{"cells":[{"metadata":{"_uuid":"53a4db2a7a3c9b967be808aadafa0cd2473ce106"},"cell_type":"markdown","source":"## The goal of this kernel is to classify quora questions into 2 categories (sincere & insincere)\n### I will not be using word embedding here and will employ some classic NLP techniques and let's see what score we can achieve\n**(This is my first NLP attempt)**"},{"metadata":{"_uuid":"401ae7d7a0333a65276f987e53fb246e9ac15bd4"},"cell_type":"markdown","source":"# Importing Libraries"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport re\nimport string\nimport nltk\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom wordcloud import WordCloud, STOPWORDS","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"# Setting paths\npath_train = '../input/train.csv'\npath_test = '../input/test.csv'\npath_sample = '../input/sample_submission.csv'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"7b054f0c3db6f6373d5a58aa50fd619d18cfb900"},"cell_type":"code","source":"# Setting dsiplay options for pandas to only display 100 chars max\npd.set_option('display.max_colwidth', 100)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"d06d90158538b0279000d6c727df6da4244ca7ed"},"cell_type":"code","source":"# Now let's read in the files\ndf_train = pd.read_csv(path_train)\ndf_test = pd.read_csv(path_test)\nsample_sub = pd.read_csv(path_sample)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"2a13fa1947f75b386b7f57e64100adcc9026f0c5"},"cell_type":"markdown","source":"# Now let's do some EDA"},{"metadata":{"trusted":true,"_uuid":"240534f9652059bf93d514c4a02e2aa5d1482f4a"},"cell_type":"code","source":"df_train.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"b1643938040daee51470583e65f694929879da20"},"cell_type":"code","source":"df_train.sample(10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"1eeb179ed1979811fad03f02104b23814953e65f"},"cell_type":"code","source":"df_train.info()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"7c7c87f90d63601781daf6c700835a5784c0d87e"},"cell_type":"markdown","source":"Good news we have no missing values to deal with"},{"metadata":{"trusted":true,"_uuid":"a7d0c3e517c610bb2be6144fc0f3a53bb86b2685"},"cell_type":"code","source":"sns.countplot(x='target', data=df_train, hue='target')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"2691cc4b9970ecee016b217f8ae6c65e1e0ee17e"},"cell_type":"code","source":"(df_train[df_train['target'] == 1]['target'].count()/df_train['target'].count()) * 100","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"fa5b6770915b6ba8a33854001f87ea4a5dd4354c"},"cell_type":"markdown","source":"About 6% of the questions are insincere which indicates the dataset is highly imbalanced (hence using accuracy as a metric is meaningless)"},{"metadata":{"_uuid":"cc56fef7490b765d0c001a1cbdb22fc0b3586efc"},"cell_type":"markdown","source":"### Let's setup the stopwords"},{"metadata":{"trusted":true,"_uuid":"c483266fe2bac7b6d4bf09524e71357c1bd680c2"},"cell_type":"code","source":"# stopwords = nltk.corpus.stopwords.words('english')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"7bfb66ee3a7b045737950c8c9c8381db35c232f3"},"cell_type":"code","source":"stopwords = [\"af\",\"'n\",\"aan\",\"af\",\"al\",\"as\",\"baie\",\"by\",\"daar\",\"dag\",\"dat\",\"die\",\"dit\",\"een\",\"ek\",\n             \"en\",\"gaan\",\"gesê\",\"haar\",\"het\",\"hom\",\"hulle\",\"hy\",\"in\",\"is\",\"jou\",\"jy\",\"kan\",\"kom\",\"ma\",\n             \"maar\",\"met\",\"my\",\"na\",\"nie\",\"om\",\"ons\",\"op\",\"saam\",\"sal\",\"se\",\"sien\",\"so\",\"sy\",\"te\",\"toe\",\"uit\",\n             \"van\",\"vir\",\"was\",\"wat\",\"ʼn\",\"ha\",\"a\",\"amma\",\"ba\",\"ban\",\"ce\",\"cikin\",\"da\",\"don\",\"ga\",\"in\",\"ina\",\"ita\",\"ji\",\n             \"ka\",\"ko\",\"kuma\",\"lokacin\",\"ma\",\"mai\",\"na\",\"ne\",\"ni\",\"sai\",\"shi\",\"su\",\"suka\",\"sun\",\"ta\",\"tafi\",\"take\",\"tana\",\n             \"wani\",\"wannan\",\"wata\",\"ya\",\"yake\",\"yana\",\"yi\",\"za\",\"so\",\"aad\",\"albaabkii\",\"atabo\",\"ay\",\"ayaa\",\"ayee\",\"ayuu\",\n             \"dhan\",\"hadana\",\"in\",\"inuu\",\"isku\",\"jiray\",\"jirtay\",\"ka\",\"kale\",\"kasoo\",\"ku\",\"kuu\",\"lakin\",\"markii\",\"oo\",\"si\",\n             \"soo\",\"uga\",\"ugu\",\"uu\",\"waa\",\"waxa\",\"waxuu\",\"st\",\"a\",\"ba\",\"bane\",\"bona\",\"e\",\"ea\",\"eaba\",\"empa\",\"ena\",\"ha\",\"hae\",\n             \"hape\",\"ho\",\"hore\",\"ka\",\"ke\",\"la\",\"le\",\"li\",\"me\",\"mo\",\"moo\",\"ne\",\"o\",\"oa\",\"re\",\"sa\",\"se\",\"tloha\",\"tsa\",\"tse\",\"sw\",\n             \"akasema\",\"alikuwa\",\"alisema\",\"baada\",\"basi\",\"bila\",\"cha\",\"chini\",\"hadi\",\"hapo\",\"hata\",\"hivyo\",\"hiyo\",\"huku\",\"huo\",\n             \"ili\",\"ilikuwa\",\"juu\",\"kama\",\"karibu\",\"katika\",\"kila\",\"kima\",\"kisha\",\"kubwa\",\"kutoka\",\"kuwa\",\"kwa\",\"kwamba\",\"kwenda\",\n             \"kwenye\",\"la\",\"lakini\",\"mara\",\"mdogo\",\"mimi\",\"mkubwa\",\"mmoja\",\"moja\",\"muda\",\"mwenye\",\"na\",\"naye\",\"ndani\",\"ng\",\"ni\",\n             \"nini\",\"nonkungu\",\"pamoja\",\"pia\",\"sana\",\"sasa\",\"sauti\",\"tafadhali\",\"tena\",\"tu\",\"vile\",\"wa\",\"wakati\",\"wake\",\"walikuwa\",\n             \"wao\",\"watu\",\"wengine\",\"wote\",\"ya\",\"yake\",\"yangu\",\"yao\",\"yeye\",\"yule\",\"za\",\"zaidi\",\"zake\",\"yo\",\"a\",\"an\",\"bá\",\"bí\",\n             \"bẹ̀rẹ̀\",\"fún\",\"fẹ́\",\"gbogbo\",\"inú\",\"jù\",\"jẹ\",\"jẹ́\",\"kan\",\"kì\",\"kí\",\"kò\",\"láti\",\"lè\",\"lọ\",\"mi\",\"mo\",\"máa\",\"mọ̀\",\"ni\",\n             \"náà\",\"ní\",\"nígbà\",\"nítorí\",\"nǹkan\",\"o\",\"padà\",\"pé\",\"púpọ̀\",\"pẹ̀lú\",\"rẹ̀\",\"sì\",\"sí\",\"sínú\",\"ṣ\",\"ti\",\"tí\",\"wà\",\"wá\",\n             \"wọn\",\"wọ́n\",\"yìí\",\"àti\",\"àwọn\",\"é\",\"í\",\"òun\",\"ó\",\"ń\",\"ńlá\",\"ṣe\",\"ṣé\",\"ṣùgbọ́n\",\"ẹmọ́\",\"ọjọ́\",\"ọ̀pọ̀lọpọ̀\",\"zu\",\"futhi\",\n             \"kahle\",\"kakhulu\",\"kanye\",\"khona\",\"kodwa\",\"kungani\",\"kusho\",\"la\",\"lakhe\",\"lapho\",\"mina\",\"ngesikhathi\",\"nje\",\"phansi\",\n             \"phezulu\",\"u\",\"ukuba\",\"ukuthi\",\"ukuze\",\"uma\",\"wahamba\",\"wakhe\",\"wami\",\"wase\",\"wathi\",\"yakhe\",\"zakhe\",\"zonke\",\"da\",\"af\",\n             \"alle\",\"andet\",\"andre\",\"at\",\"begge\",\"da\",\"de\",\"den\",\"denne\",\"der\",\"deres\",\"det\",\"dette\",\"dig\",\"din\",\"dog\",\"du\",\"ej\",\n             \"eller\",\"en\",\"end\",\"ene\",\"eneste\",\"enhver\",\"et\",\"fem\",\"fire\",\"flere\",\"fleste\",\"for\",\"fordi\",\"forrige\",\"fra\",\"få\",\"før\",\n             \"god\",\"han\",\"hans\",\"har\",\"hendes\",\"her\",\"hun\",\"hvad\",\"hvem\",\"hver\",\"hvilken\",\"hvis\",\"hvor\",\"hvordan\",\"hvorfor\",\n             \"hvornår\",\"i\",\"ikke\",\"ind\",\"ingen\",\"intet\",\"jeg\",\"jeres\",\"kan\",\"kom\",\"kommer\",\"lav\",\"lidt\",\"lille\",\"man\",\"mand\",\n             \"mange\",\"med\",\"meget\",\"men\",\"mens\",\"mere\",\"mig\",\"ned\",\"ni\",\"nogen\",\"noget\",\"ny\",\"nyt\",\"nær\",\"næste\",\"næsten\",\"og\",\n             \"op\",\"otte\",\"over\",\"på\",\"se\",\"seks\",\"ses\",\"som\",\"stor\",\"store\",\"syv\",\"ti\",\"til\",\"to\",\"tre\",\"ud\",\"var\",\"de\",\"Ernst\",\n             \"Ordnung\",\"Schluss\",\"a\",\"ab\",\"aber\",\"ach\",\"acht\",\"achte\",\"achten\",\"achter\",\"achtes\",\"ag\",\"alle\",\"allein\",\"allem\",\n             \"allen\",\"aller\",\"allerdings\",\"alles\",\"allgemeinen\",\"als\",\"also\",\"am\",\"an\",\"andere\",\"anderen\",\"andern\",\"anders\",\"au\",\n             \"auch\",\"auf\",\"aus\",\"ausser\",\"ausserdem\",\"außer\",\"außerdem\",\"b\",\"bald\",\"bei\",\"beide\",\"beiden\",\"beim\",\"beispiel\",\n             \"bekannt\",\"bereits\",\"besonders\",\"besser\",\"besten\",\"bin\",\"bis\",\"bisher\",\"bist\",\"c\",\"d\",\"d.h\",\"da\",\"dabei\",\"dadurch\",\n             \"dafür\",\"dagegen\",\"daher\",\"dahin\",\"dahinter\",\"damals\",\"damit\",\"danach\",\"daneben\",\"dank\",\"dann\",\"daran\",\"darauf\",\n             \"daraus\",\"darf\",\"darfst\",\"darin\",\"darum\",\"darunter\",\"darüber\",\"das\",\"dasein\",\"daselbst\",\"dass\",\"dasselbe\",\"davon\",\n             \"davor\",\"dazu\",\"dazwischen\",\"daß\",\"dein\",\"deine\",\"deinem\",\"deiner\",\"dem\",\"dementsprechend\",\"demgegenüber\",\"demgemäss\",\n             \"demgemäß\",\"demselben\",\"demzufolge\",\"den\",\"denen\",\"denn\",\"denselben\",\"der\",\"deren\",\"derjenige\",\"derjenigen\",\n             \"dermassen\",\"dermaßen\",\"derselbe\",\"derselben\",\"des\",\"deshalb\",\"desselben\",\"dessen\",\"deswegen\",\"dich\",\"die\",\n             \"diejenige\",\"diejenigen\",\"dies\",\"diese\",\"dieselbe\",\"dieselben\",\"diesem\",\"diesen\",\"dieser\",\"dieses\",\"dir\",\n             \"doch\",\"dort\",\"drei\",\"drin\",\"dritte\",\"dritten\",\"dritter\",\"drittes\",\"du\",\"durch\",\"durchaus\",\"durfte\",\"durften\",\n             \"dürfen\",\"dürft\",\"e\",\"eben\",\"ebenso\",\"ehrlich\",\"ei\",\"ei,\",\"eigen\",\"eigene\",\"eigenen\",\"eigener\",\"eigenes\",\"ein\",\n             \"einander\",\"eine\",\"einem\",\"einen\",\"einer\",\"eines\",\"einige\",\"einigen\",\"einiger\",\"einiges\",\"einmal\",\"eins\",\"elf\",\n             \"en\",\"ende\",\"endlich\",\"entweder\",\"er\",\"erst\",\"erste\",\"ersten\",\"erster\",\"erstes\",\"es\",\"etwa\",\"etwas\",\"euch\",\"euer\",\n             \"eure\",\"f\",\"folgende\",\"früher\",\"fünf\",\"fünfte\",\"fünften\",\"fünfter\",\"fünftes\",\"für\",\"g\",\"gab\",\"ganz\",\"ganze\",\"ganzen\",\n             \"ganzer\",\"ganzes\",\"gar\",\"gedurft\",\"gegen\",\"gegenüber\",\"gehabt\",\"gehen\",\"geht\",\"gekannt\",\"gekonnt\",\"gemacht\",\"gemocht\",\n             \"gemusst\",\"genug\",\"gerade\",\"gern\",\"gesagt\",\"geschweige\",\"gewesen\",\"gewollt\",\"geworden\",\"gibt\",\"ging\",\"gleich\",\"gott\",\n             \"gross\",\"grosse\",\"grossen\",\"grosser\",\"grosses\",\"groß\",\"große\",\"großen\",\"großer\",\"großes\",\"gut\",\"gute\",\"guter\",\"gutes\",\n             \"h\",\"habe\",\"haben\",\"habt\",\"hast\",\"hat\",\"hatte\",\"hatten\",\"hattest\",\"hattet\",\"heisst\",\"her\",\"heute\",\"hier\",\"hin\",\n             \"hinter\",\"hoch\",\"hätte\",\"hätten\",\"i\",\"ich\",\"ihm\",\"ihn\",\"ihnen\",\"ihr\",\"ihre\",\"ihrem\",\"ihren\",\"ihrer\",\"ihres\",\"im\",\n             \"immer\",\"in\",\"indem\",\"infolgedessen\",\"ins\",\"irgend\",\"ist\",\"j\",\"ja\",\"jahr\",\"jahre\",\"jahren\",\"je\",\"jede\",\"jedem\",\n             \"jeden\",\"jeder\",\"jedermann\",\"jedermanns\",\"jedes\",\"jedoch\",\"jemand\",\"jemandem\",\"jemanden\",\"jene\",\"jenem\",\"jenen\",\n             \"jener\",\"jenes\",\"jetzt\",\"k\",\"kam\",\"kann\",\"kannst\",\"kaum\",\"kein\",\"keine\",\"keinem\",\"keinen\",\"keiner\",\"kleine\",\n             \"kleinen\",\"kleiner\",\"kleines\",\"kommen\",\"kommt\",\"konnte\",\"konnten\",\"kurz\",\"können\",\"könnt\",\"könnte\",\"l\",\"lang\",\n             \"lange\",\"leicht\",\"leide\",\"lieber\",\"los\",\"m\",\"machen\",\"macht\",\"machte\",\"mag\",\"magst\",\"mahn\",\"mal\",\"man\",\"manche\",\n             \"manchem\",\"manchen\",\"mancher\",\"manches\",\"mann\",\"mehr\",\"mein\",\"meine\",\"meinem\",\"meinen\",\"meiner\",\"meines\",\"mensch\",\n             \"menschen\",\"mich\",\"mir\",\"mit\",\"mittel\",\"mochte\",\"mochten\",\"morgen\",\"muss\",\"musst\",\"musste\",\"mussten\",\"muß\",\"mußt\",\n             \"möchte\",\"mögen\",\"möglich\",\"mögt\",\"müssen\",\"müsst\",\"müßt\",\"n\",\"na\",\"nach\",\"nachdem\",\"nahm\",\"natürlich\",\"neben\",\n             \"nein\",\"neue\",\"neuen\",\"neun\",\"neunte\",\"neunten\",\"neunter\",\"neuntes\",\"nicht\",\"nichts\",\"nie\",\"niemand\",\"niemandem\",\n             \"niemanden\",\"noch\",\"nun\",\"nur\",\"o\",\"ob\",\"oben\",\"oder\",\"offen\",\"oft\",\"ohne\",\"p\",\"q\",\"r\",\"recht\",\"rechte\",\"rechten\",\n             \"rechter\",\"rechtes\",\"richtig\",\"rund\",\"s\",\"sa\",\"sache\",\"sagt\",\"sagte\",\"sah\",\"satt\",\"schlecht\",\"schon\",\"sechs\",\n             \"sechste\",\"sechsten\",\"sechster\",\"sechstes\",\"sehr\",\"sei\",\"seid\",\"seien\",\"sein\",\"seine\",\"seinem\",\"seinen\",\"seiner\",\n             \"seines\",\"seit\",\"seitdem\",\"selbst\",\"sich\",\"sie\",\"sieben\",\"siebente\",\"siebenten\",\"siebenter\",\"siebentes\",\"sind\",\n             \"so\",\"solang\",\"solche\",\"solchem\",\"solchen\",\"solcher\",\"solches\",\"soll\",\"sollen\",\"sollst\",\"sollt\",\"sollte\",\n             \"sollten\",\"sondern\",\"sonst\",\"soweit\",\"sowie\",\"später\",\"startseite\",\"statt\",\"steht\",\"suche\",\"t\",\"tag\",\"tage\",\n             \"tagen\",\"tat\",\"teil\",\"tel\",\"tritt\",\"trotzdem\",\"tun\",\"u\",\"uhr\",\"um\",\"und\",\"und?\",\"uns\",\"unser\",\"unsere\",\"unserer\",\n             \"unter\",\"v\",\"vergangenen\",\"viel\",\"viele\",\"vielem\",\"vielen\",\"vielleicht\",\"vier\",\"vierte\",\"vierten\",\"vierter\",\n             \"viertes\",\"vom\",\"von\",\"vor\",\"w\",\"wahr?\",\"wann\",\"war\",\"waren\",\"wart\",\"warum\",\"was\",\"wegen\",\"weil\",\"weit\",\"weiter\",\n             \"weitere\",\"weiteren\",\"weiteres\",\"welche\",\"welchem\",\"welchen\",\"welcher\",\"welches\",\"wem\",\"wen\",\"wenig\",\"wenige\",\n             \"weniger\",\"weniges\",\"wenigstens\",\"wenn\",\"wer\",\"werde\",\"werden\",\"werdet\",\"weshalb\",\"wessen\",\"wie\",\"wieder\",\n             \"wieso\",\"will\",\"willst\",\"wir\",\"wird\",\"wirklich\",\"wirst\",\"wissen\",\"wo\",\"wohl\",\"wollen\",\"wollt\",\"wollte\",\n             \"wollten\",\"worden\",\"wurde\",\"wurden\",\"während\",\"währenddem\",\"währenddessen\",\"wäre\",\"würde\",\"würden\",\n             \"x\",\"y\",\"z\",\"z.b\",\"zehn\",\"zehnte\",\"zehnten\",\"zehnter\",\"zehntes\",\"zeit\",\"zu\",\"zuerst\",\"zugleich\",\"zum\",\"zunächst\",\n             \"zur\",\"zurück\",\"zusammen\",\"zwanzig\",\"zwar\",\"zwei\",\"zweite\",\"zweiten\",\"zweiter\",\"zweites\",\"zwischen\",\"zwölf\",\n             \"über\",\"überhaupt\",\"übrigens\",\"es\",\"a\",\"actualmente\",\"acuerdo\",\"adelante\",\"ademas\",\"además\",\"adrede\",\"afirmó\",\n             \"agregó\",\"ahi\",\"ahora\",\"ahí\",\"al\",\"algo\",\"alguna\",\"algunas\",\"alguno\",\"algunos\",\"algún\",\"alli\",\"allí\",\"alrededor\",\n             \"ambos\",\"ampleamos\",\"antano\",\"antaño\",\"ante\",\"anterior\",\"antes\",\"apenas\",\"aproximadamente\",\"aquel\",\"aquella\",\n             \"aquellas\",\"aquello\",\"aquellos\",\"aqui\",\"aquél\",\"aquélla\",\"aquéllas\",\"aquéllos\",\"aquí\",\"arriba\",\"arribaabajo\",\n             \"aseguró\",\"asi\",\"así\",\"atras\",\"aun\",\"aunque\",\"ayer\",\"añadió\",\"aún\",\"b\",\"bajo\",\"bastante\",\"bien\",\"breve\",\"buen\",\n             \"buena\",\"buenas\",\"bueno\",\"buenos\",\"c\",\"cada\",\"casi\",\"cerca\",\"cierta\",\"ciertas\",\"cierto\",\"ciertos\",\"cinco\",\"claro\",\n             \"comentó\",\"como\",\"con\",\"conmigo\",\"conocer\",\"conseguimos\",\"conseguir\",\"considera\",\"consideró\",\"consigo\",\"consigue\",\n             \"consiguen\",\"consigues\",\"contigo\",\"contra\",\"cosas\",\"creo\",\"cual\",\"cuales\",\"cualquier\",\"cuando\",\"cuanta\",\"cuantas\",\n             \"cuanto\",\"cuantos\",\"cuatro\",\"cuenta\",\"cuál\",\"cuáles\",\"cuándo\",\"cuánta\",\"cuántas\",\"cuánto\",\"cuántos\",\"cómo\",\"d\",\"da\",\n             \"dado\",\"dan\",\"dar\",\"de\",\"debajo\",\"debe\",\"deben\",\"debido\",\"decir\",\"dejó\",\"del\",\"delante\",\"demasiado\",\"demás\",\"dentro\",\n             \"deprisa\",\"desde\",\"despacio\",\"despues\",\"después\",\"detras\",\"detrás\",\"dia\",\"dias\",\"dice\",\"dicen\",\"dicho\",\"dieron\",\n             \"diferente\",\"diferentes\",\"dijeron\",\"dijo\",\"dio\",\"donde\",\"dos\",\"durante\",\"día\",\"días\",\"dónde\",\"e\",\"ejemplo\",\"el\",\n             \"ella\",\"ellas\",\"ello\",\"ellos\",\"embargo\",\"empleais\",\"emplean\",\"emplear\",\"empleas\",\"empleo\",\"en\",\"encima\",\"encuentra\",\n             \"enfrente\",\"enseguida\",\"entonces\",\"entre\",\"era\",\"eramos\",\"eran\",\"eras\",\"eres\",\"es\",\"esa\",\"esas\",\"ese\",\"eso\",\"esos\",\n             \"esta\",\"estaba\",\"estaban\",\"estado\",\"estados\",\"estais\",\"estamos\",\"estan\",\"estar\",\"estará\",\"estas\",\"este\",\"esto\",\n             \"estos\",\"estoy\",\"estuvo\",\"está\",\"están\",\"ex\",\"excepto\",\"existe\",\"existen\",\"explicó\",\"expresó\",\"f\",\"fin\",\"final\",\n             \"fue\",\"fuera\",\"fueron\",\"fui\",\"fuimos\",\"g\",\"general\",\"gran\",\"grandes\",\"gueno\",\"h\",\"ha\",\"haber\",\"habia\",\"habla\",\n             \"hablan\",\"habrá\",\"había\",\"habían\",\"hace\",\"haceis\",\"hacemos\",\"hacen\",\"hacer\",\"hacerlo\",\"haces\",\"hacia\",\"haciendo\",\n             \"hago\",\"han\",\"hasta\",\"hay\",\"haya\",\"he\",\"hecho\",\"hemos\",\"hicieron\",\"hizo\",\"horas\",\"hoy\",\"hubo\",\"i\",\"igual\",\"incluso\",\n             \"indicó\",\"informo\",\"informó\",\"intenta\",\"intentais\",\"intentamos\",\"intentan\",\"intentar\",\"intentas\",\"intento\",\"ir\",\"j\",\n             \"junto\",\"k\",\"l\",\"la\",\"lado\",\"largo\",\"las\",\"le\",\"lejos\",\"les\",\"llegó\",\"lleva\",\"llevar\",\"lo\",\"los\",\"luego\",\"lugar\",\"m\",\n             \"mal\",\"manera\",\"manifestó\",\"mas\",\"mayor\",\"me\",\"mediante\",\"medio\",\"mejor\",\"mencionó\",\"menos\",\"menudo\",\"mi\",\"mia\",\n             \"mias\",\"mientras\",\"mio\",\"mios\",\"mis\",\"misma\",\"mismas\",\"mismo\",\"mismos\",\"modo\",\"momento\",\"mucha\",\"muchas\",\"mucho\",\n             \"muchos\",\"muy\",\"más\",\"mí\",\"mía\",\"mías\",\"mío\",\"míos\",\"n\",\"nada\",\"nadie\",\"ni\",\"ninguna\",\"ningunas\",\"ninguno\",\n             \"ningunos\",\"ningún\",\"no\",\"nos\",\"nosotras\",\"nosotros\",\"nuestra\",\"nuestras\",\"nuestro\",\"nuestros\",\"nueva\",\"nuevas\",\n             \"nuevo\",\"nuevos\",\"nunca\",\"o\",\"ocho\",\"os\",\"otra\",\"otras\",\"otro\",\"otros\",\"p\",\"pais\",\"para\",\"parece\",\"parte\",\n             \"partir\",\"pasada\",\"pasado\",\"paìs\",\"peor\",\"pero\",\"pesar\",\"poca\",\"pocas\",\"poco\",\"pocos\",\"podeis\",\"podemos\",\"poder\",\n             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            ,\"one\",\"ones\",\n             \"only\",\"onto\",\"or\",\"other\",\"others\",\"otherwise\",\"ought\",\"our\",\"ours\",\"ourselves\",\"out\",\"outside\",\"over\",\"overall\",\"own\",\"p\",\"particular\",\"particularly\",\"per\",\"perhaps\",\"placed\",\"please\",\"plus\",\"possible\",\"presumably\",\"probably\",\"provides\",\"q\",\"que\",\"quite\",\"qv\",\"r\",\"rather\",\"rd\",\"re\",\"really\",\"reasonably\",\"regarding\",\"regardless\",\"regards\",\"relatively\",\"respectively\",\"right\",\"s\",\"said\",\"same\",\"saw\",\"say\",\"saying\",\"says\",\"second\",\"secondly\",\"see\",\"seeing\",\"seem\",\"seemed\",\"seeming\",\"seems\",\"seen\",\"self\",\"selves\",\"sensible\",\"sent\",\"serious\",\"seriously\",\"seven\",\"several\",\"shall\",\"she\",\"should\",\"shouldn't\",\"since\",\"six\",\"so\",\"some\",\"somebody\",\"somehow\",\"someone\",\"something\",\"sometime\",\"sometimes\",\"somewhat\",\"somewhere\",\"soon\",\"sorry\",\"specified\",\"specify\",\"specifying\",\"still\",\"sub\",\"such\",\"sup\",\"sure\",\"t\",\"t's\",\"take\",\"taken\",\"tell\",\"tends\",\"th\",\"than\",\"thank\",\"thanks\",\"thanx\",\"that\",\"that's\",\"thats\",\"the\",\"their\",\"theirs\",\