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31 32 33 <meta name="viewport" content="width=device-width, initial-scale=1"> 34 <link href="/css/style.css?v=10420579fc48bad7c182d7d5e44a2cbb" rel="stylesheet"> 35 <link href="/css/highlight.css?v=bdd372d828c6988b6071bb877211ebcb" rel="stylesheet"> 36 37 <meta name="description" content="The best resource for advanced Python data journalism, from statistics to machine learning and data science. Lessons, tutorials, reference code and more."> 38 <meta content="investigate.ai: Data Science for Journalists" property="og:site_name"> 39 40 <meta name="twitter:creator" content="@dangerscarf"> 41 <meta name="twitter:title" content="investigate.ai: Data Science for Journalism"> 42 <meta name="twitter:description" content="The best resource for advanced Python data journalism, from statistics to machine learning and data science. Lessons, tutorials, reference code and more."> 43 <meta name="twitter:image" content="https://investigate.ai/images/flame/flame-searching.png"> 44 <meta name="twitter:image:alt" content="A person metaphorically examining data"> 45 <meta name="twitter:card" content="summary" /> 46 47 <meta property="og:type" content="website" /> 48 <meta property="og:url" content="https://investigate.ai/" /> 49 <meta property="og:title" content="investigate.ai: Data Science for Journalism" /> 50 <meta property="og:description" content="The best resource for advanced Python data journalism, from statistics to machine learning and data science. Lessons, tutorials, reference code and more." /> 51 <meta property="og:image" content="https://investigate.ai/images/flame/flame-searching.png" /> 52 <meta property="og:image:height" content="912" /> 53 <meta property="og:image:width" content="921" /> 54 <meta property="fb:admins" content="1504164"/> 55 56 <link rel="apple-touch-icon" sizes="180x180" href="/apple-touch-icon.png"> 57 <link rel="icon" type="image/png" sizes="32x32" href="/favicon-32x32.png"> 58 <link rel="icon" type="image/png" sizes="16x16" href="/favicon-16x16.png"> 59 <link rel="manifest" href="/site.webmanifest"> 60 <link rel="mask-icon" href="/safari-pinned-tab.svg" color="#5bbad5"> 61 <meta name="msapplication-TileColor" content="#da532c"> 62 <meta name="theme-color" content="#ffffff"> 63 64</head> 65 66<body class="sidebar-closed"> 67 <div> 68 <div class="content-main"> 69 <div class="nav-holder bg-secondary"> 70 <div class='content'> 71 <header class="navbar"> 72 <a href="#" class="sidebar-opener" aria-label="Sidebar opener"><i class="fas fa-bars fa-2x"></i></a> 73 <section class="navbar-section"> 74 <a href="/" class="btn btn-link">Home</a> 75 <a href="/projects/" class="btn btn-link">Projects</a> 76 <a href="/topics/" class="btn btn-link">Topics</a> 77 <a href="/search/" class="btn btn-link">Search</a> 78 <a href="/newsletter/" class="btn btn-link">Newsletter</a> 79 <a href="/about/" class="btn btn-link">About</a> 80 </section> 81 </header> 82 </div> 83</div> 84 85<div class="content section-top"> 86 <div class="columns callout"> 87 <div class="column col-sm-auto col-5"> 88 <img alt="eye examining planet" style="padding: 2rem;" src="/images/flame/flame-searching.png"> 89 </div> 90 <div class="column col-sm-auto col-7"> 91 <h1>Practical data science for journalists (and everyone else)</h1> 92 <p>If you know some Python and have dabbled in data, we're here for you! 93 Let's add a dash of machine learning and a sprinkling of stats to your skillset.</p> 94 <p><small>And if you don't know Python, <a href="http://littlecolumns.com/learn/python/">take this</a> and <a href="http://littlecolumns.com/tools/python-wrangler/">this</a> and call me in the morning</small>. Or <a href="http://ledeprogram.com/">go all-in</a>, maybe?</p> 95 </div> 96 </div> 97 <div class="columns"> 98 <div class="column col-sm-auto col-4"> 99 <h3>Topic walkthroughs</h3> 100 <p>Practical, start-to-finish guides on data science concepts and tools. 101 Not (too) boring, not (too) mathy, they're hopefully just what you're looking for.</p> 102 <p><a class="btn btn-primary" href="/topics/">
102See our topics guide</a></p> 103 </div> 104 <div class="column col-sm-auto col-4"> 105 <h3>Real-life examples</h3> 106 <p>Theory on its own doesn't do much! Practice your skills by reproducing 107 published, award-winning investigations.</p> 108 <p><small>(The ones that didn't win "real" awards win an award called "I think this project is pretty neat")</small></p> 109 <p><a class="btn btn-primary" href="/projects/">See our projects page</a></p> 110 </div> 111 <div class="column col-sm-auto col-4"> 112 <h3>Reference materials</h3> 113 <p>Most of the time we spend "programming" is finding things to cut and paste from the internet. Might as well put it all in one place, right? Somewhat-organized snippets to make our days go faster.</p> 114 <p><a class="btn btn-primary" href="/reference/">Check our references</a></p> 115 </div> 116 </div> 117</div> 118 119<div class="divider"></div> 120 121<div class="content section-topics"> 122 <div class="columns"> 123 <div class="column col-sm-auto col-8 col-mx-auto text-center"> 124 <h3>Topics we cover</h3> 125 <p>There's more than one way to dice this onion, but here's a broad overview. You might also be interested in <a href="/topics/">our topics list</a>.</p> 126 </div> 127 </div> 128 <div class="columns callout"> 129 <div class="column col-sm-auto col-7"> 130 <img alt="design tools" src="/images/flame/flame-design-science.png"> 131 </div> 132 <div class="column col-sm-auto col-5"> 133 <h2>Regression (aka "how X affects Y")</h2> 134 <p>Learn what you <em>really</em> mean when you wonder if two things are "correlated."</p> 135 136 <ul> 137 <li>Unemployment and life expectancy from the Associated Press</li> 138 <li>Machine bias from ProPublica</li> 139 <li>More from Dallas Morning News, Reveal, APM Reports, and others</li> 140 </ul> 141 <p><a class="btn btn-default" href="/regression/what-is-regression/">Get started with regression now â</a></p> 142 </div> 143 </div> 144 <div class="columns callout"> 145 <div class="column col-sm-auto col-5"> 146 <h2>Text analysis</h2> 147 <p>From counting words to the terrors of sentiment analysis, you'll be covered.</p> 148 149 <ul> 150 <li>"Cut and paste" legislation from USA Today/Arizona Central</li> 151 <li>Democratic candidate topics from Bloomberg</li> 152 <li>More from New York Times and others</li> 153 </ul> 154 <p><a class="btn btn-default" href="/text-analysis/types-of-text-analysis">Get started with text analysis now â</a></p> 155 </div> 156 <div class="column col-sm-auto col-7"> 157 <img alt="books with feelings" src="/images/flame/flame-books.png"> 158 </div> 159 </div> 160 <div class="columns callout"> 161 <div class="column col-sm-auto col-7"> 162 <img alt="robot saying X" src="/images/flame/flame-no-comments.png"> 163 </div> 164 <div class="column col-sm-auto col-5"> 165 <h2>Classification</h2> 166 <p>No time to look at 100,000 things? Teach computers to automatically classify documents, crimes, airplanes, or anything else!</p> 167 168 <ul> 169 <li>Misclassified crimes from LA Times</li> 170 <li>Finding spyplanes from BuzzFeed</li> 171 <li>More from The Washington Post, Atlanta Journal-Constitution, and others</li> 172 </ul> 173 <p><a class="btn btn-default" href="/classification/intro-to-classification/">Get started with classification now â</a></p> 174 </div> 175 </div> 176</div> 177 178<div class="divider"></div> 179 180<div class="content section-projects"> 181 <div class="columns"> 182 <div class="column col-sm-auto col-8 col-mx-auto text-center"> 183 <h3>Published reproductions</h3> 184 <p>Data science and machine learning can be used anywhere! From a small visualization at The Upshot to a year-long investigations by Reveal, let's try to put these new skills in context.</p> 185 </div> 186 <div class="column col-12"> 187 <div class="columns"> 188 189 190 <div class="column col-4 col-lg-6 col-sm-12"> 191 <article class="card"> 192 <div class="card-header"> 193 <h5><a href="/nyt-takata-airbags/">Searching for faulty airbags in vehicle complaints</a></h5> 194 </div> 195 <div class="card-body"> 196 <p>The National Highway Transportation Safety Administration receives thousands and thousands of vehicle complaints each year. Can we train a computer to filter out leads on Takata airbag malfunctions?