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154 155 </div> 156 <div class="bg-dark content-sidebar"> 157 <div class="sidebar-holder"> 158 <div class="brand"> 159 <a href="/">investigate.ai</a> 160 <br> 161 data science for everybody 162 </div> 163 164 <div class="accordion-container"> 165 <div class="accordion"> 166 <div class="form-group sidebar-sticky"> 167 <form method="GET" action="/search/" class="search-form"> 168 <div class="input-group"> 169 <input type="text" class="form-input input-sm" name="q" placeholder="Search investigate.ai"> 170 <button class="btn btn-primary input-group-btn btn-sm">Search</button> 171 </div> 172 </form> 173 </div> 174 175 176 <h4 id="textanalysis" class="sidebar-sticky"> 177 <a href="#textanalysis">Text analysis</a> 178 </h4> 179 <ol class="menu menu-nav"> 180 181 <li class="menu-item"> 182 183 <a href="/text-analysis/types-of-text-analysis/"> 184 Types of text analysis 185 </a> 186 <div class="toc"></div> 187 188 189 190 </li> 191 192 <li class="menu-item"> 193 194 195 196 <input id="accordion-countingwords" type="checkbox" name="accordion-checkbox" hidden=""> 197 <label class="accordion-header c-hand" for="accordion-countingwords"> 198 <i class="icon icon-arrow-right mr-1"></i> 199 Counting words 200 </label> 201 <div class="accordion-body"> 202 <ol class="menu menu-nav"> 203 204 <li class="menu-item"> 205 206 <a href="/text-analysis/counting-words-with-pythons-counter/"> 207 Simple word counting 208 </a> 209 <div class="toc"></div> 210 211 212 213 </li> 214 215 <li class="menu-item"> 216 217 <a href="/text-analysis/counting-words-with-scikit-learns-countvectorizer/"> 218 Counting words across many documents 219 </a> 220 <div class="toc"></div> 221 222 223 224 </li> 225 226 <li class="menu-item"> 227 228 <a href="/text-analysis/splitting-words-in-east-asian-languages/"> 229 Segmenting words in East Asian languages 230 </a> 231 <div class="toc"></div> 232 233 234 235 </li> 236 237 <li class="menu-item"> 238 239 <a href="/caixin-museum-word-count/counting-words-in-chinese-museum-names/"> 240 Project: Caixin museums 241 </a> 242 <div class="toc"></div> 243 244 245 246 </li> 247 248 <li class="menu-item"> 249 250 <a href="/text-analysis/how-to-make-scikit-learn-natural-language-processing-work-with-japanese-chinese/"> 251 Using scikit-learn vectorizers with East Asian languages 252 </a> 253 <div class="toc"></div> 254 255 256 257 </li> 258 259 </ol> 260 </div> 261 262 </li> 263 264 <li class="menu-item"> 265 266 267 268 <input id="accordion-advancedwordanalysis" type="checkbox" name="accordion-checkbox" hidden=""> 269 <label class="accordion-header c-hand" for="accordion-advancedwordanalysis"> 270 <i class="icon icon-arrow-right mr-1"></i> 271 Advanced word analysis 272 </label> 273 <div class="accordion-body"> 274 <ol class="menu menu-nav"> 275 276 <li class="menu-item"> 277 278 <a href="/text-analysis/a-simple-explanation-of-tf-idf/"> 279 Upgraded word counts with TF-IDF 280 </a> 281 <div 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class="accordion-body"> 335 <ol class="menu menu-nav"> 336 337 <li class="menu-item"> 338 339 <a href="/investigating-sentiment-analysis/comparing-sentiment-analysis-tools/"> 340 Comparing sentiment analysis tools 341 </a> 342 <div class="toc"></div> 343 344 345 346 </li> 347 348 <li class="menu-item"> 349 350 <a href="/investigating-sentiment-analysis/designing-your-own-sentiment-analysis-tool/"> 351 Design your own sentiment analyzer 352 </a> 353 <div class="toc"></div> 354 355 356 357 </li> 358 359 <li class="menu-item"> 360 361 <a href="/investigating-sentiment-analysis/more-data-to-train-our-sentiment-analysis-tool/"> 362 Improving your tool 363 </a> 364 <div class="toc"></div> 365 366 367 368 </li> 369 370 <li class="menu-item"> 371 372 <a href="/upshot-trump-emolex/nrc-emotional-lexicon/"> 373 NRC Emotional Lexicon 374 </a> 375 <div class="toc"></div> 376 377 378 379 </li> 380 381 <li class="menu-item"> 382 383 <a href="/upshot-trump-emolex/trump-vs-state-of-the-union-addresses/"> 384 Project: UpShot State of the Union 385 </a> 386 <div class="toc"></div> 387 388 389 390 </li> 391 392 <li class="menu-item"> 393 394 <a href="/nyt-trump-tweets/"> 395 Project: NYT Trump tweets 396 </a> 397 <div class="toc"></div> 398 399 400 401 </li> 402 403 </ol> 404 </div> 405 406 </li> 407 408 <li class="menu-item"> 409 410 411 412 <input id="accordion-documentstotext" type="checkbox" name="accordion-checkbox" hidden=""> 413 <label class="accordion-header c-hand" for="accordion-documentstotext"> 414 <i class="icon icon-arrow-right mr-1"></i> 415 Documents to text 416 </label> 417 <div class="accordion-body"> 418 <ol class="menu menu-nav"> 419 420 <li class="menu-item"> 421 422 <a href="/text-analysis/processing-documents-with-apache-tika/"> 423 Converting documents to text (English) 424 </a> 425 <div class="toc"></div> 426 427 428 429 </li> 430 431 <li class="menu-item"> 432 433 <a href="/text-analysis/processing-documents-with-apache-tika-greek/"> 434 Converting documents to text (non-English) 435 </a> 436 <div class="toc"></div> 437 438 439 440 </li> 441 442 </ol> 443 </div> 444 445 </li> 446 447 <li class="menu-item"> 