"them\",\"themselves\",\"then\",\"thence\",\"there\",\"there's\",\"thereafter\",\"thereby\",\"therefore\",\"therein\",\"theres\",\"thereupon\",\"these\",\"they\",\"they'd\",\"they'll\",\"they're\",\"they've\",\"think\",\"third\",\"this\",\"thorough\",\"thoroughly\",\"those\",\"though\",\"three\",\"through\",\"throughout\",\"thru\",\"thus\",\"to\",\"together\",\"too\",\"took\",\"toward\",\"towards\",\"tried\",\"tries\",\"truly\",\"try\",\"trying\",\"twice\",\"two\",\"u\",\"un\",\"under\",\"unfortunately\",\"unless\",\"unlikely\",\"until\",\"unto\",\"up\",\"upon\",\"us\",\"use\",\"used\",\"useful\",\"uses\",\"using\",\"usually\",\"uucp\",\"v\",\"value\",\"various\",\"very\",\"via\",\"viz\",\"vs\",\"w\",\"want\",\"wants\",\"was\",\"wasn't\",\"way\",\"we\",\"we'd\",\"we'll\",\"we're\",\"we've\",\"welcome\",\"well\",\"went\",\"were\",\"weren't\",\"what\",\"what's\",\"whatever\",\"when\",\"whence\",\"whenever\",\"where\",\"where's\",\"whereafter\",\"whereas\",\"whereby\",\"wherein\",\"whereupon\",\"wherever\",\"whether\",\"which\",\"while\",\"whither\",\"who\",\"who's\",\"whoever\",\"whole\",\"whom\",\"whose\",\"why\",\"will\",\"willing\",\"wish\",\"with\",\"within\",\"without\",\"won't\",\"wonder\",\"would\",\"wouldn't\",\"x\",\"y\",\"yes\",\"yet\",\"you\",\"you'd\",\"you'll\",\"you're\",\"you've\",\"your\",\"yours\",\"yourself\",\"yourselves\",\"z\",\"zero\"]\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"2f17c9a4de7e317a161b8d14253fe0d71f4d4951"},"cell_type":"code","source":"len(stopwords)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"scrolled":true,"_uuid":"c51dc9afa9f7045821fa9c8b230a1156fde9b729"},"cell_type":"code","source":"stopwords=set(stopwords)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"0f934018525be0e4f512fa1982a3f8bedb6a9d37"},"cell_type":"code","source":"len(stopwords)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"30540e5583584a22ce720446e892067c95e25e97"},"cell_type":"markdown","source":"### Let's look at most frequent words where questions are insincere"},{"metadata":{"trusted":true,"_uuid":"550ba73c37acc2a9c61dea52738140de9799e8dd"},"cell_type":"code","source":"questions = ' '.join(question for question in df_train['question_text'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"7979278eea331dbb278db810a11ef20b000e4596"},"cell_type":"code","source":"wordcloud = WordCloud(stopwords = stopwords).generate(questions)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"66d344f28218de3348a24afbf820ecd2c691d4b5"},"cell_type":"code","source":"plt.figure(figsize = (10,10))\nplt.imshow(wordcloud, interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"77fc80ae2304d9342f43d4e48300822a2e909b9e"},"cell_type":"code","source":"insincere_questions = ' '.join(question for question in df_train['question_text'][df_train['target']==1])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"2d2de49630b5c28c0efea816aa8d9646e5e7c2b3"},"cell_type":"code","source":"wordcloud = WordCloud(stopwords = stopwords).generate(insincere_questions)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"scrolled":true,"_uuid":"f0f0e45240bebb3153722f76ea0a28fd24250cd9"},"cell_type":"code","source":"plt.figure(figsize = (10,10))\nplt.imshow(wordcloud, interpolation='bilinear')\nplt.axis(\"off\")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"67d30ed7627b3eed8f3a0a5631a2212f97a5189b"},"cell_type":"markdown","source":"### Stemmer & lemmatizer"},{"metadata":{"trusted":true,"_uuid":"0b58b67a53425144d953a02c2ae63df3393bc699"},"cell_type":"code","source":"ps = nltk.PorterStemmer()\nlm = nltk.WordNetLemmatizer()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"fe8cb4160e6abf65655c8049e4d9a1471fecc983"},"cell_type":"markdown","source":"### Now we will define a function to clean the text by:\n1. Removing all punctuation and returning all words as lower case\n2. Splitting each setence into tokens (separate words)\n3. lemmatizing all words & removing stopwords"},{"metadata":{"trusted":true,"_uuid":"5e4be28143721bc069f537ffbe1152c182c15f8f"},"cell_type":"code","source":"def clean_text(text):\n    text = ''.join([word.lower() for word in text if word not in string.punctuation])\n    tokens = re.split('\\W+', text)\n    text = [lm.lemmatize(word) for word in tokens if word not in stopwords]\n    return text","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"scrolled":false,"_uuid":"7dd34d03ee9e2e9a7e0e7ccade87ed7d0c28b925"},"cell_type":"code","source":"df_train['clean_text'] = df_train['question_text'].apply(lambda x: clean_text(x))","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"e4153169e6d5cfdd1c9f253bb2dd0d0d58a25ef3"},"cell_type":"markdown","source":"## Feature Engineering"},{"metadata":{"_uuid":"b945cf03c8293139953ed792edb22b24cf8de266"},"cell_type":"markdown","source":"### Now we will define 3 functions:\n1. To count punctuation