</p> 197 </div> 198 <div class="card-footer"> 199 The New York Times 200 </div> 201 </article> 202 </div> 203 204 205 206 <div class="column col-4 col-lg-6 col-sm-12"> 207 <article class="card"> 208 <div class="card-header"> 209 <h5><a href="/latimes-crime-classification/">Building a crime classification engine</a></h5> 210 </div> 211 <div class="card-body"> 212 <p>Using machine learning as an investigative tool to cast light on years of underreporting by the Los Angeles Police Department.</p> 213 </div> 214 <div class="card-footer"> 215 Los Angeles Times 216 </div> 217 </article> 218 </div> 219 220 221 222 <div class="column col-4 col-lg-6 col-sm-12"> 223 <article class="card"> 224 <div class="card-header"> 225 <h5><a href="/caixin-museum-word-count/">Chinese museum analysis</a></h5> 226 </div> 227 <div class="card-body"> 228 <p>A word-count analysis of the names of around 4500 museums in China.</p> 229 </div> 230 <div class="card-footer"> 231 Caixin 232 </div> 233 </article> 234 </div> 235 236 237 238 <div class="column col-4 col-lg-6 col-sm-12"> 239 <article class="card"> 240 <div class="card-header"> 241 <h5><a href="/wapo-app-reviews/">Analyzing online safety through app store reviews</a></h5> 242 </div> 243 <div class="card-body"> 244 <p>
244After downloading over a hundred thousand reviews of "random chat apps," how to find reports of bullying, racism, and unwanted sexual behavior.</p> 245 </div> 246 <div class="card-footer"> 247 The Washington Post 248 </div> 249 </article> 250 </div> 251 252 253 254 <div class="column col-4 col-lg-6 col-sm-12"> 255 <article class="card"> 256 <div class="card-header"> 257 <h5><a href="/ajc-doctors-abuse/">Uncovering abusive doctors that were allowed to continue practicing</a></h5> 258 </div> 259 <div class="card-body"> 260 <p>How to comb through 100,000 disciplinary documents without reading each individual one.</p> 261 </div> 262 <div class="card-footer"> 263 Atlanta Journal-Constitution 264 </div> 265 </article> 266 </div> 267 268 269 270 <div class="column col-4 col-lg-6 col-sm-12"> 271 <article class="card"> 272 <div class="card-header"> 273 <h5><a href="/upshot-trump-emolex/">Analyzing the tone of Trump's speeches</a></h5> 274 </div> 275 <div class="card-body"> 276 <p>Standard sentiment analysis scores a document on a positive-vs-negative scale. Using the Emotional Lexicon, though, you can add unique emotional measurements like anger, joy, surprise, or fear.</p> 277 </div> 278 <div class="card-footer"> 279 The New York Times 280 </div> 281 </article> 282 </div> 283 284 285 286 <div class="column col-4 col-lg-6 col-sm-12"> 287 <article class="card"> 288 <div class="card-header"> 289 <h5><a href="/azcentral-text-reuse-model-legislation/">Detecting special interest model legislation in state laws</a></h5> 290 </div> 291 <div class="card-body"> 292 <p>Special interest groups use model legislation to push their agendas in state government. How can we find bills based on these "cut and paste" models?</p> 293 </div> 294 <div class="card-footer"> 295 USA Today, The Arizona Republic, and the Center for Public Integrity 296 </div> 297 </article> 298 </div> 299 300 301 302 <div class="column col-4 col-lg-6 col-sm-12"> 303 <article class="card"> 304 <div class="card-header"> 305 <h5><a href="/fcc-comments/">Detecting bots in FCC comment submissions</a></h5> 306 </div> 307 <div class="card-body"> 308 <p>The comment period on the FCC's net neutrality decision was flooded with bots. See how one still-in-training data scientist tackled finding re-used comments.</p> 309 </div> 310 <div class="card-footer"> 311 312 </div> 313 </article> 314 </div> 315 316 317 318 <div class="column col-4 col-lg-6 col-sm-12"> 319 <article class="card"> 320 <div class="card-header"> 321 <h5><a href="/bloomberg-tweet-topics/">Figuring out what Democratic candidates care about</a></h5> 322 </div> 323 <div class="card-body"> 324 <p>In the wide field of Democratic presidential candidates, who cares about what topics and how do these topics change over time?</p> 325 </div> 326 <div class="card-footer"> 327 Bloomberg 328 </div> 329 </article> 330 </div> 331 332 333 334 <div class="column col-4 col-lg-6 col-sm-12"> 335 <article class="card"> 336 <div class="card-header"> 337 <h5><a href="/nyt-trump-tweets/">What does Trump tweet about?</a></h5> 338 </div> 339 <div class="card-body"> 340 <p>What does Trump tweet about? An analysis of over 11,000 tweets.</p> 341 </div> 342 <div class="card-footer"> 343 The New York Times 344 </div> 345 </article> 346 </div> 347 348 349 350 <div class="column col-4 col-lg-6 col-sm-12"> 351 <article class="card"> 352 <div class="card-header"> 353 <h5><a href="/ap-regression-unemployment/">Examining life expectancy at the local level</a></h5> 354 </div> 355 <div class="card-body"> 356 <p>Combine geographically granular life expectancy data with the American Community Survey to see how poverty, education, income, and demographics can affect a community.</p> 357 </div> 358 <div class="card-footer"> 359 The Associated Press 360 </div> 361 </article> 362 </div> 363 364 365 366 <div class="column col-4 col-lg-6 col-sm-12"> 367 <article class="card"> 368 <div class="card-header"> 369 <h5>
369<a href="/fivethirtyeight-p-hacking/">p values and p-hacking</a></h5> 370 </div> 371 <div class="card-body"> 372 <p>p-values and the quest for "statistical significance"</p> 373 </div> 374 <div class="card-footer"> 375 FiveThirtyEight 376 </div> 377 </article> 378 </div> 379 380 381 382 <div class="column col-4 col-lg-6 col-sm-12"> 383 <article class="card"> 384 <div class="card-header"> 385 <h5><a href="/milwaukee-potholes/">Predicting delays in patching potholes based on demographics</a></h5> 386 </div> 387 <div class="card-body"> 388 <p>An analysis of the relationship between race and city sanitation services in Milwaukee.</p> 389 </div> 390 <div class="card-footer"> 391 Milwaukee Journal-Sentinel 392 </div> 393 </article> 394 </div> 395 396 397 398 <div class="column col-4 col-lg-6 col-sm-12"> 399 <article class="card"> 400 <div class="card-header"> 401 <h5><a href="/dmn-texas-school-cheating/">Finding cheating schools in Texas with linear regression</a></h5> 402 </div> 403 <div class="card-body"> 404 <p>Some schools in Texas had an odd jump in standardized test scores between different grades. Was it cheating? Linear regression is on the case!</p> 405 </div> 406 <div class="card-footer"> 407 Dallas Morning News 408 </div> 409 </article> 410 </div> 411 412 413 414 <div class="column col-4 col-lg-6 col-sm-12"> 415 <article class="card"> 416 <div class="card-header"> 417 <h5><a href="/tampa-bay-times-schools/">Measuring the impact of re-segregation on Florida elementary schools</a></h5> 418 </div> 419 <div class="card-body"> 420 <p>Using race, income, and other data to predict the performance of schools in Pinellas County, Florida. Along with a linear regression-driven critique.</p> 421 </div> 422 <div class="card-footer"> 423 Tampa Bay Times 424 </div> 425 </article> 426 </div> 427 428 429 430 <div class="column col-4 col-lg-6 col-sm-12"> 431 <article class="card"> 432 <div class="card-header"> 433 <h5><a href="/car-crashes-weight-regression/">Analyzing whether larger cars cause more deadly crashes</a></h5> 434 </div> 435 <div class="card-body"> 436 <p>Reproducing a research paper on the impact of weight on car accidents, along with a look at a state-based car crash database.</p> 437 </div> 438 <div class="card-footer"> 439 Review of Economic Studies 440 </div> 441 </article> 442 </div> 443 444 445 446 <div class="column col-4 col-lg-6 col-sm-12"> 447 <article class="card"> 448 <div class="card-header"> 449 <h5><a href="/propublica-opportunity-gap/">Tracking equal access to school programs</a></h5> 450 </div> 451 <div class="card-body"> 452 <p>Looking at differences in access to advanced classes between schools with wealthy