448 449 450 451 <input id="accordion-conceptspeopleandplaces" type="checkbox" name="accordion-checkbox" hidden=""> 452 <label class="accordion-header c-hand" for="accordion-conceptspeopleandplaces"> 453 <i class="icon icon-arrow-right mr-1"></i> 454 Concepts, people and places 455 </label> 456 <div class="accordion-body"> 457 <ol class="menu menu-nav"> 458 459 <li class="menu-item"> 460 461 <a href="/text-analysis/introduction-to-topic-modeling/"> 462 Extracting topics from documents 463 </a> 464 <div class="toc"></div> 465 466 467 468 </li> 469 470 <li class="menu-item"> 471 472 <a href="/text-analysis/choosing-the-right-number-of-topics-for-a-scikit-learn-topic-model/"> 473 Choosing the right number of topics 474 </a> 475 <div class="toc"></div> 476 477 478 479 </li> 480 481 <li class="menu-item"> 482 483 <a href="/text-analysis/topic-models-with-gensim/"> 484 Topic models with Gensim 485 </a> 486 <div class="toc"></div> 487 488 489 490 </li> 491 492 <li class="menu-item"> 493 494 <a href="/text-analysis/topic-modeling-and-clustering/"> 495 Topic models vs clustering
496 </a> 497 <div class="toc"></div> 498 499 500 501 </li> 502 503 <li class="menu-item"> 504 505 <a href="/text-analysis/named-entity-recognition/"> 506 Entity recognition 507 </a> 508 <div class="toc"></div> 509 510 511 512 </li> 513 514 <li class="menu-item"> 515 516 <a href="/text-analysis/word-embeddings/"> 517 Intro to word embeddings 518 </a> 519 <div class="toc"></div> 520 521 522 523 </li> 524 525 <li class="menu-item"> 526 527 <a href="/text-analysis/document-similarity-using-word-embeddings/"> 528 Conceptual document similarity 529 </a> 530 <div class="toc"></div> 531 532 533 534 </li> 535 536 <li class="menu-item"> 537 538 <a href="/text-analysis/comparing-documents-in-different-languages/"> 539 Comparing documents in different languages 540 </a> 541 <div class="toc"></div> 542 543 544 545 </li> 546 547 </ol> 548 </div> 549 550 </li> 551 552 </ol> 553 554 <h4 id="puttingthingsincategoriesautomatically" class="sidebar-sticky"> 555 <a href="#puttingthingsincategoriesautomatically">Putting things in categories automatically</a> 556 </h4> 557 <ol class="menu menu-nav"> 558 559 <li class="menu-item"> 560 561 <a href="/classification/intro-to-classification/"> 562 Introduction to Classification 563 </a> 564 <div class="toc"></div> 565 566 567 568 </li> 569 570 <li class="menu-item"> 571 572 573 574 <input id="accordion-techniques" type="checkbox" name="accordion-checkbox" hidden=""> 575 <label class="accordion-header c-hand" for="accordion-techniques"> 576 <i class="icon icon-arrow-right mr-1"></i> 577 Techniques 578 </label> 579 <div class="accordion-body"> 580 <ol class="menu menu-nav"> 581 582 <li class="menu-item"> 583 584 <a href="/classification/evaluating-classifiers/"> 585 Evaluating classifiers 586 </a> 587 <div class="toc"></div> 588 589 590 591 </li> 592 593 <li class="menu-item"> 594 595 <a href="/classification/scikit-learn-and-categorical-features/"> 596 Categorical features 597 </a> 598 <div class="toc"></div> 599 600 601 602 </li> 603 604 <li class="menu-item"> 605 606 <a href="/classification/using-classification-algorithms-with-text/"> 607 Classifiers with text 608 </a> 609 <div class="toc"></div> 610 611 612 613 </li> 614 615 <li class="menu-item"> 616 617 <a href="/classification/correcting-for-imbalanced-datasets/"> 618 Correcting for imbalanced datasets 619 </a> 620 <div class="toc"></div> 621 622 623 624 </li> 625 626 </ol> 627 </div> 628 629 </li> 630 631 <li class="menu-item"> 632 633 634 635 <input id="accordion-projects" type="checkbox" name="accordion-checkbox" hidden=""> 636 <label class="accordion-header c-hand" for="accordion-projects"> 637 <i class="icon icon-arrow-right mr-1"></i> 638 Projects 639 </label> 640 <div class="accordion-body"> 641 <ol class="menu menu-nav"> 642 643 <li class="menu-item"> 644 645 <a href="/buzzfeed-spy-planes/buzzfeed-surveillance-planes-random-forests/"> 646 BuzzFeed: Spy planes 647 </a> 648 <div class="toc"></div> 649 650 651 652 </li> 653 654 <li class="menu-item"> 655 656 <a href="/wapo-app-reviews/predict-reviews/"> 657 WaPo chat: App reviews 658 </a> 659 <div class="toc"></div> 660 661 662 663 </li> 664 665 <li class="menu-item"> 666 667 <a href="/nyt-takata-airbags/nyt-takata-completed/"> 668 NYT: Faulty airbag search 669 </a> 670 <div class="toc"></div> 671 672 673 674 </li> 675 676 <li class="menu-item"> 677 678 <a href="/latimes-crime-classification/using-a-classifier-to-find-misclassified-crimes/"> 679 LA Times: crime classifier 680 </a> 681 <div class="toc"></div> 682 683 684 685 </li> 686 687 </ol> 688 </div> 689 690 </li> 691 692 </ol> 693 694 <h4 id="howxaffectsy" class="sidebar-sticky"> 695 <a href="#howxaffectsy">How X affects Y</a> 696 </h4> 697 <ol class="menu menu-nav"> 698 699 <li class="menu-item"> 700 701 <a href="/regression/what-is-regression/"> 702 Finding relationships with regression 703 </a> 704 <div class="toc"></div> 705 706 707 708 </li> 709 710 <li class="menu-item"> 711 712 713 714 <input id="accordion-linearregression" type="checkbox" name="accordion-checkbox" hidden=""> 715 <label class="accordion-header