characters\n2. To count uppercase characters\n3. To count no. of swear words + common terms used in insincere questions in the context of:\n    * Race\n    * Color\n    * Religion"},{"metadata":{"trusted":true,"_uuid":"ebe0a45557107c40653b54a6e8bd86b2275b3e48"},"cell_type":"code","source":"def count_punct(text):\n    count = sum([1 for char in text if char in string.punctuation])\n    return(round(count/(len(text)-text.count(' ')), 3) * 100)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"eade0dd5e563d903e5e2542e49e923f2c20134a9"},"cell_type":"code","source":"def count_uppercase(text):\n    count = sum([1 for char in text if char.isupper()])\n    return(round(count/(len(text)-text.count(' ')), 3) * 100)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"e6b6d67ab3e99d715ff2037fe70dbd95416c3b44"},"cell_type":"code","source":"swear_words = [\"word\",\"anal\",\"anus\",\"arse\",\"ass\",\"assfuck\",\"ass hole\",\"assfucker\",\"asshole\",\n               \"assshole\",\"bastard\",\"bitch\",\"black cock\",\"bloody hell\",\"boong\",\"cock\",\"cockfucker\",\"cocksuck\",\n               \"cocksucker\",\"coon\",\"coonnass\",\"crap\",\"cunt\",\"cyberfuck\",\"damn\",\"darn\",\"dick\",\"dirty\",\"douche\",\"dummy\",\n               \"erect\",\"erection\",\"erotic\",\"escort\",\"fag\",\"faggot\",\"fuck\",\"Fuck off\",\"fuck you\",\"fuckass\",\"fuckhole\",\"god damn\",\n               \"gook\",\"hard core\",\"hardcore\",\"homoerotic\",\"hore\",\"lesbian\",\"lesbians\",\"mother fucker\",\"motherfuck\",\"motherfucker\",\n               \"negro\",\"nigger\",\"orgasim\",\"orgasm\",\"penis\",\"penisfucker\",\"piss\",\"piss off\",\"porn\",\"porno\",\"pornography\",\"pussy\",\"retard\",\n               \"sadist\",\"sex\",\"sexy\",\"shit\",\"slut\",\"son of a bitch\",\"suck\",\"tits\",\"viagra\",\"whore\",\"xxx”,2 girls 1 cup\",\"2g1c\",\"4r5e\",\"5h1t\",\n               \"5hit\",\"a$$\",\"a$$hole\",\"a_s_s\",\"a2m\",\"a54\",\"a55\",\"a55hole\",\"acrotomophilia\",\"aeolus\",\"ahole\",\"alabama hot pocket\",\"alaskan pipeline\",\n               \"anal\",\"anal impaler\",\"anal leakage\",\"analprobe\",\"anilingus\",\"anus\",\"apeshit\",\"ar5e\",\"areola\",\"areole\",\"arian\",\"arrse\",\"arse\",\"arsehole\",\n               \"aryan\",\"ass\",\"ass fuck\",\"ass fuck\",\"ass hole\",\"assbag\",\"assbandit\",\"assbang\",\"assbanged\",\"assbanger\",\"assbangs\",\"assbite\",\"assclown\",\"asscock\",\n               \"asscracker\",\"asses\",\"assface\",\"assfaces\",\"assfuck\",\"assfucker\",\"ass-fucker\",\"assfukka\",\"assgoblin\",\"assh0le\",\"asshat\",\"ass-hat\",\"asshead\",\n               \"assho1e\",\"asshole\",\"assholes\",\"asshopper\",\"ass-jabber\",\"assjacker\",\"asslick\",\"asslicker\",\"assmaster\",\"assmonkey\",\"assmucus\",\"assmucus\",\n               \"assmunch\",\"assmuncher\",\"assnigger\",\"asspirate\",\"ass-pirate\",\"assshit\",\"assshole\",\"asssucker\",\"asswad\",\"asswhole\",\"asswipe\",\"asswipes\",\n               \"auto erotic\",\"autoerotic\",\"axwound\",\"azazel\",\"azz\",\"b!tch\",\"b00bs\",\"b17ch\",\"b1tch\",\"babeland\",\"baby batter\",\"baby juice\",\"ball gag\",\n               \"ball gravy\",\"ball kicking\",\"ball licking\",\"ball sack\",\"ball sucking\",\"ballbag\",\"balls\",\"ballsack\",\"bampot\",\"bang (one's) box\",\"bangbros\",\n               \"bareback\",\"barely legal\",\"barenaked\",\"barf\",\"bastard\",\"bastardo\",\"bastards\",\"bastinado\",\"batty boy\",\"bawdy\",\"bbw\",\"bdsm\",\"beaner\",\"beaners\",\n               \"beardedclam\",\"beastial\",\"beastiality\",\"beatch\",\"beaver\",\"beaver cleaver\",\"beaver lips\",\"beef curtain\",\"beef curtain\",\"beef curtains\",\"beeyotch\",\n               \"bellend\",\"bender\",\"beotch\",\"bescumber\",\"bestial\",\"bestiality\",\"bi+ch\",\"biatch\",\"big black\",\"big breasts\",\"big knockers\",\"big tits\",\"bigtits\",\n               \"bimbo\",\"bimbos\",\"bint\",\"birdlock\",\"bitch\",\"bitch tit\",\"bitch tit\",\"bitchass\",\"bitched\",\"bitcher\",\"bitchers\",\"bitches\",\"bitchin\",\"bitching\",\n               \"bitchtits\",\"bitchy\",\"black cock\",\"blonde action\",\"blonde on blonde action\",\"bloodclaat\",\"bloody\",\"bloody hell\",\"blow job\",\"blow me\",\"blow mud\",\n               \"blow your load\",\"blowjob\",\"blowjobs\",\"blue waffle\",\"blue waffle\",\"blumpkin\",\"blumpkin\",\"bod\",\"bodily\",\"boink\",\"boiolas\",\"bollock\",\"bollocks\",\n               \"bollok\",\"bollox\",\"bondage\",\"boned\",\"boner\",\"boners\",\"bong\",\"boob\",\"boobies\",\"boobs\",\"booby\",\"booger\",\"bookie\",\"boong\",\"booobs\",\"boooobs\",\n               \"booooobs\",\"booooooobs\",\"bootee\",\"bootie\",\"booty\",\"booty call\",\"booze\",\"boozer\",\"boozy\",\"bosom\",\"bosomy\",\"breasts\",\"Breeder\",\"brotherfucker\",\n               \"brown showers\",\"brunette action\",\"buceta\",\"bugger\",\"bukkake\",\"bull shit\",\"bulldyke\",\"bullet vibe\",\"bullshit\",\"bullshits\",\"bullshitted\",\n               \"bullturds\",\"bum\",\"bum boy\",\"bumblefuck\",\"bumclat\",\"bummer\",\"buncombe\",\"bung\",\"bung hole\",\"bunghole\",\"bunny fucker\",\"bust a load\",\"bust a load\",\n               \"busty\",\"butt\",\"butt fuck\",\"butt fuck\",\"butt plug\",\"buttcheeks\",\"buttfuck\",\"buttfucka\",\"buttfucker\",\"butthole\",\"buttmuch\",\"buttmunch\",\n               \"butt-pirate\",\"buttplug\",\"c.0.c.k\",\"c.o.c.k.