452students and schools with poor students.</p> 453 </div> 454 <div class="card-footer"> 455 ProPublica 456 </div> 457 </article> 458 </div> 459 460 461 462 <div class="column col-4 col-lg-6 col-sm-12"> 463 <article class="card"> 464 <div class="card-header"> 465 <h5><a href="/boston-globe-tickets/">Investigating who gets a ticket and who gets a warning</a></h5> 466 </div> 467 <div class="card-body"> 468 <p>A classic piece of data journalism analyzing ticketing by Massachusetts police, and whether the race or gender of the driver might change the outcome.</p> 469 </div> 470 <div class="card-footer"> 471 The Boston Globe 472 </div> 473 </article> 474 </div> 475 476 477 478 <div class="column col-4 col-lg-6 col-sm-12"> 479 <article class="card"> 480 <div class="card-header"> 481 <h5><a href="/stanford-open-policing/">Stanford Open Policing Data</a></h5> 482 </div> 483 <div class="card-body"> 484 <p>A giant dataset of standardized data policing data across different states</p> 485 </div> 486 <div class="card-footer"> 487 488 </div> 489 </article> 490 </div> 491 492 493 494 <div class="column col-4 col-lg-6 col-sm-12"> 495 <article class="card"> 496 <div class="card-header"> 497 <h5><a href="/buzzfeed-spy-planes/">Uncovering surveillance planes with BuzzFeed</a></h5> 498 </div> 499 <div class="card-body"> 500 <p>From a list of points along a flight's path, how can you say "this looks like a surveillance plane?" And once you've found them, what do you do with the results?</p> 501 </div> 502 <div class="card-footer"> 503 BuzzFeed News 504 </div> 505 </article> 506 </div> 507 508 509 510 <div class="column col-4 col-lg-6 col-sm-12"> 511 <article class="card"> 512 <div class="card-header"> 513 <h5><a href="/reveal-mortgages/">Analyzing mortgage rejections for racial bias</a></h5> 514 </div> 515 <div class="card-body"> 516 <p>Based on government-mandated data collection on mortgage granting, are certain banks or areas discriminatory in their lending practices?</p> 517 </div> 518 <div class="card-footer"> 519 Reveal 520 </div> 521 </article> 522 </div> 523 524 525 526 <div class="column col-4 col-lg-6 col-sm-12"> 527 <article class="card"> 528 <div class="card-header"> 529 <h5><a href="/apm-reports-jury-bias/">Bias in the jury selection process</a></h5> 530 </div> 531 <div class="card-body"> 532 <p>When selecting a jury, both the defense and the prosecution are allowed to strike potential jurors from the pool. While the potential jurors provide answers to a questionnaire, what kind of role might race play in their selection or rejection?</p> 533 </div> 534 <div class="card-footer"> 535 APM Reports 536 </div> 537 </article> 538 </div> 539 540 541 542 <div class="column col-4 col-lg-6 col-sm-12"> 543 <article class="card"> 544 <div class="card-header"> 545 <h5><a href="/reuters-asylum/">Analyzing the impact of particular judges on the US asylum process</a></h5> 546 </div> 547 <div class="card-body"> 548 <p>
548In U.S. immigration courts, are certain judges and locations more likely to approve or deny claims of asylum?</p> 549 </div> 550 <div class="card-footer"> 551 Reuters 552 </div> 553 </article> 554 </div> 555 556 557 558 <div class="column col-4 col-lg-6 col-sm-12"> 559 <article class="card"> 560 <div class="card-header"> 561 <h5><a href="/propublica-pardons/">Investigating who receives presidential pardons</a></h5> 562 </div> 563 <div class="card-body"> 564 <p>An analysis of the presidential pardon process, but also a look into what to do when a very personal dataset doesn't exist.</p> 565 </div> 566 <div class="card-footer"> 567 ProPublica 568 </div> 569 </article> 570 </div> 571 572 573 574 <div class="column col-4 col-lg-6 col-sm-12"> 575 <article class="card"> 576 <div class="card-header"> 577 <h5><a href="/propublica-criminal-sentencing/">An analysis of racial bias in criminal sentencing</a></h5> 578 </div> 579 <div class="card-body"> 580 <p>Can an algorithm be racist? An examination of the COMPAS algorithm used as an aid in making sentencing and parole decisions. Also featuring a critique of the critique!</p> 581 </div> 582 <div class="card-footer"> 583 ProPublica 584 </div> 585 </article> 586 </div> 587 588 589 590 <div class="column col-4 col-lg-6 col-sm-12"> 591 <article class="card"> 592 <div class="card-header"> 593 <h5><a href="/foia-predictor/">Predicting FOIA requests success rates</a></h5> 594 </div> 595 <div class="card-body"> 596 <p>Government agencies seem to fulfill or reject FOIA request without rhyme or reason. Can a journalist use machine learning to improve their chances?</p> 597 </div> 598 <div class="card-footer"> 599 data.world 600 </div> 601 </article> 602 </div> 603 604 605 606 607 608 609 610 611 612 613</div> 614 </div> 615 </div> 616</div> 617 618<div class="divider"></div> 619 620<div class="content section-reference"> 621 <div class="columns"> 622 <div class="column col-sm-auto col-8 col-mx-auto text-center"> 623 <h3>Data snippets library</h3> 624 <p>This doesn't need a section, but it'll feel left out if everything else gets one.</p> 625 </div> 626 </div> 627 <div class="columns"> 628 <div class="column col-sm-auto col-3"> 629 <h3>Vectorizing text</h3> 630 <p>Slicing and dicing text, mostly focused on scikit-learn's vectorizers. Includes lots of tweaks for stemming, n-grams, and more.</p> 631 <p><a class="btn btn-primary" href="/reference/vectorizing/">See the snippets</a></p> 632 </div> 633 <div class="column col-sm-auto col-3"> 634 <h3>Text analysis</h3> 635 <p>Topic modeling, clustering, and other tools of the natural language processing trade.</p> 636 <p><a class="btn btn-primary" href="/reference/text-analysis/">See the snippets</a></p> 637 </div> 638 <div class="column col-sm-auto col-3"> 639 <h3>Regression</h3> 640 <p>Code snippets for performing linear and logistic regression in statsmodels, along with techniques to use and abuse the "formula" method of writing regressions.</p> 641 <p><a class="btn btn-primary" href="/reference/regression/">See the snippets</a></p> 642 </div> 643 <div class="column col-sm-auto col-3"> 644 <h3>Classification</h3> 645 <p>Cut-and-paste-ready code to leverage scikit-learn's classifiers, and other related tasks things like feature importance and confusion matrices.</p> 646 <p><a class="btn btn-primary" href="/reference/classification/">See the snippets</a></p> 647 </div> 648 </div> 649</div> 650 651 652 <div class="footer bg-secondary"> 653 <div class="content"> 654 <div class="columns"> 655 <div class="column col-8 col-sm-12"> 656 <p><strong>About the site</strong></p> 657 <p>Hi, I'm <a href="https://twitter.com/dangerscarf">Soma</a>, welcome to Data Science for Journalism a.k.a. investigate.ai!</p> 658 <p>There's been a lot of buzz about machine learning and "artificial intelligence" being used in stories over the past few years. It's mostly not that complicated - a little stats, a classifier here or there - but it's hard to know where to start without a little help.</p> 659 <p>If you know a little Python programming, hopefully this site can be that help! <a href="/about">Learn more about this project here.</a></p> 660 <p><strong>Our newsletter</strong></p> 661 <form action="https://littlecolumns.us12.list-manage.com/subscribe/post?u=ecdebf156be0b7e068fac7c25&id=16537e7c90" method="post" role="form" target="_blank" novalidate> 662 <div class="input-group"> 663 <input name="EMAIL" type="email" class="form-input" placeholder="Enter your Email" required=""> 664 <div style="position: absolute; left: -5000px;" aria-hidden="true"><input type="text" name="b_ecdebf156be0b7e068fac7c25_16537e7c90" tabindex="-1" value=""></div> 665 <div class="input-group-append"> 666 <button class="btn btn-primary input-group-btn" type="submit" aria-label="Email signup submit button"> 667 <i class="fas fa-envelope"></i> 668 Sign up 669 </button> 670 </div> 671 </div> 672 </form> 673 674 </div> 675 <div class="column col-4 col-sm-12"> 676 <p><strong>Links</strong></p> 677 <ul> 678 <li><a href="mailto:[email protected]">[email protected]</a></li> 679 <li><a href="https://twitter.com/dangerscarf">@dangerscarf</a></li> 680 <li><a href="/privacy-policy/">Privacy policy</a></li> 681 <li><a href="/newsletter/">Newsletter</a></li> 682 <li>Images via <a href="https://icons8.com/">icons8</a></li> 683 </ul> 684 <p>Thanks to <a href="https://journalism.columbia.edu/">Columbia Journalism School</a>, the <a href="https://knightfoundation.org/">Knight Foundation</a>, and <a href="/about#thankyou">many others</a>.</p> 685 </div> 686 </div> 687 </div> 688</div>
