,8.05,,S 670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52,C126,S 671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40,1,1,29750,39,,S 672,0,1,"Davidson, Mr. Thornton",male,31,1,0,F.C. 12750,52,B71,S 673,0,2,"Mitchell, Mr. Henry Michael",male,70,0,0,C.A. 24580,10.5,,S 674,1,2,"Wilhelms, Mr. Charles",male,31,0,0,244270,13,,S 675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0,,S 676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18,0,0,349912,7.775,,S 677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S 678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S 679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S 680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C 681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q 682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C 683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S 684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S 685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S 686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C 687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S 688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S 689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S 690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S 691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S 692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C 693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S 694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C 695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S 696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S 697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S 698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q 699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C 700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S 701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.525,C62 C64,C 702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S 703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C 704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q 705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S 706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S 707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S 708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S 709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S 710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C 711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C 712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S 713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S 714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S 715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S 716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S 717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C 718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S 719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q 720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S 721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S 722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S 723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S 724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S 725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S 726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S 727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S 728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q 729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S 730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S 731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S 732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C 733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S 734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S 735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S 736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S 737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S 738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C 739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S 740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S 741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S 742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S 743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C 744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S 745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S 746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S 747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S 748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S 749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S 750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q 751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S 752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S 753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S 754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S 755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S 756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S 757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S 758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S 759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S 760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S 761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S 762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S 763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C 764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S 765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S 766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S 767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C 768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q 769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q 770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S 771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S 772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S 773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S 774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C 775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S 776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S 777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q 778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S 779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q 780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S 781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C 782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S 783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S 784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S 785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S 786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S 787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S 788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q 789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S 790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C 791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q 792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S 793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S 794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C 795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S 796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S 797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S 798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S 799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C 800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S 801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S 802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S 803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S 804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C 805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S 806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S 807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S 808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S 809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S 810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S 811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S 812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S 813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S 814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S 815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S 816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S 817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S 818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C 819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S 820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S 821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S 822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S 823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S 824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S 825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S 826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q 827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S 828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C 829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q 830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28, 831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C 832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S 833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C 834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S 835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S 836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C 837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S 838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S 839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S 840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C 841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S 842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S 843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C 844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C 845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S 846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S 847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S 848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C 849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S 850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C 851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S 852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S 853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C 854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S 855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S 856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S 857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S 858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S 859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C 860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C 861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S 862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S 863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S 864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S 865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S 866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S 867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C 868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S 869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S 870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S 871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S 872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S 873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S 874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S 875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C 876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C 877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S 878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S 879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S 880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C 881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S 882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S 883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S 884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S 885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S 886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q 887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S 888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S 889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S 890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C 891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q -------------------------------------------------------------------------------- /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) --------------------------------------------------------------------------------