\",\"c.u.n.t\",\"c0ck\",\"c-0-c-k\",\"c0cksucker\",\"caca\",\"cacafuego\",\"cahone\",\"camel toe\",\"cameltoe\",\n               \"camgirl\",\"camslut\",\"camwhore\",\"carpet muncher\",\"carpetmuncher\",\"cawk\",\"cervix\",\"chesticle\",\"chi-chi man\",\"chick with a dick\",\"child-fucker\",\n               \"chinc\",\"chincs\",\"chink\",\"chinky\",\"choad\",\"choade\",\"choade\",\"choc ice\",\"chocolate rosebuds\",\"chode\",\"chodes\",\"chota bags\",\"chota bags\",\"cipa\",\n               \"circlejerk\",\"cl1t\",\"cleveland steamer\",\"climax\",\"clit\",\"clit licker\",\"clit licker\",\"clitface\",\"clitfuck\",\"clitoris\",\"clitorus\",\"clits\",\"clitty\",\n               \"clitty litter\",\"clitty litter\",\"clover clamps\",\"clunge\",\"clusterfuck\",\"cnut\",\"cocain\",\"cocaine\",\"coccydynia\",\"cock\",\"c-o-c-k\",\"cock pocket\",\"cock pocket\",\"cock snot\",\"cock snot\",\"cock sucker\",\"cockass\",\"cockbite\",\"cockblock\",\"cockburger\",\"cockeye\",\"cockface\",\"cockfucker\",\"cockhead\",\"cockholster\",\"cockjockey\",\"cockknocker\",\"cockknoker\",\"Cocklump\",\"cockmaster\",\"cockmongler\",\"cockmongruel\",\"cockmonkey\",\"cockmunch\",\"cockmuncher\",\"cocknose\",\"cocknugget\",\"cocks\",\"cockshit\",\"cocksmith\",\"cocksmoke\",\"cocksmoker\",\"cocksniffer\",\"cocksuck\",\"cocksuck\",\"cocksucked\",\"cocksucked\",\"cocksucker\",\"cock-sucker\",\"cocksuckers\",\"cocksucking\",\"cocksucks\",\"cocksucks\",\"cocksuka\",\"cocksukka\",\"cockwaffle\",\"coffin dodger\",\"coital\",\"cok\",\"cokmuncher\",\"coksucka\",\"commie\",\"condom\",\"coochie\",\"coochy\",\"coon\",\"coonnass\",\"coons\",\"cooter\",\"cop some wood\",\"cop some wood\",\"coprolagnia\",\"coprophilia\",\"corksucker\",\"cornhole\",\"cornhole\",\"corp whore\",\"corp whore\",\"corpulent\",\"cox\",\"crabs\",\"crack\",\"cracker\",\"crackwhore\",\"crap\",\"crappy\",\"creampie\",\"cretin\",\"crikey\",\"cripple\",\"crotte\",\"cum\",\"cum chugger\",\"cum chugger\",\"cum dumpster\",\"cum dumpster\",\"cum freak\",\"cum freak\",\"cum guzzler\",\"cum guzzler\",\"cumbubble\",\"cumdump\",\"cumdump\",\"cumdumpster\",\"cumguzzler\",\"cumjockey\",\"cummer\",\"cummin\",\"cumming\",\"cums\",\"cumshot\",\"cumshots\",\"cumslut\",\"cumstain\",\"cumtart\",\"cunilingus\",\"cunillingus\",\"cunnie\",\"cunnilingus\",\"cunny\",\"cunt\",\"c-u-n-t\",\"cunt hair\",\"cunt hair\",\"cuntass\",\"cuntbag\",\"cuntbag\",\"cuntface\",\"cunthole\",\"cunthunter\",\"cuntlick\",\"cuntlick\",\"cuntlicker\",\"cuntlicker\",\"cuntlicking\",\"cuntlicking\",\"cuntrag\",\"cunts\",\"cuntsicle\",\"cuntsicle\",\"cuntslut\",\"cunt-struck\",\"cunt-struck\",\"cus\",\"cut rope\",\"cut rope\",\"cyalis\",\"cyberfuc\",\"cyberfuck\",\"cyberfuck\",\"cyberfucked\",\"cyberfucked\",\"cyberfucker\",\"cyberfuckers\",\"cyberfucking\",\"cyberfucking\",\"d0ng\",\"d0uch3\",\"d0uche\",\"d1ck\",\"d1ld0\",\"d1ldo\",\"dago\",\"dagos\",\"dammit\",\"damn\",\"damned\",\"damnit\",\"darkie\",\"darn\",\"date rape\",\"daterape\",\"dawgie-style\",\"deep throat\",\"deepthroat\",\"deggo\",\"dendrophilia\",\"dick\",\"dick head\",\"dick hole\",\"dick hole\",\"dick shy\",\"dick shy\",\"dickbag\",\"dickbeaters\",\"dickdipper\",\"dickface\",\"dickflipper\",\"dickfuck\",\"dickfucker\",\"dickhead\",\"dickheads\",\"dickhole\",\"dickish\",\"dick-ish\",\"dickjuice\",\"dickmilk\",\"dickmonger\",\"dickripper\",\"dicks\",\"dicksipper\",\"dickslap\",\"dick-sneeze\",\"dicksucker\",\"dicksucking\",\"dicktickler\",\"dickwad\",\"dickweasel\",\"dickweed\",\"dickwhipper\",\"dickwod\",\"dickzipper\",\"diddle\",\"dike\",\"dildo\",\"dildos\",\"diligaf\",\"dillweed\",\"dimwit\",\"dingle\",\"dingleberries\",\"dingleberry\",\"dink\",\"dinks\",\"dipship\",\"dipshit\",\"dirsa\",\"dirty\",\"dirty pillows\",\"dirty sanchez\",\"dirty Sanchez\",\"div\",\"dlck\",\"dog style\",\"dog-fucker\",\"doggie style\",\"doggiestyle\",\"doggie-style\",\"doggin\",\"dogging\",\"doggy style\",\"doggystyle\",\"doggy-style\",\"dolcett\",\"domination\",\"dominatrix\",\"dommes\",\"dong\",\"donkey punch\",\"donkeypunch\",\"donkeyribber\",\"doochbag\",\"doofus\",\"dookie\",\"doosh\",\"dopey\",\"double dong\",\"double penetration\",\"Doublelift\",\"douch3\",\"douche\",\"douchebag\",\"douchebags\",\"douche-fag\",\"douchewaffle\",\"douchey\",\"dp action\",\"drunk\",\"dry