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689 690 </div> 691 <div class="bg-dark content-sidebar"> 692 <div class="sidebar-holder"> 693 <div class="brand"> 694 <a href="/">investigate.ai</a> 695 <br> 696 data science for everybody 697 </div> 698 699 <div class="accordion-container"> 700 <div class="accordion"> 701 <div class="form-group sidebar-sticky"> 702 <form method="GET" action="/search/" class="search-form"> 703 <div class="input-group"> 704 <input type="text" class="form-input input-sm" name="q" placeholder="Search investigate.ai"> 705 <button class="btn btn-primary input-group-btn btn-sm">Search</button> 706 </div> 707 </form> 708 </div> 709 710 711 <h4 id="textanalysis" class="sidebar-sticky"> 712 <a href="#textanalysis">Text analysis</a> 713 </h4> 714 <ol class="menu menu-nav"> 715 716 <li class="menu-item"> 717 718 <a href="/text-analysis/types-of-text-analysis/"> 719 Types of text analysis 720 </a> 721 <div class="toc"></div> 722 723 724 725 </li> 726 727 <li class="menu-item"> 728 729 730 731 <input 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class="toc"></div> 817 818 819 820 </li> 821 822 <li class="menu-item"> 823 824 <a href="/text-analysis/explaining-n-grams-in-natural-language-processing/"> 825 Multi-word phrases and n-grams 826 </a> 827 <div class="toc"></div> 828 829 830 831 </li> 832 833 <li class="menu-item"> 834 835 <a href="/text-analysis/stemming-and-lemmatization/"> 836 Standardizing text with stemming and lemmatization 837 </a> 838 <div class="toc"></div> 839 840 841 842 </li> 843 844 <li class="menu-item"> 845 846 <a href="/text-analysis/using-tf-idf-with-chinese/"> 847 Using TF-IDF with Chinese text 848 </a> 849 <div class="toc"></div> 850 851 852 853 </li> 854 855 </ol> 856 </div> 857 858 </li> 859 860 <li class="menu-item"> 861 862 863 864 <input id="accordion-sentimentanalysis" type="checkbox" name="accordion-checkbox" hidden=""> 865 <label class="accordion-header c-hand" for="accordion-sentimentanalysis"> 866 <i class="icon icon-arrow-right mr-1"></i> 867 Sentiment analysis 868 </label> 869 <div class="accordion-body"> 870 <ol class="menu menu-nav"> 871 872 <li class="menu-item"> 873 874 <a href="/investigating-sentiment-analysis/comparing-sentiment-analysis-tools/"> 875 Comparing sentiment analysis tools 876 </a> 877 <div class="toc"></div> 878 879 880 881 </li> 882 883 <li class="menu-item"> 884 885 <a href="/investigating-sentiment-analysis/designing-your-own-sentiment-analysis-tool/"> 886 Design your own sentiment analyzer 887 </a> 888 <div class="toc"></div> 889 890 891 892 </li> 893 894 <li class="menu-item"> 895 896 <a href="/investigating-sentiment-analysis/more-data-to-train-our-sentiment-analysis-tool/"> 897 Improving your tool 898 </a> 899 <div class="toc"></div> 900 901 902 903 </li> 904 905 <li class="menu-item"> 906 907 <a href="/upshot-trump-emolex/nrc-emotional-lexicon/"> 908 NRC Emotional Lexicon 909 </a> 910 <div class="toc"></div> 911 912 913 914 </li> 915 916 <li class="menu-item"> 917 918 <a href="/upshot-trump-emolex/trump-vs-state-of-the-union-addresses/"> 919 Project: UpShot State of the Union 920 </a> 921 <div class="toc"></div> 922 923 924 925 </li> 926 927 <li class="menu-item"> 928 929 <a href="/nyt-trump-tweets/"> 930 Project: NYT Trump tweets 931 </a> 932 <div class="toc"></div> 933 934 935 936 </li> 937 938 </ol> 939 </div> 940 941 </li> 942 943 <li class="menu-item"> 944 945 946 947 <input id="accordion-documentstotext" type="checkbox" name="accordion-checkbox" hidden=""> 948 <label class="accordion-header c-hand" for="accordion-documentstotext"> 949 <i class="icon icon-arrow-right mr-1"></i> 950 Documents to text 951 </label> 952 <div class="accordion-body"> 953 <ol class="menu menu-nav"> 954 955 <li class="menu-item"> 956 957 <a href="/text-analysis/processing-documents-with-apache-tika/"> 958 Converting documents to text (English) 959 </a> 960 <div class="toc"></div> 961 962 963 964 </li> 965 966 <li class="menu-item"> 967 968 <a href="/text-analysis/processing-documents-with-apache-tika-greek/"> 969 Converting documents to text (non-English) 970 </a> 971 <div class="toc"></div> 972 973 974 975 </li> 976 977 </ol> 978 </div> 979 980 </li> 981 982 <li class="menu-item"> 983 984 985 986 <input id="accordion-conceptspeopleandplaces" type="checkbox" name="accordion-checkbox" hidden=""> 987 <label class="accordion-header c-hand" for="accordion-conceptspeopleandplaces"> 988 <i class="icon icon-arrow-right mr-1"></i> 989 Concepts, people and places 990 </label> 991 <div class="accordion-body"> 992 <ol class="menu menu-nav"> 993 994 <li class="menu-item"> 995 996 <a href="/text-analysis/introduction-to-topic-modeling/"> 997 Extracting topics from documents 998 </a> 999 <div class="toc"></div> 1000 1001 1002 1003 </li> 1004 1005 <li class="menu-item"> 1006 1007 <a href="/text-analysis/choosing-the-right-number-of-topics-for-a-scikit-learn-topic-model/"> 1008 Choosing the right number of topics 1009 </a> 1010 <div class="toc"></div> 1011 1012 1013 1014 </li> 1015 1016 <li class="menu-item"> 1017 1018 <a href="/text-analysis/topic-models-with-gensim/"> 1019 Topic models with Gensim 1020 </a> 1021 <div class="toc"></div> 1022 1023 1024 1025 </li> 1026 1027 <li class="menu-item"> 1028 1029 <a href="/text-analysis/topic-modeling-and-clustering/"> 1030 Topic models vs clustering
1031 </a> 1032 <div class="toc"></div> 1033 1034 1035 1036 </li> 1037 1038 <li class="menu-item"> 1039 1040 <a href="/text-analysis/named-entity-recognition/"> 1041 Entity recognition 1042 </a> 1043 <div class="toc"></div> 1044 1045 1046 1047 </li> 1048 1049 <li class="menu-item"> 1050 1051 <a href="/text-analysis/word-embeddings/"> 1052 Intro to word embeddings 1053 </a> 1054 <div class="toc"></div> 1055 1056 1057 1058 </li> 1059 1060 <li class="menu-item"> 1061 1062 <a href="/text-analysis/document-similarity-using-word-embeddings/"> 1063 Conceptual document similarity 1064 </a> 1065 <div class="toc"></div> 1066 1067 1068 1069 </li> 1070 1071 <li class="menu-item"> 1072 1073 <a href="/text-analysis/comparing-documents-in-different-languages/"> 1074 Comparing documents in different languages 1075 </a> 1076 <div class="toc"></div> 1077 1078 1079 1080 </li> 1081 1082 </ol> 1083 </div> 1084 1085 </li> 1086 1087 </ol> 1088 1089 <h4 id="puttingthingsincategoriesautomatically" class="sidebar-sticky"> 1090 <a href="#puttingthingsincategoriesautomatically">Putting things in categories automatically</a> 1091 </h4> 1092 <ol class="menu menu-nav"> 1093 1094 <li class="menu-item"> 1095 1096 <a href="/classification/intro-to-classification/"> 1097 Introduction to Classification 1098 </a> 1099 <div class="toc"></div> 1100 1101 1102 1103 </li> 1104 1105 <li class="menu-item"> 1106 1107 1108 1109 <input id="accordion-techniques" type="checkbox" name="accordion-checkbox" hidden=""> 1110 <label class="accordion-header c-hand" for="accordion-techniques"> 1111 <i class="icon icon-arrow-right mr-1"></i> 1112 Techniques 1113 </label> 1114 <div class="accordion-body"> 1115 <ol class="menu menu-nav"> 1116 1117 <li class="menu-item"> 1118 1119 <a href="/classification/evaluating-classifiers/"> 1120 Evaluating classifiers 1121 </a> 1122 <div class="toc"></div> 1123 1124 1125 1126 </li> 1127 1128 <li class="menu-item"> 1129 1130 <a href="/classification/scikit-learn-and-categorical-features/"> 1131 Categorical features 1132 </a> 1133 <div class="toc"></div> 1134 1135 1136 1137 </li> 1138 1139 <li class="menu-item"> 1140 1141 <a href="/classification/using-classification-algorithms-with-text/"> 1142 Classifiers with text 1143 </a> 1144 <div class="toc"></div> 1145 1146 1147 1148 </li> 1149 1150 <li class="menu-item"> 1151 1152 <a href="/classification/correcting-for-imbalanced-datasets/"> 1153 Correcting for imbalanced datasets 1154 </a> 1155 <div class="toc"></div> 1156 1157 1158 1159 </li> 1160 1161 </ol> 1162 </div> 1163 1164 </li> 1165 1166 <li class="menu-item"> 1167 1168 1169 1170 <input id="accordion-projects" type="checkbox" name="accordion-checkbox" hidden=""> 1171 <label class="accordion-header c-hand" for="accordion-projects"> 1172 <i class="icon icon-arrow-right mr-1"></i> 1173 Projects 1174 </label> 1175 <div class="accordion-body"> 1176 <ol class="menu menu-nav"> 1177 1178 <li class="menu-item"> 1179 1180 <a href="/buzzfeed-spy-planes/buzzfeed-surveillance-planes-random-forests/"> 1181 BuzzFeed: Spy planes 1182 </a> 1183 <div class="toc"></div> 1184 1185 1186 1187 </li> 1188 1189 <li class="menu-item"> 1190 1191 <a href="/wapo-app-reviews/predict-reviews/"> 1192 WaPo chat: App reviews 1193 </a> 1194 <div class="toc"></div> 1195 1196 1197 1198 </li> 1199 1200 <li class="menu-item"> 1201 1202 <a href="/nyt-takata-airbags/nyt-takata-completed/"> 1203 NYT: Faulty airbag search 1204 </a> 1205 <div class="toc"></div> 1206 1207 1208 1209 </li> 1210 1211 <li class="menu-item"> 1212 1213 <a href="/latimes-crime-classification/using-a-classifier-to-find-misclassified-crimes/"> 1214 LA Times: crime classifier 1215 </a> 1216 <div class="toc"></div> 1217 1218 1219 1220 </li> 1221 1222 </ol> 1223 </div> 1224 1225 </li> 1226 1227 </ol> 1228 1229 <h4 id="howxaffectsy" class="sidebar-sticky"> 1230 <a href="#howxaffectsy">How X affects Y</a> 1231 </h4> 1232 <ol class="menu menu-nav"> 1233 1234 <li class="menu-item"> 1235 1236 <a href="/regression/what-is-regression/"> 1237 Finding relationships with regression 1238 </a> 1239 <div class="toc"></div> 1240 1241 1242 1243 </li> 1244 1245 <li class="menu-item"> 1246 1247 1248 1249 <input id="accordion-linearregression" type="checkbox" name="accordion-checkbox" hidden=""> 1250 <label class="accordion-header c-hand" for="accordion-linearregression"> 1251 <i class="icon icon-arrow-right mr-1"></i> 1252 Linear Regression 1253 </label> 1254 <div class="accordion-body"> 1255 <ol class="menu menu-nav"> 1256 1257 <li class="menu-item"> 1258 1259 <a href="/regression/linear-regression-quickstart/"> 1260 Linear regression (Quickstart) 1261 </a> 1262 <div class="toc"></div> 1263 1264 1265 1266 </li> 1267 1268 <li class="menu-item"> 1269 1270 <a href="/regression/linear-regression/"> 1271 Linear regression for humans 1272 </a> 1273 <div class="toc"></div> 1274 1275 1276 1277 </li> 1278 1279 <li class="menu-item"> 1280 1281 <a href="/regression/linear-regression-part-two/"> 1282 Putting regression to use 1283 </a> 1284 <div class="toc"></div> 1285 1286 1287 1288 </li> 1289 1290 <li class="menu-item"> 1291 1292 <a href="/regression/linear-regression-evaluation/"> 1293 Evaluating regressions 1294 </a> 1295 <div class="toc"></div> 1296 1297 1298 1299 </li> 1300 1301 <li class="menu-item"> 1302 1303 <a href="/ap-regression-unemployment/simple-regression-with-census-data-statsmodels-with-formulas/"> 1304 Associated Press: Life expectancy and unemployment 1305 </a> 1306 <div class="toc"></div> 1307 1308 1309 1310 </li> 1311 1312 </ol> 1313 </div> 1314 1315 </li> 1316 1317 <li class="menu-item"> 1318 1319 1320 1321 <input id="accordion-logisticregression" type="checkbox" name="accordion-checkbox" hidden=""> 1322 <label class="accordion-header c-hand" for="accordion-logisticregression"> 1323 <i class="icon icon-arrow-right mr-1"></i> 1324 Logistic Regression 1325 </label> 1326 <div class="accordion-body"> 1327 <ol class="menu menu-nav"> 1328 1329 <li class="menu-item"> 1330 1331 <a href="/regression/logistic-regression-quickstart/"> 1332 Logistic regression (Quickstart) 1333 </a> 1334 <div class="toc"></div> 1335 1336 1337 1338 </li> 1339 1340 <li class="menu-item"> 1341 1342 <a href="/regression/logistic-regression/"> 1343 Logistic regression for humans 1344 </a> 1345 <div class="toc"></div> 1346 1347 1348 1349 </li> 1350 1351 <li class="menu-item"> 1352 1353 <a href="/regression/logistic-regression-part-two/"> 1354 More complex logistic regressions 1355 </a> 1356 <div class="toc"></div> 1357 1358 1359 1360 </li> 1361 1362 <li class="menu-item"> 1363 1364 <a href="/regression/evaluating-logistic-regressions/"> 1365 Evaluating logistic regressions 1366 </a> 1367 <div class="toc"></div> 1368 1369 1370 1371 </li> 1372 1373 <li class="menu-item"> 1374 1375 <a href="/boston-globe-tickets/boston-globe-ticketing-regression/"> 1376 Boston Globe: Speeding tickets 1377 </a> 1378 <div class="toc"></div> 1379 1380 1381 1382 </li> 1383 1384 <li class="menu-item"> 1385 1386 <a href="/apm-reports-jury-bias/in-the-dark-alternative-formula-methods/"> 1387 APM Reports: Jury selection 1388 </a> 1389 <div class="toc"></div> 1390 1391 1392 1393 </li> 1394 1395 </ol> 1396 </div> 1397 1398 </li> 1399 1400 </ol> 1401 1402 <h4 id="pythondatasciencereference" class="sidebar-sticky"> 1403 <a href="#pythondatasciencereference">Python data science reference</a> 1404 </h4> 1405 <ol class="menu menu-nav"> 1406 1407 <li class="menu-item"> 1408 1409 <a href="/reference/"> 1410 Introduction 1411 </a> 1412 <div class="toc"></div> 1413 1414 1415 1416 </li> 1417 1418 <li class="menu-item"> 1419 1420 <a href="/reference/vectorizing/"> 1421 Vectorizing 1422 </a> 1423 <div class="toc"></div> 1424 1425 1426 1427 </li> 1428 1429 <li class="menu-item"> 1430 1431 <a href="/reference/text-analysis/"> 1432 Text Analysis 1433 </a> 1434 <div class="toc"></div> 1435 1436 1437 1438 </li> 1439 1440 <li class="menu-item"> 1441 1442 <a href="/reference/regression/"> 1443 Regression 1444 </a> 1445 <div class="toc"></div> 1446 1447 1448 1449 </li> 1450 1451 <li class="menu-item"> 1452 1453 <a href="/reference/classification/"> 1454 Classification 1455 </a> 1456 <div class="toc"></div> 1457 1458 1459 1460 </li> 1461 1462 </ol> 1463 1464 <h4 id="allprojects" class="sidebar-sticky"> 1465 <a href="#allprojects">All Projects</a> 1466 </h4> 1467 <ol class="menu menu-nav"> 1468 1469 <li class="menu-item"> 1470 1471 <a href="/projects/"> 1472 Project Summaries 1473 </a> 1474 <div class="toc"></div> 1475 1476 1477 1478 </li> 1479 1480 <li class="menu-item"> 1481 1482 1483 1484 <input id="accordion-strongnytstrongtakataairbagsearch" type="checkbox" name="accordion-checkbox" hidden=""> 1485 <label class="accordion-header c-hand" for="accordion-strongnytstrongtakataairbagsearch"> 1486 <i class="icon icon-arrow-right mr-1"></i> 1487 <strong>NYT:</strong> Takata airbag search 1488 </label> 1489 <div class="accordion-body"> 1490 <ol class="menu menu-nav"> 1491 1492 <li class="menu-item"> 1493 1494 <a href="/nyt-takata-airbags/"> 1495 Summary 1496 </a> 1497 <div class="toc"></div> 1498 1499 1500 1501 </li> 1502 1503 <li class="menu-item"> 1504 1505 <a href="/nyt-takata-airbags/airbag-classifier-search-binary/"> 1506 A simplistic reproduction of the NYT's research using logistic regression 1507 </a> 1508 <div class="toc"></div> 1509 1510 1511 1512 </li> 1513 1514 <li class="menu-item"> 1515 1516 <a href="/nyt-takata-airbags/airbag-classifier-search-decision-tree/"> 1517 A decision-tree reproduction of the NYT's research 1518 </a> 1519 <div class="toc"></div> 1520 1521 1522 1523 </li> 1524 1525 <li class="menu-item"> 1526 1527 <a href="/nyt-takata-airbags/airbag-classifier-search-countvectorizer/"> 1528 Combining a text vectorizer and a classifier to track down suspicious complaints 1529 </a> 1530 <div class="toc"></div> 1531 1532 1533 1534 </li> 1535 1536 </ol> 1537 </div> 1538 1539 </li> 1540 1541 <li class="menu-item"> 1542 1543 1544 1545 <input id="accordion-stronglatimesstrongcrimeclassification" type="checkbox" name="accordion-checkbox" hidden=""> 1546 <label class="accordion-header c-hand" for="accordion-stronglatimesstrongcrimeclassification"> 1547 <i class="icon icon-arrow-right mr-1"></i> 1548 <strong>LA Times:</strong> Crime classification 1549 </label> 1550 <div class="accordion-body"> 1551 <ol class="menu menu-nav"> 1552 1553 <li class="menu-item"> 1554 1555 <a href="/latimes-crime-classification/"> 1556 Summary 1557 </a> 1558 <div class="toc"></div> 1559 1560 1561 1562 </li> 1563 1564 <li class="menu-item"> 1565 1566 <a href="/latimes-crime-classification/using-a-classifier-to-find-misclassified-crimes/"> 1567 Predicting downgraded assaults with machine learning 1568 </a> 1569 <div class="toc"></div> 1570 1571 1572 1573 </li> 1574 1575 <li class="menu-item"> 1576 1577 <a href="/latimes-crime-classification/inspecting-classifications/"> 1578 Taking a closer look at our classifier and its misclassifications 1579 </a> 1580 <div class="toc"></div> 