c-hand" for="accordion-linearregression"> 716 <i class="icon icon-arrow-right mr-1"></i> 717 Linear Regression 718 </label> 719 <div class="accordion-body"> 720 <ol class="menu menu-nav"> 721 722 <li class="menu-item"> 723 724 <a href="/regression/linear-regression-quickstart/"> 725 Linear regression (Quickstart) 726 </a> 727 <div class="toc"></div> 728 729 730 731 </li> 732 733 <li class="menu-item"> 734 735 <a href="/regression/linear-regression/"> 736 Linear regression for humans 737 </a> 738 <div class="toc"></div> 739 740 741 742 </li> 743 744 <li class="menu-item"> 745 746 <a href="/regression/linear-regression-part-two/"> 747 Putting regression to use 748 </a> 749 <div class="toc"></div> 750 751 752 753 </li> 754 755 <li class="menu-item"> 756 757 <a href="/regression/linear-regression-evaluation/"> 758 Evaluating regressions 759 </a> 760 <div class="toc"></div> 761 762 763 764 </li> 765 766 <li class="menu-item"> 767 768 <a href="/ap-regression-unemployment/simple-regression-with-census-data-statsmodels-with-formulas/"> 769 Associated Press: Life expectancy and unemployment 770 </a> 771 <div class="toc"></div> 772 773 774 775 </li> 776 777 </ol> 778 </div> 779 780 </li> 781 782 <li class="menu-item"> 783 784 785 786 <input id="accordion-logisticregression" type="checkbox" name="accordion-checkbox" hidden=""> 787 <label class="accordion-header c-hand" for="accordion-logisticregression"> 788 <i class="icon icon-arrow-right mr-1"></i> 789 Logistic Regression 790 </label> 791 <div class="accordion-body"> 792 <ol class="menu menu-nav"> 793 794 <li class="menu-item"> 795 796 <a href="/regression/logistic-regression-quickstart/"> 797 Logistic regression (Quickstart) 798 </a> 799 <div class="toc"></div> 800 801 802 803 </li> 804 805 <li class="menu-item"> 806 807 <a href="/regression/logistic-regression/"> 808 Logistic regression for humans 809 </a> 810 <div class="toc"></div> 811 812 813 814 </li> 815 816 <li class="menu-item"> 817 818 <a href="/regression/logistic-regression-part-two/"> 819 More complex logistic regressions 820 </a> 821 <div class="toc"></div> 822 823 824 825 </li> 826 827 <li class="menu-item"> 828 829 <a href="/regression/evaluating-logistic-regressions/"> 830 Evaluating logistic regressions 831 </a> 832 <div class="toc"></div> 833 834 835 836 </li> 837 838 <li class="menu-item"> 839 840 <a href="/boston-globe-tickets/boston-globe-ticketing-regression/"> 841 Boston Globe: Speeding tickets 842 </a> 843 <div class="toc"></div> 844 845 846 847 </li> 848 849 <li class="menu-item"> 850 851 <a href="/apm-reports-jury-bias/in-the-dark-alternative-formula-methods/"> 852 APM Reports: Jury selection 853 </a> 854 <div class="toc"></div> 855 856 857 858 </li> 859 860 </ol> 861 </div> 862 863 </li> 864 865 </ol> 866 867 <h4 id="pythondatasciencereference" class="sidebar-sticky"> 868 <a href="#pythondatasciencereference">Python data science reference</a> 869 </h4> 870 <ol class="menu menu-nav"> 871 872 <li class="menu-item"> 873 874 <a href="/reference/"> 875 Introduction 876 </a> 877 <div class="toc"></div> 878 879 880 881 </li> 882 883 <li class="menu-item"> 884 885 <a href="/reference/vectorizing/"> 886 Vectorizing 887 </a> 888 <div class="toc"></div> 889 890 891 892 </li> 893 894 <li class="menu-item"> 895 896 <a href="/reference/text-analysis/"> 897 Text Analysis 898 </a> 899 <div class="toc"></div> 900 901 902 903 </li> 904 905 <li class="menu-item"> 906 907 <a href="/reference/regression/"> 908 Regression 909 </a> 910 <div class="toc"></div> 911 912 913 914 </li> 915 916 <li class="menu-item"> 917 918 <a href="/reference/classification/"> 919 Classification 920 </a> 921 <div class="toc"></div> 922 923 924 925 </li> 926 927 </ol> 928 929 <h4 id="allprojects" class="sidebar-sticky"> 930 <a href="#allprojects">All Projects</a> 931 </h4> 932 <ol class="menu menu-nav"> 933 934 <li class="menu-item"> 935 936 <a href="/projects/"> 937 Project Summaries 938 </a> 939 <div class="toc"></div> 940 941 942 943 </li> 944 945 <li class="menu-item"> 946 947 948 949 <input id="accordion-strongnytstrongtakataairbagsearch" type="checkbox" name="accordion-checkbox" hidden=""> 950 <label class="accordion-header c-hand" for="accordion-strongnytstrongtakataairbagsearch"> 951 <i class="icon icon-arrow-right mr-1"></i> 952 <strong>NYT:</strong> Takata airbag search 953 </label> 954 <div class="accordion-body"> 955 <ol class="menu menu-nav"> 956 957 <li class="menu-item"> 958 959 <a href="/nyt-takata-airbags/"> 960 Summary 961 </a> 962 <div class="toc"></div> 963 964 965 966 </li> 967 968 <li class="menu-item"> 969 970 <a href="/nyt-takata-airbags/airbag-classifier-search-binary/"> 971 A simplistic reproduction of the NYT's research using logistic regression 972 </a> 973 <div class="toc"></div> 974 975 976 977 </li> 978 979 <li class="menu-item"> 980 981 <a href="/nyt-takata-airbags/airbag-classifier-search-decision-tree/"> 982 A decision-tree reproduction of the NYT's research 983 </a> 984 <div class="toc"></div> 985 986 987 988 </li> 989 990 <li class="menu-item"> 991 992 <a href="/nyt-takata-airbags/airbag-classifier-search-countvectorizer/"> 993 Combining a text vectorizer and a classifier to track