1581 1582 1583 1584 </li> 1585 1586 <li class="menu-item"> 1587 1588 <a href="/latimes-crime-classification/trying-out-different-classifiers/"> 1589 Trying out and combining different classifiers 1590 </a> 1591 <div class="toc"></div> 1592 1593 1594 1595 </li> 1596 1597 </ol> 1598 </div> 1599 1600 </li> 1601 1602 <li class="menu-item"> 1603 1604 1605 1606 <input id="accordion-strongcaixinstrongmuseumnames" type="checkbox" name="accordion-checkbox" hidden=""> 1607 <label class="accordion-header c-hand" for="accordion-strongcaixinstrongmuseumnames"> 1608 <i class="icon icon-arrow-right mr-1"></i> 1609 <strong>Caixin:</strong> Museum names 1610 </label> 1611 <div class="accordion-body"> 1612 <ol class="menu menu-nav"> 1613 1614 <li class="menu-item"> 1615 1616 <a href="/caixin-museum-word-count/"> 1617 Summary 1618 </a> 1619 <div class="toc"></div> 1620 1621 1622 1623 </li> 1624 1625 <li class="menu-item"> 1626 1627 <a href="/caixin-museum-word-count/chinese-museum-dataset-cleanup/"> 1628 Chinese museum dataset cleanup 1629 </a> 1630 <div class="toc"></div> 1631 1632 1633 1634 </li> 1635 1636 <li class="menu-item"> 1637 1638 <a href="/caixin-museum-word-count/chinese-museums-per-capita-analysis/"> 1639 Chinese museums per capita analysis 1640 </a> 1641 <div class="toc"></div> 1642 1643 1644 1645 </li> 1646 1647 <li class="menu-item"> 1648 1649 <a href="/caixin-museum-word-count/counting-words-in-chinese-museum-names/"> 1650 Counting words in Chinese museum names 1651 </a> 1652 <div class="toc"></div> 1653 1654 1655 1656 </li> 1657 1658 </ol> 1659 </div> 1660 1661 </li> 1662 1663 <li class="menu-item"> 1664 1665 1666 1667 <input id="accordion-strongwapostrongrandomchatappsafety" type="checkbox" name="accordion-checkbox" hidden=""> 1668 <label class="accordion-header c-hand" for="accordion-strongwapostrongrandomchatappsafety"> 1669 <i class="icon icon-arrow-right mr-1"></i> 1670 <strong>WaPo:</strong> Random chat app safety 1671 </label> 1672 <div class="accordion-body"> 1673 <ol class="menu menu-nav"> 1674 1675 <li class="menu-item"> 1676 1677 <a href="/wapo-app-reviews/"> 1678 Summary 1679 </a> 1680 <div class="toc"></div> 1681 1682 1683 1684 </li> 1685 1686 <li class="menu-item"> 1687 1688 <a href="/wapo-app-reviews/scrape-app-store-reviews/"> 1689 Scrape and combine app store reviews 1690 </a> 1691 <div class="toc"></div> 1692 1693 1694 1695 </li> 1696 1697 <li class="menu-item"> 1698 1699 <a href="/wapo-app-reviews/predict-reviews/"> 1700 Build a classifier to detect reviews about bad behavior 1701 </a> 1702 <div class="toc"></div> 1703 1704 1705 1706 </li> 1707 1708 </ol> 1709 </div> 1710 1711 </li> 1712 1713 <li class="menu-item"> 1714 1715 <a href="/ajc-doctors-abuse/"> 1716 <strong>AJC:</strong> Doctors and sex abuse 1717 </a> 1718 <div class="toc"></div> 1719 1720 1721 1722 </li> 1723 1724 <li class="menu-item"> 1725 1726 1727 1728 <input id="accordion-strongtheupshotstrongtrumpspeeches" type="checkbox" name="accordion-checkbox" hidden=""> 1729 <label class="accordion-header c-hand" for="accordion-strongtheupshotstrongtrumpspeeches"> 1730 <i class="icon icon-arrow-right mr-1"></i> 1731 <strong>The UpShot:</strong> Trump speeches 1732 </label> 1733 <div class="accordion-body"> 1734 <ol class="menu menu-nav"> 1735 1736 <li class="menu-item"> 1737 1738 <a href="/upshot-trump-emolex/"> 1739 Summary 1740 </a> 1741 <div class="toc"></div> 1742 1743 1744 1745 </li> 1746 1747 <li class="menu-item"> 1748 1749 <a href="/upshot-trump-emolex/nrc-emotional-lexicon/"> 1750 An introduction to the NRC Emotional Lexicon 1751 </a> 1752 <div class="toc"></div> 1753 1754 1755 1756 </li> 1757 1758 <li class="menu-item"> 1759 1760 <a href="/upshot-trump-emolex/trump-vs-state-of-the-union-addresses/"> 1761 Reproducing The UpShot's Trump State of the Union visualization 1762 </a> 1763 <div class="toc"></div> 1764 1765 1766 1767 </li> 1768 1769 </ol> 1770 </div> 1771 1772 </li> 1773 1774 <li class="menu-item"> 1775 1776 1777 1778 <input id="accordion-strongusatodaystrongmodellegislation" type="checkbox" name="accordion-checkbox" hidden=""> 1779 <label class="accordion-header c-hand" for="accordion-strongusatodaystrongmodellegislation"> 1780 <i class="icon icon-arrow-right mr-1"></i> 1781 <strong>USA Today:</strong> Model legislation 1782 </label> 1783 <div class="accordion-body"> 1784 <ol class="menu menu-nav"> 1785 1786 <li class="menu-item"> 1787 1788 <a href="/azcentral-text-reuse-model-legislation/"> 1789 Summary 1790 </a> 1791 <div class="toc"></div> 1792 1793 1794 1795 </li> 1796 1797 <li class="menu-item"> 1798 1799 <a href="/azcentral-text-reuse-model-legislation/01-downloading-one-million-pieces-of-legislation-from-legiscan/"> 1800 Downloading one million pieces of legislation from LegiScan 1801 </a> 1802 <div class="toc"></div> 1803 1804 1805 1806 </li> 1807 1808 <li class="menu-item"> 1809 1810 <a href="/azcentral-text-reuse-model-legislation/02-taking-a-mill
1810ion-pieces-of-legislation-from-a-csv-and-inserting-them-into-postgres/"> 1811 Taking a million pieces of legislation from a CSV and inserting them into Postgres 1812 </a> 1813 <div class="toc"></div> 1814 1815 1816 1817 </li> 1818 1819 <li class="menu-item"> 1820 1821 <a href="/azcentral-text-reuse-model-legislation/03-download-word-pdf-and-html-content-and-process-it-into-text-with-tika/"> 1822 Download Word, PDF and HTML content and process it into text with Tika 1823 </a> 1824 <div class="toc"></div> 1825 1826 1827 1828 </li> 1829 1830 <li class="menu-item"> 1831 1832 <a href="/azcentral-text-reuse-model-legislation/04-import-content-into-solr-for-advanced-text-searching/"> 1833 Import content into Solr for advanced text searching 1834 </a> 1835 <div class="toc"></div> 1836 1837 1838 1839 </li> 1840 1841 <li class="menu-item"> 1842 1843 <a href="/azcentral-text-reuse-model-legislation/05-checking-for-legislative-text-reuse-using-python-solr-and-ngrams/"> 1844 Checking for legislative text reuse using Python, Solr, and ngrams 1845 </a> 1846 <div class="toc"></div> 1847 1848 1849 1850 </li> 1851 1852 <li class="menu-item"> 1853 1854 <a href="/azcentral-text-reuse-model-legislation/05-checking-for-legislative-text-reuse-using-python-solr-and-simple-text-search/"> 1855 Checking for legislative text reuse using Python, Solr, and simple text search 1856 </a> 1857 <div class="toc"></div> 1858 1859 1860 1861 </li> 1862 1863 <li class="menu-item"> 1864 1865 <a href="/azcentral-text-reuse-model-legislation/06-search-for-model-legislation-in-over-one-million-bills-using-postgres-and-solr/"> 1866 Search for model legislation in over one million bills using Postgres and Solr 1867 </a> 1868 <div class="toc"></div> 1869 1870 1871 1872 </li> 1873 1874 <li class="menu-item"> 1875 1876 <a href="/azcentral-text-reuse-model-legislation/using-topic-modeling-to-categorize-legislation/"> 1877 Using topic modeling to categorize legislation 1878 </a> 1879 <div class="toc"></div> 1880 1881 1882 1883 </li> 1884 1885 </ol> 1886 </div> 1887 1888 </li> 1889 1890 <li class="menu-item"> 1891 1892 <a href="/fcc-comments/"> 1893 FCC comment bots 1894 </a> 1895 <div class="toc"></div> 1896 1897 1898 1899 </li> 1900 1901 <li class="menu-item"> 1902 1903 1904 1905 <input id="accordion-strongbloombergstrongdemocraticcandidatetweets" type="checkbox" name="accordion-checkbox" hidden=""> 1906 <label class="accordion-header c-hand" for="accordion-strongbloombergstrongdemocraticcandidatetweets"> 1907 <i class="icon icon-arrow-right mr-1"></i> 1908 <strong>Bloomberg:</strong> Democratic Candidate Tweets 1909 </label> 1910 <div class="accordion-body"> 1911 <ol class="menu menu-nav"> 1912 1913 <li class="menu-item"> 1914 1915 <a href="/bloomberg-tweet-topics/"> 1916 Summary 1917 </a> 1918 <div class="toc"></div> 1919 1920 1921 1922 </li> 1923 1924 <li class="menu-item"> 1925 1926 <a href="/bloomberg-tweet-topics/scrape-tweets-from-presidential-primary-candidates/"> 1927 Downloading all 2019 tweets from Democratic presidential candidates 1928 </a> 1929 <div class="toc"></div> 1930 1931 1932 1933 </li> 1934 1935 <li class="menu-item"> 1936 1937 <a href="/bloomberg-tweet-topics/topic-modeling-for-tweets/"> 1938 Using topic modeling to analyze presidential candidate tweets 1939 </a> 1940 <div class="toc"></div> 1941 1942 1943 1944 </li> 1945 1946 <li class="menu-item"> 1947 1948 <a href="/bloomberg-tweet-topics/assigning-categories-to-text-using-keyword-matching/"> 1949 Assigning categories to tweets using keyword matching 1950 </a> 1951 <div class="toc"></div> 1952 1953 1954 1955 </li> 1956 1957 <li class="menu-item"> 1958 1959 <a href="/bloomberg-tweet-topics/building-streamgraphs-from-candidate-tweets/"> 1960 Building streamgraphs from categorized and dated datasets 1961 </a> 1962 <div class="toc"></div> 1963 1964 1965 1966 </li> 1967 1968 </ol> 1969 </div> 1970 1971 </li> 1972 1973 <li class="menu-item"> 1974 1975 <a href="/nyt-trump-tweets/"> 1976 <strong>NYT:</strong> Trump tweets 1977 </a> 1978 <div class="toc"></div> 1979 1980 1981 1982 </li> 1983 1984 <li class="menu-item"> 1985 1986 1987 1988 <input id="accordion-strongapstronglifeexpectancy" type="checkbox" name="accordion-checkbox" hidden=""> 1989 <label class="accordion-header c-hand" for="accordion-strongapstronglifeexpectancy"> 1990 <i class="icon icon-arrow-right mr-1"></i> 1991 <strong>AP:</strong> Life expectancy 1992 </label> 1993 <div class="accordion-body"> 1994 <ol class="menu menu-nav"> 1995 1996 <li class="menu-item"> 1997 1998 <a href="/ap-regression-unemployment/"> 1999 Summary 2000 </a> 2001 <div class="toc"></div> 2002 2003 2004 2005 </li> 2006 2007 <li class="menu-item"> 2008 2009 <a href="/ap-regression-unemployment/simple-regression-with-census-data-statsmodels-with-formulas/"> 2010 Simple logistic regression using statsmodels (formula version) 2011 </a> 2012 <div class="toc"></div> 2013 2014 2015 2016 </li> 2017 2018 <li class="menu-item"> 2019 2020 <a href="/ap-regression-unemployment/simple-regression-with-census-data-statsmodels-with-dataframes/"> 2021 Simple logistic regression using statsmodels (dataframes version) 2022 </a> 2023 <div class="toc"></div> 2024 2025 2026 2027 </li> 2028 2029 </ol> 2030 </div> 2031 2032 </li> 2033 2034 <li class="menu-item"> 2035
2036 <a href="/fivethirtyeight-p-hacking/"> 2037 <strong>FiveThirtyEight:</strong> P-values 2038 </a> 2039 <div class="toc"></div> 2040 2041 2042 2043 </li> 2044 2045 <li class="menu-item"> 2046 2047 2048 2049 <input id="accordion-strongmilwaukeejournalsentinelstrongpotholes" type="checkbox" name="accordion-checkbox" hidden=""> 2050 <label class="accordion-header c-hand" for="accordion-strongmilwaukeejournalsentinelstrongpotholes"> 2051 <i class="icon icon-arrow-right mr-1"></i> 2052 <strong>Milwaukee Journal-Sentinel:</strong> Potholes 2053 </label> 2054 <div class="accordion-body"> 2055 <ol class="menu menu-nav"> 2056 2057 <li class="menu-item"> 2058 2059 <a href="/milwaukee-potholes/"> 2060 Summary 2061 </a> 2062 <div class="toc"></div> 2063 2064 2065 2066 </li> 2067 2068 <li class="menu-item"> 2069 2070 <a href="/milwaukee-potholes/milwaukee-journal-sentinel-and-potholes-full-walkthrough/"> 2071 Pothole geographic analysis and linear regression, complete walkthrough 2072 </a> 2073 <div class="toc"></div> 2074 2075 2076 2077 </li> 2078 2079 <li class="menu-item"> 2080 2081 <a href="/milwaukee-potholes/milwaukee-journal-sentinel-and-potholes-without-merging/"> 2082 Pothole demographics linear regression, no spatial analysis 2083 </a> 2084 <div class="toc"></div> 2085 2086 2087 2088 </li> 2089 2090 </ol> 2091 </div> 2092 2093 </li> 2094 2095 <li class="menu-item"> 2096 2097 2098 2099 <input id="accordion-strongdallasmorningnewsstrongcheatingschools" type="checkbox" name="accordion-checkbox" hidden=""> 2100 <label class="accordion-header c-hand" for="accordion-strongdallasmorningnewsstrongcheatingschools"> 2101 <i class="icon icon-arrow-right mr-1"></i> 2102 <strong>Dallas Morning News:</strong> Cheating schools 2103 </label> 2104 <div class="accordion-body"> 2105 <ol class="menu menu-nav"> 2106 2107 <li class="menu-item"> 2108 2109 <a href="/dmn-texas-school-cheating/"> 2110 Summary 2111 </a> 2112 <div class="toc"></div> 2113 2114 2115 2116 </li> 2117 2118 <li class="menu-item"> 2119 2120 <a href="/dmn-texas-school-cheating/texas-school-cheating-finding-outliers-with-standard-deviation-and-regression/"> 2121 Finding outliers with standard deviation and regression 2122 </a> 2123 <div class="toc"></div> 2124 2125 2126 2127 </li> 2128 2129 <li class="menu-item"> 2130 2131 <a href="/dmn-texas-school-cheating/texas-school-cheating-finding-outliers-with-regression-residuals/"> 2132 Finding outliers with regression residuals (short version) 2133 </a> 2134 <div class="toc"></div> 2135 2136 2137 2138 </li> 2139 2140 <li class="menu-item"> 2141 2142 <a href="/dmn-texas-school-cheating/texas-school-cheating-graph-reproductions/"> 2143 Reproducing the graphics from The Dallas Morning News piece 2144 </a> 2145 <div class="toc"></div> 2146 2147 2148 2149 </li> 2150 2151 </ol> 2152 </div> 2153 2154 </li> 2155 2156 <li class="menu-item"> 2157 2158 2159 2160 <input id="accordion-strongtampabaytimesstrongfailurefactories" type="checkbox" name="accordion-checkbox" hidden=""> 2161 <label class="accordion-header c-hand" for="accordion-strongtampabaytimesstrongfailurefactories"> 2162 <i class="icon icon-arrow-right mr-1"></i> 2163 <strong>Tampa Bay Times:</strong> Failure factories 2164 </label> 2165 <div class="accordion-body"> 2166 <ol class="menu menu-nav"> 2167 2168 <li class="menu-item"> 2169 2170 <a href="/tampa-bay-times-schools/"> 2171 Summary 2172 </a> 2173 <div class="toc"></div> 2174 2175 2176 2177 </li> 2178 2179 <li class="menu-item"> 2180 2181 <a href="/tampa-bay-times-schools/linear-regression-on-florida-schools/"> 2182 Linear regression on Florida schools, complete walkthrough 2183 </a> 2184 <div class="toc"></div> 2185 2186 2187 2188 </li> 2189 2190 <li class="menu-item"> 2191 2192 <a href="/tampa-bay-times-schools/linear-regression-on-florida-schools-no-cleaning/"> 2193 Linear regression on Florida schools, no cleaning 2194 </a> 2195 <div class="toc"></div> 2196 2197 2198 2199 </li> 2200 2201 </ol> 2202 </div> 2203 2204 </li> 2205 2206 <li class="menu-item"> 2207 2208 2209 2210 <input id="accordion-caraccidentsandcarweight" type="checkbox" name="accordion-checkbox" hidden=""> 2211 <label class="accordion-header c-hand" for="accordion-caraccidentsandcarweight"> 2212 <i class="icon icon-arrow-right mr-1"></i> 2213 Car accidents and car weight 2214 </label> 2215 <div class="accordion-body"> 2216 <ol class="menu menu-nav"> 2217 2218 <li class="menu-item"> 2219 2220 <a href="/car-crashes-weight-regression/"> 2221 Summary 2222 </a> 2223 <div class="toc"></div> 2224 2225 2226 2227 </li> 2228 2229 <li class="menu-item"> 2230 2231 <a href="/car-crashes-weight-regression/car-crashes-feature-selection-and-engineering/"> 2232 Feature selection and engineering
2233 </a> 2234 <div class="toc"></div> 2235 2236 2237 2238 </li> 2239 2240 <li class="menu-item"> 2241 2242 <a href="/car-crashes-weight-regression/01-combine-excel-files-across-multiple-sheets-and-save-as-csv-files/"> 2243 Combine Excel files across multiple sheets and save as CSV files 2244 </a> 2245 <div class="toc"></div> 2246 2247 2248 2249 </li> 2250 2251 <li class="menu-item"> 2252 2253 <a href="/car-crashes-weight-regression/02-create-make-model-weights-csv/"> 2254 Create make model weights csv 2255 </a> 2256 <div class="toc"></div> 2257 2258 2259 2260 </li> 2261 2262 <li class="menu-item"> 2263 2264 <a href="/car-crashes-weight-regression/03-find-car-data-from-vins/"> 2265 Find car data from VINs 2266 </a> 2267 <div class="toc"></div> 2268 2269 2270 2271 </li> 2272 2273 <li class="menu-item"> 2274 2275 <a href="/car-crashes-weight-regression/04-combine-vins-and-weights/"> 2276 Combine VINs and weights 2277 </a> 2278 <div class="toc"></div> 2279 2280 2281 2282 </li> 2283 2284 <li class="menu-item"> 2285 2286 <a href="/car-crashes-weight-regression/05-clean-combine-and-filter-data/"> 2287 Clean combine and filter data 2288 </a> 2289 <div class="toc"></div> 2290 2291 2292 2293 </li> 2294 2295 </ol> 2296 </div> 2297 2298 </li> 2299 2300 <li class="menu-item"> 2301 2302 <a href="/propublica-opportunity-gap/"> 2303 <strong>ProPublica:</strong> Opportunity Gap 2304 </a> 2305 <div class="toc"></div> 2306 2307 2308 2309 </li> 2310 2311 <li class="menu-item"> 