down suspicious complaints 994 </a> 995 <div class="toc"></div> 996 997 998 999 </li> 1000 1001 </ol> 1002 </div> 1003 1004 </li> 1005 1006 <li class="menu-item"> 1007 1008 1009 1010 <input id="accordion-stronglatimesstrongcrimeclassification" type="checkbox" name="accordion-checkbox" hidden=""> 1011 <label class="accordion-header c-hand" for="accordion-stronglatimesstrongcrimeclassification"> 1012 <i class="icon icon-arrow-right mr-1"></i> 1013 <strong>LA Times:</strong> Crime classification 1014 </label> 1015 <div class="accordion-body"> 1016 <ol class="menu menu-nav"> 1017 1018 <li class="menu-item"> 1019 1020 <a href="/latimes-crime-classification/"> 1021 Summary 1022 </a> 1023 <div class="toc"></div> 1024 1025 1026 1027 </li> 1028 1029 <li class="menu-item"> 1030 1031 <a href="/latimes-crime-classification/using-a-classifier-to-find-misclassified-crimes/"> 1032 Predicting downgraded assaults with machine learning 1033 </a> 1034 <div class="toc"></div> 1035 1036 1037 1038 </li> 1039 1040 <li class="menu-item"> 1041 1042 <a href="/latimes-crime-classification/inspecting-classifications/"> 1043 Taking a closer look at our classifier and its misclassifications 1044 </a> 1045 <div class="toc"></div> 1046 1047 1048 1049 </li> 1050 1051 <li class="menu-item"> 1052 1053 <a href="/latimes-crime-classification/trying-out-different-classifiers/"> 1054 Trying out and combining different classifiers 1055 </a> 1056 <div class="toc"></div> 1057 1058 1059 1060 </li> 1061 1062 </ol> 1063 </div> 1064 1065 </li> 1066 1067 <li class="menu-item"> 1068 1069 1070 1071 <input id="accordion-strongcaixinstrongmuseumnames" type="checkbox" name="accordion-checkbox" hidden=""> 1072 <label class="accordion-header c-hand" for="accordion-strongcaixinstrongmuseumnames"> 1073 <i class="icon icon-arrow-right mr-1"></i> 1074 <strong>Caixin:</strong> Museum names 1075 </label> 1076 <div class="accordion-body"> 1077 <ol class="menu menu-nav"> 1078 1079 <li class="menu-item"> 1080 1081 <a href="/caixin-museum-word-count/"> 1082 Summary 1083 </a> 1084 <div class="toc"></div> 1085 1086 1087 1088 </li> 1089 1090 <li class="menu-item"> 1091 1092 <a href="/caixin-museum-word-count/chinese-museum-dataset-cleanup/"> 1093 Chinese museum dataset cleanup 1094 </a> 1095 <div class="toc"></div> 1096 1097 1098 1099 </li> 1100 1101 <li class="menu-item"> 1102 1103 <a href="/caixin-museum-word-count/chinese-museums-per-capita-analysis/"> 1104 Chinese museums per capita analysis 1105 </a> 1106 <div class="toc"></div> 1107 1108 1109 1110 </li> 1111 1112 <li class="menu-item"> 1113 1114 <a href="/caixin-museum-word-count/counting-words-in-chinese-museum-names/"> 1115 Counting words in Chinese museum names 1116 </a> 1117 <div class="toc"></div> 1118 1119 1120 1121 </li> 1122 1123 </ol> 1124 </div> 1125 1126 </li> 1127 1128 <li class="menu-item"> 1129 1130 1131 1132 <input id="accordion-strongwapostrongrandomchatappsafety" type="checkbox" name="accordion-checkbox" hidden=""> 1133 <label class="accordion-header c-hand" for="accordion-strongwapostrongrandomchatappsafety"> 1134 <i class="icon icon-arrow-right mr-1"></i> 1135 <strong>WaPo:</strong> Random chat app safety 1136 </label> 1137 <div class="accordion-body"> 1138 <ol class="menu menu-nav"> 1139 1140 <li class="menu-item"> 1141 1142 <a href="/wapo-app-reviews/"> 1143 Summary 1144 </a> 1145 <div class="toc"></div> 1146 1147 1148 1149 </li> 1150 1151 <li class="menu-item"> 1152 1153 <a href="/wapo-app-reviews/scrape-app-store-reviews/"> 1154 Scrape and combine app store reviews 1155 </a> 1156 <div class="toc"></div> 1157 1158 1159 1160 </li> 1161 1162 <li class="menu-item"> 1163 1164 <a href="/wapo-app-reviews/predict-reviews/"> 1165 Build a classifier to detect reviews about bad behavior 1166 </a> 1167 <div class="toc"></div> 1168 1169 1170 1171 </li> 1172 1173 </ol> 1174 </div> 1175 1176 </li> 1177 1178 <li class="menu-item"> 1179 1180 <a href="/ajc-doctors-abuse/"> 1181 <strong>AJC:</strong> Doctors and sex abuse 1182 </a> 1183 <div class="toc"></div> 1184 1185 1186 1187 </li> 1188 1189 <li class="menu-item"> 1190 1191 1192 1193 <input id="accordion-strongtheupshotstrongtrumpspeeches" type="checkbox" name="accordion-checkbox" hidden=""> 1194 <label class="accordion-header c-hand" for="accordion-strongtheupshotstrongtrumpspeeches"> 1195 <i class="icon icon-arrow-right mr-1"></i> 1196 <strong>The UpShot:</strong> Trump speeches 1197 </label> 1198 <div class="accordion-body"> 1199 <ol class="menu menu-nav"> 1200 1201 <li class="menu-item"> 1202 1203 <a href="/upshot-trump-emolex/"> 1204 Summary 1205 </a> 1206 <div class="toc"></div> 1207 1208 1209 1210 </li> 1211 1212 <li class="menu-item"> 1213 1214 <a href="/upshot-trump-emolex/nrc-emotional-lexicon/"> 1215 An introduction to the NRC Emotional Lexicon 1216 </a> 1217 <div class="toc"></div> 1218 1219 1220 1221 </li> 1222 1223 <li class="menu-item"> 1224 1225 <a href="/upshot-trump-emolex/trump-vs-state-of-the-union-addresses/"> 1226 Reproducing The UpShot's Trump State of the Union visualization 1227 </a> 1228 <div class="toc"></div> 1229 1230 1231 1232 </li> 1233 1234 </ol> 1235 </div> 1236 1237 </li> 1238 1239 <li class="menu-item"> 