2312 2313 2314 2315 <input id="accordion-strongbostonglobestrongticketingbias" type="checkbox" name="accordion-checkbox" hidden=""> 2316 <label class="accordion-header c-hand" for="accordion-strongbostonglobestrongticketingbias"> 2317 <i class="icon icon-arrow-right mr-1"></i> 2318 <strong>Boston Globe:</strong> Ticketing bias 2319 </label> 2320 <div class="accordion-body"> 2321 <ol class="menu menu-nav"> 2322 2323 <li class="menu-item"> 2324 2325 <a href="/boston-globe-tickets/"> 2326 Summary 2327 </a> 2328 <div class="toc"></div> 2329 2330 2331 2332 </li> 2333 2334 <li class="menu-item"> 2335 2336 <a href="/boston-globe-tickets/boston-globe-ticketing-regression/"> 2337 Logistic regression for speeding tickets 2338 </a> 2339 <div class="toc"></div> 2340 2341 2342 2343 </li> 2344 2345 </ol> 2346 </div> 2347 2348 </li> 2349 2350 <li class="menu-item"> 2351 2352 <a href="/stanford-open-policing/"> 2353 <strong>Stanford:</strong> Open Policing Data 2354 </a> 2355 <div class="toc"></div> 2356 2357 2358 2359 </li> 2360 2361 <li class="menu-item"> 2362 2363 2364 2365 <input id="accordion-strongbuzzfeedstrongsurveillanceplanes" type="checkbox" name="accordion-checkbox" hidden=""> 2366 <label class="accordion-header c-hand" for="accordion-strongbuzzfeedstrongsurveillanceplanes"> 2367 <i class="icon icon-arrow-right mr-1"></i> 2368 <strong>BuzzFeed:</strong> Surveillance planes 2369 </label> 2370 <div class="accordion-body"> 2371 <ol class="menu menu-nav"> 2372 2373 <li class="menu-item"> 2374 2375 <a href="/buzzfeed-spy-planes/"> 2376 Summary 2377 </a> 2378 <div class="toc"></div> 2379 2380 2381 2382 </li> 2383 2384 <li class="menu-item"> 2385 2386 <a href="/buzzfeed-spy-planes/feature-engineering-buzzfeed-spy-planes/"> 2387 Feature engineering - BuzzFeed spy planes 2388 </a> 2389 <div class="toc"></div> 2390 2391 2392 2393 </li> 2394 2395 <li class="menu-item"> 2396 2397 <a href="/buzzfeed-spy-planes/drawing-flight-paths-on-maps-with-cartopy/"> 2398 Drawing flight paths on maps with cartopy 2399 </a> 2400 <div class="toc"></div> 2401 2402 2403 2404 </li> 2405 2406 <li class="menu-item"> 2407 2408 <a href="/buzzfeed-spy-planes/buzzfeed-surveillance-planes-random-forests/"> 2409 Finding surveillance planes using random forests 2410 </a> 2411 <div class="toc"></div> 2412 2413 2414 2415 </li> 2416 2417 </ol> 2418 </div> 2419 2420 </li> 2421 2422 <li class="menu-item"> 2423 2424 2425 2426 <input id="accordion-strongrevealstrongmortgagelendingbias" type="checkbox" name="accordion-checkbox" hidden=""> 2427 <label class="accordion-header c-hand" for="accordion-strongrevealstrongmortgagelendingbias"> 2428 <i class="icon icon-arrow-right mr-1"></i> 2429 <strong>Reveal:</strong> Mortgage lending bias 2430 </label> 2431 <div class="accordion-body"> 2432 <ol class="menu menu-nav"> 2433 2434 <li class="menu-item"> 2435 2436 <a href="/reveal-mortgages/"> 2437 Summary 2438 </a> 2439 <div class="toc"></div> 2440 2441 2442 2443 </li> 2444 2445 <li class="menu-item"> 2446 2447 <a href="/reveal-mortgages/reveal-mortgage-analysis-cleaning-and-combining-data/"> 2448 Cleaning and combining data for the Reveal Mortgage Analysis 2449 </a> 2450 <div class="toc"></div> 2451 2452 2453 2454 </li> 2455 2456 <li class="menu-item"> 2457 2458 <a href="/reveal-mortgages/reveal-mortgage-analysis-wild-formulas-in-statsmodels-using-patsy-short-version/"> 2459 Wild formulas in statsmodels using Patsy (short version) 2460 </a> 2461 <div class="toc"></div> 2462 2463 2464 2465 </li> 2466 2467 <li class="menu-item"> 2468 2469 <a href="/reveal-mortgages/reveal-mortgage-analysis-logistic-regression-using-statsmodels-formulas/"> 2470 Reveal Mortgage Analysis - Logistic Regression using statsmodels formulas 2471 </a> 2472 <div class="toc"></div> 2473 2474 2475 2476 </li> 2477 2478 <li class="menu-item"> 2479 2480 <a href="/reveal-mortgages/reveal-mortgage-analysis-logistic-regression/"> 2481 Reveal Mortgage Analysis - Logistic Regression 2482 </a> 2483 <div class="toc"></div> 2484 2485 2486 2487 </li> 2488 2489 </ol> 2490 </div> 2491 2492 </li> 2493 2494 <li class="menu-item"> 2495 2496 2497 2498 <input id="accordion-strongapmreportsstrongjuryselectionbias" type="checkbox" name="accordion-checkbox" hidden=""> 2499 <label class="accordion-header c-hand" for="accordion-strongapmreportsstrongjuryselectionbias"> 2500 <i class="icon icon-arrow-right mr-1"></i> 2501 <strong>APM Reports:</strong> Jury selection bias 2502 </label> 2503 <div class="accordion-body"> 2504 <ol class="menu menu-nav"> 2505 2506 <li class="menu-item"> 2507 2508 <a href="/apm-reports-jury-bias/"> 2509 Summary 2510 </a> 2511 <div class="toc"></div> 2512 2513 2514 2515 </li> 2516 2517 <li class="menu-item"> 2518 2519 <a href="/apm-reports-jury-bias/in-the-dark-combining-datasets-and-cleaning-the-data/"> 2520 Combining and cleaning the initial dataset 2521 </a> 2522 <div class="toc"></div> 2523 2524 2525 2526 </li> 2527 2528 <li class="menu-item"> 2529 2530 <a href="/apm-reports-jury-bias/in-the-dark-feature-selection-with-p-values/"> 2531 Picking what matters and what doesn't in a regression 2532 </a> 2533 <div class="toc"></div> 2534 2535 2536 2537 </li> 2538 2539 <li class="menu-item"> 2540 2541 <a href="/apm-reports-jury-bias/in-the-dark-jury-selection-regression-walkthrough/"> 2542 Analyzing data using statsmodels formulas 2543 </a> 2544 <div class="toc"></div> 2545 2546 2547 2548 </li> 2549 2550 <li class="menu-item"> 2551 2552 <a href="/apm-reports-jury-bias/in-the-dark-alternative-formula-methods/"> 2553 Alternative techniques with statsmodels formulas 2554 </a> 2555 <div class="toc"></div> 2556 2557 2558 2559 </li> 2560 2561 </ol> 2562 </div> 2563 2564 </li> 2565 2566 <li class="menu-item"> 2567 2568 2569 2570 <input id="accordion-strongreutersstrongasylumdenials" type="checkbox" name="accordion-checkbox" hidden=""> 2571 <label class="accordion-header c-hand" for="accordion-strongreutersstrongasylumdenials"> 2572 <i class="icon icon-arrow-right mr-1"></i> 2573 <strong>Reuters:</strong> Asylum denials 2574 </label> 2575 <div class="accordion-body"> 2576 <ol class="menu menu-nav"> 2577 2578 <li class="menu-item"> 2579 2580 <a href="/reuters-asylum/"> 2581 Summary 2582 </a> 2583 <div class="toc"></div> 2584 2585 2586 2587 </li> 2588 2589 <li class="menu-item"> 2590 2591 <a href="/reuters-asylum/cleaning-the-eoir-immigration-court-dataset/"> 2592 Preparing the EOIR immigration court data for analysis 2593 </a> 2594 <div class="toc"></div> 2595 2596 2597 2598 </li> 2599 2600 <li class="menu-item"> 2601 2602 <a href="/reuters-asylum/using-regression-to-analyze-asylum-cases/"> 2603 How nationality and judges affect your chance of asylum in immigration court 2604 </a> 2605 <div class="toc"></div> 2606 2607 2608 2609 </li> 2610 2611 </ol> 2612 </div> 2613 2614 </li> 2615 2616 <li class="menu-item"> 2617 2618 <a href="/propublica-pardons/"> 2619 <strong>ProPublica:</strong> Presidential pardons 2620 </a> 2621 <div class="toc"></div> 2622 2623 2624 2625 </li> 2626 2627 <li class="menu-item"> 2628 2629 2630 2631 <input id="accordion-strongpropublicastrongcriminalsentencing" t
2631ype="checkbox" name="accordion-checkbox" hidden=""> 2632 <label class="accordion-header c-hand" for="accordion-strongpropublicastrongcriminalsentencing"> 2633 <i class="icon icon-arrow-right mr-1"></i> 2634 <strong>ProPublica:</strong> Criminal sentencing 2635 </label> 2636 <div class="accordion-body"> 2637 <ol class="menu menu-nav"> 2638 2639 <li class="menu-item"> 2640 2641 <a href="/propublica-criminal-sentencing/"> 2642 Summary 2643 </a> 2644 <div class="toc"></div> 2645 2646 2647 2648 </li> 2649 2650 <li class="menu-item"> 2651 2652 <a href="/propublica-criminal-sentencing/week-5-1-machine-bias-class/"> 2653 Breaking down machine bias 2654 </a> 2655 <div class="toc"></div> 2656 2657 2658 2659 </li> 2660 2661 </ol> 2662 </div> 2663 2664 </li> 2665 2666 <li class="menu-item"> 2667 2668 <a href="/foia-predictor/"> 2669 <strong>data.world:</strong> The FOIA Predictor 2670 </a> 2671 <div class="toc"></div> 2672 2673 2674 2675 </li> 2676 2677 </ol> 2678 2679 </div> 2680 </div> 2681</div> 2682 </div> 2683 </div> 2684 </div> 2685
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