1240 1241 1242 1243 <input id="accordion-strongusatodaystrongmodellegislation" type="checkbox" name="accordion-checkbox" hidden=""> 1244 <label class="accordion-header c-hand" for="accordion-strongusatodaystrongmodellegislation"> 1245 <i class="icon icon-arrow-right mr-1"></i> 1246 <strong>USA Today:</strong> Model legislation 1247 </label> 1248 <div class="accordion-body"> 1249 <ol class="menu menu-nav"> 1250 1251 <li class="menu-item"> 1252 1253 <a href="/azcentral-text-reuse-model-legislation/"> 1254 Summary 1255 </a> 1256 <div class="toc"></div> 1257 1258 1259 1260 </li> 1261 1262 <li class="menu-item"> 1263 1264 <a href="/azcentral-text-reuse-model-legislation/01-downloading-one-million-pieces-of-legislation-from-legiscan/"> 1265 Downloading one million pieces of legislation from LegiScan 1266 </a> 1267 <div class="toc"></div> 1268 1269 1270 1271 </li> 1272 1273 <li class="menu-item"> 1274 1275 <a href="/azcentral-text-reuse-model-legislation/02-taking-a-mill
1275ion-pieces-of-legislation-from-a-csv-and-inserting-them-into-postgres/"> 1276 Taking a million pieces of legislation from a CSV and inserting them into Postgres 1277 </a> 1278 <div class="toc"></div> 1279 1280 1281 1282 </li> 1283 1284 <li class="menu-item"> 1285 1286 <a href="/azcentral-text-reuse-model-legislation/03-download-word-pdf-and-html-content-and-process-it-into-text-with-tika/"> 1287 Download Word, PDF and HTML content and process it into text with Tika 1288 </a> 1289 <div class="toc"></div> 1290 1291 1292 1293 </li> 1294 1295 <li class="menu-item"> 1296 1297 <a href="/azcentral-text-reuse-model-legislation/04-import-content-into-solr-for-advanced-text-searching/"> 1298 Import content into Solr for advanced text searching 1299 </a> 1300 <div class="toc"></div> 1301 1302 1303 1304 </li> 1305 1306 <li class="menu-item"> 1307 1308 <a href="/azcentral-text-reuse-model-legislation/05-checking-for-legislative-text-reuse-using-python-solr-and-ngrams/"> 1309 Checking for legislative text reuse using Python, Solr, and ngrams 1310 </a> 1311 <div class="toc"></div> 1312 1313 1314 1315 </li> 1316 1317 <li class="menu-item"> 1318 1319 <a href="/azcentral-text-reuse-model-legislation/05-checking-for-legislative-text-reuse-using-python-solr-and-simple-text-search/"> 1320 Checking for legislative text reuse using Python, Solr, and simple text search 1321 </a> 1322 <div class="toc"></div> 1323 1324 1325 1326 </li> 1327 1328 <li class="menu-item"> 1329 1330 <a href="/azcentral-text-reuse-model-legislation/06-search-for-model-legislation-in-over-one-million-bills-using-postgres-and-solr/"> 1331 Search for model legislation in over one million bills using Postgres and Solr 1332 </a> 1333 <div class="toc"></div> 1334 1335 1336 1337 </li> 1338 1339 <li class="menu-item"> 1340 1341 <a href="/azcentral-text-reuse-model-legislation/using-topic-modeling-to-categorize-legislation/"> 1342 Using topic modeling to categorize legislation 1343 </a> 1344 <div class="toc"></div> 1345 1346 1347 1348 </li> 1349 1350 </ol> 1351 </div> 1352 1353 </li> 1354 1355 <li class="menu-item"> 1356 1357 <a href="/fcc-comments/"> 1358 FCC comment bots 1359 </a> 1360 <div class="toc"></div> 1361 1362 1363 1364 </li> 1365 1366 <li class="menu-item"> 1367 1368 1369 1370 <input id="accordion-strongbloombergstrongdemocraticcandidatetweets" type="checkbox" name="accordion-checkbox" hidden=""> 1371 <label class="accordion-header c-hand" for="accordion-strongbloombergstrongdemocraticcandidatetweets"> 1372 <i class="icon icon-arrow-right mr-1"></i> 1373 <strong>Bloomberg:</strong> Democratic Candidate Tweets 1374 </label> 1375 <div class="accordion-body"> 1376 <ol class="menu menu-nav"> 1377 1378 <li class="menu-item"> 1379 1380 <a href="/bloomberg-tweet-topics/"> 1381 Summary 1382 </a> 1383 <div class="toc"></div> 1384 1385 1386 1387 </li> 1388 1389 <li class="menu-item"> 1390 1391 <a href="/bloomberg-tweet-topics/scrape-tweets-from-presidential-primary-candidates/"> 1392 Downloading all 2019 tweets from Democratic presidential candidates 1393 </a> 1394 <div class="toc"></div> 1395 1396 1397 1398 </li> 1399 1400 <li class="menu-item"> 1401 1402 <a href="/bloomberg-tweet-topics/topic-modeling-for-tweets/"> 1403 Using topic modeling to analyze presidential candidate tweets 1404 </a> 1405 <div class="toc"></div> 1406 1407 1408 1409 </li> 1410 1411 <li class="menu-item"> 1412 1413 <a href="/bloomberg-tweet-topics/assigning-categories-to-text-using-keyword-matching/"> 1414 Assigning categories to tweets using keyword matching 1415 </a> 1416 <div class="toc"></div> 1417 1418 1419 1420 </li> 1421 1422 <li class="menu-item"> 1423 1424 <a href="/bloomberg-tweet-topics/building-streamgraphs-from-candidate-tweets/"> 1425 Building streamgraphs from categorized and dated datasets 1426 </a> 1427 <div class="toc"></div> 1428 1429 1430 1431 </li> 1432 1433 </ol> 1434 </div> 1435 1436 </li> 1437 1438 <li class="menu-item"> 1439 1440 <a href="/nyt-trump-tweets/"> 1441 <strong>NYT:</strong> Trump tweets 1442 </a> 1443 <div class="toc"></div> 1444 1445 1446 1447 </li> 1448 1449 <li class="menu-item"> 1450 1451 1452 1453 <input id="accordion-strongapstronglifeexpectancy" type="checkbox" name="accordion-checkbox" hidden=""> 1454 <label class="accordion-header c-hand" for="accordion-strongapstronglifeexpectancy"> 1455 <i class="icon icon-arrow-right mr-1"></i> 1456 <strong>AP:</strong> Life expectancy 1457 </label> 1458 <div class="accordion-body"> 1459 <ol class="menu menu-nav"> 1460 1461 <li class="menu-item"> 1462 1463 <a href="/ap-regression-unemployment/"> 1464 Summary 1465 </a> 1466 <div class="toc"></div> 1467 1468 1469 1470 </li> 1471 1472 <li class="menu-item"> 1473 1474 <a href="/ap-regression-unemployment/simple-regression-with-census-data-statsmodels-with-formulas/"> 1475 Simple logistic regression using statsmodels (formula version) 1476 </a> 1477 <div class="toc"></div> 1478 1479 1480 1481 </li> 1482 1483 <li class="menu-item"> 1484 1485 <a href="/ap-regression-unemployment/simple-regression-with-census-data-statsmodels-with-dataframes/"> 1486 Simple logistic regression using statsmodels (dataframes version) 1487 </a> 1488 <div class="toc"></div> 1489 1490 1491 1492 </li> 1493 1494 </ol> 1495 </div> 1496 1497 </li> 1498 1499 <li class="menu-item"> 1500
1501 <a href="/fivethirtyeight-p-hacking/"> 1502 <strong>FiveThirtyEight:</strong> P-values 1503 </a> 1504 <div class="toc"></div> 1505 1506 1507 1508 </li> 1509 1510 <li class="menu-item"> 1511 1512 1513 1514 <input id="accordion-strongmilwaukeejournalsentinelstrongpotholes" type="checkbox" name="accordion-checkbox" hidden=""> 1515 <label class="accordion-header c-hand" for="accordion-strongmilwaukeejournalsentinelstrongpotholes"> 1516 <i class="icon icon-arrow-right mr-1"></i> 1517 <strong>Milwaukee Journal-Sentinel:</strong> Potholes 1518 </label> 1519 <div class="accordion-body"> 1520 <ol class="menu menu-nav"> 1521 1522 <li class="menu-item"> 1523 1524 <a href="/milwaukee-potholes/"> 1525 Summary 1526 </a> 1527 <div class="toc"></div> 1528 1529 1530 1531 </li> 1532 1533 <li class="menu-item"> 1534 1535 <a href="/milwaukee-potholes/milwaukee-journal-sentinel-and-potholes-full-walkthrough/"> 1536 Pothole geographic analysis and linear regression, complete walkthrough 1537 </a> 1538 <div class="toc"></div> 1539 1540 1541 1542 </li> 1543 1544 <li class="menu-item"> 1545 1546 <a href="/milwaukee-potholes/milwaukee-journal-sentinel-and-potholes-without-merging/"> 1547 Pothole demographics linear regression, no spatial analysis 1548 </a> 1549 <div class="toc"></div> 1550 1551 1552 1553 </li> 1554 1555 </ol> 1556 </div> 1557 1558 </li> 1559 1560 <li class="menu-item"> 1561 1562 1563 1564 <input id="accordion-strongdallasmorningnewsstrongcheatingschools" type="checkbox" name="accordion-checkbox" hidden=""> 1565 <label class="accordion-header c-hand" for="accordion-strongdallasmorningnewsstrongcheatingschools"> 1566 <i class="icon icon-arrow-right mr-1"></i> 1567 <strong>Dallas Morning News:</strong> Cheating schools 1568 </label> 1569 <div class="accordion-body"> 1570 <ol class="menu menu-nav"> 1571 1572 <li class="menu-item"> 1573 1574 <a href="/dmn-texas-school-cheating/"> 1575 Summary 1576 </a> 1577 <div class="toc"></div> 1578 1579 1580 1581 </li> 1582 1583 <li class="menu-item"> 1584 1585 <a href="/dmn-texas-school-cheating/texas-school-cheating-finding-outliers-with-standard-deviation-and-regression/"> 1586 Finding outliers with standard deviation and regression 1587 </a> 1588 <div class="toc"></div> 1589 1590 1591 1592 </li> 1593 1594 <li class="menu-item"> 1595 1596 <a href="/dmn-texas-school-cheating/texas-school-cheating-finding-outliers-with-regression-residuals/"> 1597 Finding outliers with regression residuals (short version) 1598 </a> 1599 <div class="toc"></div> 1600 1601 1602 1603 </li> 1604 1605 <li class="menu-item"> 1606 1607 <a href="/dmn-texas-school-cheating/texas-school-cheating-graph-reproductions/"> 1608 Reproducing the graphics from The Dallas Morning News piece 1609 </a> 1610 <div class="toc"></div> 1611 1612 1613 1614 </li> 1615 1616 </ol> 1617 </div> 1618 1619 </li> 1620 1621 <li class="menu-item"> 1622 1623 1624 1625 <input id="accordion-strongtampabaytimesstrongfailurefactories" type="checkbox" name="accordion-checkbox" hidden=""> 1626 <label class="accordion-header c-hand" for="accordion-strongtampabaytimesstrongfailurefactories"> 1627 <i class="icon icon-arrow-right mr-1"></i> 1628 <strong>Tampa Bay Times:</strong> Failure factories 1629 </label> 1630 <div class="accordion-body"> 1631 <ol class="menu menu-nav"> 1632 1633 <li class="menu-item"> 1634 1635 <a href="/tampa-bay-times-schools/"> 1636 Summary 1637 </a> 1638 <div class="toc"></div> 1639 1640 1641 1642 </li> 1643 1644 <li class="menu-item"> 1645 1646 <a href="/tampa-bay-times-schools/linear-regression-on-florida-schools/"> 1647 Linear regression on Florida schools, complete walkthrough 1648 </a> 1649 <div class="toc"></div> 1650 1651 1652 1653 </li> 1654 1655 <li class="menu-item"> 1656 1657 <a href="/tampa-bay-times-schools/linear-regression-on-florida-schools-no-cleaning/"> 1658 Linear regression on Florida schools, no cleaning 1659 </a> 1660 <div class="toc"></div> 1661 1662 1663 1664 </li> 1665 1666 </ol> 1667 </div> 1668 1669 </li> 1670 1671 <li class="menu-item"> 1672 1673 1674 1675 <input id="accordion-caraccidentsandcarweight" type="checkbox" name="accordion-checkbox" hidden=""> 1676 <label class="accordion-header c-hand" for="accordion-caraccidentsandcarweight"> 1677 <i class="icon icon-arrow-right mr-1"></i> 1678 Car accidents and car weight 1679 </label> 1680 <div class="accordion-body"> 1681 <ol class="menu menu-nav"> 1682 1683 <li class="menu-item"> 1684 1685 <a href="/car-crashes-weight-regression/"> 1686 Summary 1687 </a> 1688 <div class="toc"></div> 1689 1690 1691 1692 </li> 1693 1694 <li class="menu-item"> 1695 1696 <a href="/car-crashes-weight-regression/car-crashes-feature-selection-and-engineering/"> 1697 Feature selection and engineering
1698 </a> 1699 <div class="toc"></div> 1700 1701 1702 1703 </li> 1704 1705 <li class="menu-item"> 1706 1707 <a href="/car-crashes-weight-regression/01-combine-excel-files-across-multiple-sheets-and-save-as-csv-files/"> 1708 Combine Excel files across multiple sheets and save as CSV files 1709 </a> 1710 <div class="toc"></div> 1711 1712 1713 1714 </li> 1715 1716 <li class="menu-item"> 1717 1718 <a href="/car-crashes-weight-regression/02-create-make-model-weights-csv/"> 1719 Create make model weights csv 1720 </a> 1721 <div class="toc"></div> 1722 1723 1724 1725 </li> 1726 1727 <li class="menu-item"> 1728 1729 <a href="/car-crashes-weight-regression/03-find-car-data-from-vins/"> 1730 Find car data from VINs 1731 </a> 1732 <div class="toc"></div> 1733 1734 1735 1736 </li> 1737 1738 <li class="menu-item"> 1739 1740 <a href="/car-crashes-weight-regression/04-combine-vins-and-weights/"> 1741 Combine VINs and weights 1742 </a> 1743 <div class="toc"></div> 1744 1745 1746 1747 </li> 1748 1749 <li class="menu-item"> 1750 1751 <a href="/car-crashes-weight-regression/05-clean-combine-and-filter-data/"> 1752 Clean combine and filter data 1753 </a> 1754 <div class="toc"></div> 1755 1756 1757 1758 </li> 1759 1760 </ol> 1761 </div> 1762 1763 </li> 1764 1765 <li class="menu-item"> 1766 1767 <a href="/propublica-opportunity-gap/"> 1768 <strong>ProPublica:</strong> Opportunity Gap 1769 </a> 1770 <div class="toc"></div> 1771 1772 1773 1774 </li> 1775 1776 <li class="menu-item"> 1777 1778 1779 1780 <input id="accordion-strongbostonglobestrongticketingbias" type="checkbox" name="accordion-checkbox" hidden=""> 1781 <label class="accordion-header c-hand" for="accordion-strongbostonglobestrongticketingbias"> 1782 <i class="icon icon-arrow-right mr-1"></i> 1783 <strong>Boston Globe:</strong> Ticketing bias 1784 </label> 1785 <div class="accordion-body"> 1786 <ol class="menu menu-nav"> 1787 1788 <li class="menu-item"> 1789 1790 <a href="/boston-globe-tickets/"> 1791 Summary 1792 </a> 1793 <div class="toc"></div> 1794 1795 1796 1797 </li> 1798 1799 <li class="menu-item"> 1800 1801 <a href="/boston-globe-tickets/boston-globe-ticketing-regression/"> 1802 Logistic regression for speeding tickets 1803 </a> 1804 <div class="toc"></div> 1805 1806 1807 1808 </li> 1809 1810 </ol> 1811 </div> 1812 1813 </li> 1814 1815 <li class="menu-item"> 1816 1817 <a href="/stanford-open-policing/"> 1818 <strong>Stanford:</strong> Open Policing Data 1819 </a> 1820 <div class="toc"></div> 1821 1822 1823 1824 </li> 1825 1826 <li class="menu-item"> 1827 1828 1829 1830 <input id="accordion-strongbuzzfeedstrongsurveillanceplanes" type="checkbox" name="accordion-checkbox" hidden=""> 1831 <label class="accordion-header c-hand" for="accordion-strongbuzzfeedstrongsurveillanceplanes"> 1832 <i class="icon icon-arrow-right mr-1"></i> 1833 <strong>BuzzFeed:</strong> Surveillance planes 1834 </label> 1835 <div class="accordion-body"> 1836 <ol class="menu menu-nav"> 1837 1838 <li class="menu-item"> 1839 1840 <a href="/buzzfeed-spy-planes/"> 1841 Summary 1842 </a> 1843 <div class="toc"></div> 1844 1845 1846 1847 </li> 1848 1849 <li class="menu-item"> 1850 1851 <a href="/buzzfeed-spy-planes/feature-engineering-buzzfeed-spy-planes/"> 1852 Feature engineering - BuzzFeed spy planes 1853 </a> 1854 <div class="toc"></div> 1855 1856 1857 1858 </li> 1859 1860 <li class="menu-item"> 1861 1862 <a href="/buzzfeed-spy-planes/drawing-flight-paths-on-maps-with-cartopy/"> 1863 Drawing flight paths on maps with cartopy 1864 </a> 1865 <div class="toc"></div> 1866 1867 1868 1869 </li> 1870 1871 <li class="menu-item"> 1872 1873 <a href="/buzzfeed-spy-planes/buzzfeed-surveillance-planes-random-forests/"> 1874 Finding surveillance planes using random forests 1875 </a> 1876 <div class="toc"></div> 1877 1878 1879 1880 </li> 1881 1882 </ol> 1883 </div> 1884 1885 </li> 1886 1887 <li class="menu-item"> 1888 1889 1890 1891 <input id="accordion-strongrevealstrongmortgagelendingbias" type="checkbox" name="accordion-checkbox" hidden=""> 1892 <label class="accordion-header c-hand" for="accordion-strongrevealstrongmortgagelendingbias"> 1893 <i class="icon icon-arrow-right mr-1"></i> 1894 <strong>Reveal:</strong> Mortgage lending bias 1895 </label> 1896 <div class="accordion-body"> 1897 <ol class="menu menu-nav"> 1898 1899 <li class="menu-item"> 1900 1901 <a href="/reveal-mortgages/"> 1902 Summary 1903 </a> 1904 <div class="toc"></div> 1905 1906 1907 1908 </li> 1909 1910 <li class="menu-item"> 1911 1912 <a href="/reveal-mortgages/reveal-mortgage-analysis-cleaning-and-combining-data/"> 1913 Cleaning and combining data for the Reveal Mortgage Analysis 1914 </a> 1915 <div class="toc"></div> 1916 1917 1918 1919 </li> 1920 1921 <li class="menu-item"> 1922 1923 <a href="/reveal-mortgages/reveal-mortgage-analysis-wild-formulas-in-statsmodels-using-patsy-short-version/"> 1924 Wild formulas in statsmodels using Patsy (short version) 1925 </a> 1926 <div class="toc"></div> 1927 1928 1929 1930 </li> 1931 1932 <li class="menu-item"> 1933 1934 <a href="/reveal-mortgages/reveal-mortgage-analysis-logistic-regression-using-statsmodels-formulas/"> 1935 Reveal Mortgage Analysis - Logistic Regression using statsmodels formulas 1936 </a> 1937 <div class="toc"></div> 1938 1939 1940 1941 </li> 1942 1943 <li class="menu-item"> 1944 1945 <a href="/reveal-mortgages/reveal-mortgage-analysis-logistic-regression/"> 1946 Reveal Mortgage Analysis - Logistic Regression 1947 </a> 1948 <div class="toc"></div> 1949 1950 1951 1952 </li> 1953 1954 </ol> 1955 </div> 1956 1957 </li> 1958 1959 <li class="menu-item"> 1960 1961 1962 1963 <input id="accordion-strongapmreportsstrongjuryselectionbias" type="checkbox" name="accordion-checkbox" hidden=""> 1964 <label class="accordion-header c-hand" for="accordion-strongapmreportsstrongjuryselectionbias"> 1965 <i class="icon icon-arrow-right mr-1"></i> 1966 <strong>APM Reports:</strong> Jury selection bias 1967 </label> 1968 <div class="accordion-body"> 1969 <ol class="menu menu-nav"> 1970 1971 <li class="menu-item"> 1972 1973 <a href="/apm-reports-jury-bias/"> 1974 Summary 1975 </a> 1976 <div class="toc"></div> 1977 1978 1979 1980 </li> 1981 1982 <li class="menu-item"> 1983 1984 <a href="/apm-reports-jury-bias/in-the-dark-combining-datasets-and-cleaning-the-data/"> 1985 Combining and cleaning the initial dataset 1986 </a> 1987 <div class="toc"></div> 1988 1989 1990 1991 </li> 1992 1993 <li class="menu-item"> 1994 1995 <a href="/apm-reports-jury-bias/in-the-dark-feature-selection-with-p-values/"> 1996 Picking what matters and what doesn't in a regression 1997 </a> 1998 <div class="toc"></div> 1999 2000 2001 2002 </li> 2003 2004 <li class="menu-item"> 2005 2006 <a href="/apm-reports-jury-bias/in-the-dark-jury-selection-regression-walkthrough/"> 2007 Analyzing data using statsmodels formulas 2008 </a> 2009 <div class="toc"></div> 2010 2011 2012 2013 </li> 2014 2015 <li class="menu-item"> 2016 2017 <a href="/apm-reports-jury-bias/in-the-dark-alternative-formula-methods/"> 2018 Alternative techniques with statsmodels formulas 2019 </a> 2020 <div class="toc"></div> 2021 2022 2023 2024 </li> 2025 2026 </ol> 2027 </div> 2028 2029 </li> 2030 2031 <li class="menu-item"> 2032 2033 2034 2035 <input id="accordion-strongreutersstrongasylumdenials" type="checkbox" name="accordion-checkbox" hidden=""> 2036 <label class="accordion-header c-hand" for="accordion-strongreutersstrongasylumdenials"> 2037 <i class="icon icon-arrow-right mr-1"></i> 2038 <strong>Reuters:</strong> Asylum denials 2039 </label> 2040 <div class="accordion-body"> 2041 <ol class="menu menu-nav"> 2042 2043 <li class="menu-item"> 2044 2045 <a href="/reuters-asylum/"> 2046 Summary 2047 </a> 2048 <div class="toc"></div> 2049 2050 2051 2052 </li> 2053 2054 <li class="menu-item"> 2055 2056 <a href="/reuters-asylum/cleaning-the-eoir-immigration-court-dataset/"> 2057 Preparing the EOIR immigration court data for analysis 2058 </a> 2059 <div class="toc"></div> 2060 2061 2062 2063 </li> 2064 2065 <li class="menu-item"> 2066 2067 <a href="/reuters-asylum/using-regression-to-analyze-asylum-cases/"> 2068 How nationality and judges affect your chance of asylum in immigration court 2069 </a> 2070 <div class="toc"></div> 2071 2072 2073 2074 </li> 2075 2076 </ol> 2077 </div> 2078 2079 </li> 2080 2081 <li class="menu-item"> 2082 2083 <a href="/propublica-pardons/"> 2084 <strong>ProPublica:</strong> Presidential pardons 2085 </a> 2086 <div class="toc"></div> 2087 2088 2089 2090 </li> 2091 2092 <li class="menu-item"> 2093 2094 2095 2096 <input id="accordion-strongpropublicastrongcriminalsentencing" t
2096ype="checkbox" name="accordion-checkbox" hidden=""> 2097 <label class="accordion-header c-hand" for="accordion-strongpropublicastrongcriminalsentencing"> 2098 <i class="icon icon-arrow-right mr-1"></i> 2099 <strong>ProPublica:</strong> Criminal sentencing 2100 </label> 2101 <div class="accordion-body"> 2102 <ol class="menu menu-nav"> 2103 2104 <li class="menu-item"> 2105 2106 <a href="/propublica-criminal-sentencing/"> 2107 Summary 2108 </a> 2109 <div class="toc"></div> 2110 2111 2112 2113 </li> 2114 2115 <li class="menu-item"> 2116 2117 <a href="/propublica-criminal-sentencing/week-5-1-machine-bias-class/"> 2118 Breaking down machine bias 2119 </a> 2120 <div class="toc"></div> 2121 2122 2123 2124 </li> 2125 2126 </ol> 2127 </div> 2128 2129 </li> 2130 2131 <li class="menu-item"> 2132 2133 <a href="/foia-predictor/"> 2134 <strong>data.world:</strong> The FOIA Predictor 2135 </a> 2136 <div class="toc"></div> 2137 2138 2139 2140 </li> 2141 2142 </ol> 2143 2144 </div> 2145 </div> 2146</div> 2147 </div> 2148 </div> 2149 </div> 2150
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