hump\",\"duche\",\"dumass\",\"dumb ass\",\"dumbass\",\"dumbasses\",\"Dumbcunt\",\"dumbfuck\",\"dumbshit\",\"dummy\",\"dumshit\",\"dvda\",\"dyke\",\"dykes\",\"eat a dick\",\"eat a dick\",\"eat hair pie\",\"eat hair pie\",\"eat my ass\",\"ecchi\",\"ejaculate\",\"ejaculated\",\"ejaculates\",\"ejaculates\",\"ejaculating\",\"ejaculating\",\"ejaculatings\",\"ejaculation\",\"ejakulate\",\"erect\",\"erection\",\"erotic\",\"erotism\",\"escort\",\"essohbee\",\"eunuch\",\"extacy\",\"extasy\",\"f u c k\",\"f u c k e r\",\"f.u.c.k\",\"f_u_c_k\",\"f4nny\",\"facial\",\"fack\",\"fag\",\"fagbag\",\"fagfucker\",\"fagg\",\"fagged\",\"fagging\",\"faggit\",\"faggitt\",\"faggot\",\"faggotcock\",\"faggots\",\"faggs\",\"fagot\",\"fagots\",\"fags\",\"fagtard\",\"faig\",\"faigt\",\"fanny\",\"fannybandit\",\"fannyflaps\",\"fannyfucker\",\"fanyy\",\"fart\",\"fartknocker\",\"fatass\",\"fcuk\",\"fcuker\",\"fcuking\",\"fecal\",\"feck\",\"fecker\",\"feist\",\"felch\",\"felcher\",\"felching\",\"fellate\",\"fellatio\",\"feltch\",\"feltcher\",\"female squirting\",\"femdom\",\"fenian\",\"fice\",\"figging\",\"fingerbang\",\"fingerfuck\",\"fingerfuck\",\"fingerfucked\",\"fingerfucked\",\"fingerfucker\",\"fingerfucker\",\"fingerfuckers\",\"fingerfucking\",\"fingerfucking\",\"fingerfucks\",\"fingerfucks\",\"fingering\",\"fist fuck\",\"fist fuck\",\"fisted\",\"fistfuck\",\"fistfucked\",\"fistfucked\",\"fistfucker\",\"fistfucker\",\"fistfuckers\",\"fistfuckers\",\"fistfucking\",\"fistfucking\",\"fistfuckings\",\"fistfuckings\",\"fistfucks\",\"fistfucks\",\"fisting\",\"fisty\",\"flamer\",\"flange\",\"flaps\",\"fleshflute\",\"flog the log\",\"flog the log\",\"floozy\",\"foad\",\"foah\",\"fondle\",\"foobar\",\"fook\",\"fooker\",\"foot fetish\",\"footjob\",\"foreskin\",\"freex\",\"frenchify\",\"frigg\",\"frigga\",\"frotting\",\"fubar\",\"fuc\",\"fuck\",\"fuck\",\"f-u-c-k\",\"fuck buttons\",\"fuck hole\",\"fuck hole\",\"Fuck off\",\"fuck puppet\",\"fuck puppet\",\"fuck trophy\",\"fuck trophy\",\"fuck yo mama\",\"fuck yo mama\",\"fuck you\",\"fucka\",\"fuckass\",\"fuck-ass\",\"fuck-ass\",\"fuckbag\",\"fuck-bitch\",\"fuck-bitch\",\"fuckboy\",\"fuckbrain\",\"fuckbutt\",\"fuckbutter\",\"fucked\",\"fuckedup\",\"fucker\",\"fuckers\",\"fuckersucker\",\"fuckface\",\"fuckhead\",\"fuckheads\",\"fuckhole\",\"fuckin\",\"fucking\",\"fuckings\",\"fuckingshitmotherfucker\",\"fuckme\",\"fuckme\",\"fuckmeat\",\"fuckmeat\",\"fucknugget\",\"fucknut\",\"fucknutt\",\"fuckoff\",\"fucks\",\"fuckstick\",\"fucktard\",\"fuck-tard\",\"fucktards\",\"fucktart\",\"fucktoy\",\"fucktoy\",\"fucktwat\",\"fuckup\",\"fuckwad\",\"fuckwhit\",\"fuckwit\",\"fuckwitt\",\"fudge packer\",\"fudgepacker\",\"fudge-packer\",\"fuk\",\"fuker\",\"fukker\",\"fukkers\",\"fukkin\",\"fuks\",\"fukwhit\",\"fukwit\",\"fuq\",\"futanari\",\"fux\",\"fux0r\",\"fvck\",\"fxck\",\"gae\",\"gai\",\"gang bang\",\"gangbang\",\"gang-bang\",\"gang-bang\",\"gangbanged\",\"gangbangs\",\"ganja\",\"gash\",\"gassy ass\",\"gassy ass\",\"gay\",\"gay sex\",\"gayass\",\"gaybob\",\"gaydo\",\"gayfuck\",\"gayfuckist\",\"gaylord\",\"gays\",\"gaysex\",\"gaytard\",\"gaywad\",\"gender bender\",\"genitals\",\"gey\",\"gfy\",\"ghay\",\"ghey\",\"giant cock\",\"gigolo\",\"ginger\",\"gippo\",\"girl on\",\"girl on top\",\"girls gone wild\",\"git\",\"glans\",\"goatcx\",\"goatse\",\"god\",\"god damn\",\"godamn\",\"godamnit\",\"goddam\",\"god-dam\",\"goddammit\",\"goddamn\",\"goddamned\",\"god-damned\",\"goddamnit\",\"godsdamn\",\"gokkun\",\"golden shower\",\"goldenshower\",\"golliwog\",\"gonad\",\"gonads\",\"goo girl\",\"gooch\",\"goodpoop\",\"gook\",\"gooks\",\"goregasm\",\"gringo\",\"grope\",\"group sex\",\"gspot\",\"g-spot\",\"gtfo\",\"guido\",\"guro\",\"h0m0\",\"h0mo\",\"ham flap\",\"ham flap\",\"hand job\",\"handjob\",\"hard core\",\"hard on\",\"hardcore\",\"hardcoresex\",\"he11\",\"hebe\",\"heeb\",\"hell\",\"hemp\",\"hentai\",\n               \"heroin\",\"herp\",\"herpes\",\"herpy\",\"heshe\",\"he-she\",\"hircismus\",\"hitler\",\"hiv\",\"ho\",\"hoar\",\"hoare\",\"hobag\",\"hoe\",\"hoer\",\"holy shit\",\"hom0\",\"homey\",\n               \"homo\",\"homodumbshit\",\"homoerotic\",\"homoey\",\"honkey\",\"honky\",\"hooch\",\"hookah\",\"hooker\",\"hoor\",\"hootch\",\"hooter\",\"hooters\",\"hore\",\"horniest\",\"horny\",\n               \"hot carl\",\"hot chick\",\"hotsex\",\"how to kill\",\"how to murdep\",\"how to murder\",\"huge fat\",\"hump\",\"humped\",\"humping\",\"hun\",\"hussy\",\"hymen\",\"iap\",\n               \"iberian slap\",\"inbred\",\"incest\",\"injun\",\"intercourse\",\"jack off\",\"jackass\",\"jackasses\",\"jackhole\",\"jackoff\",\"jack-off\",\"jaggi\",\"jagoff\",\"jail bait\",\n               \"jailbait\",\"jap\",\"japs\",\"jelly donut\",\"jerk\",\"jerk off\",\"jerk0ff\",\"jerkass\",\"jerked\",\"jerkoff\",\"jerk-off\",\"jigaboo\",\"jiggaboo\",\"jiggerboo\",\"jism\",\n               \"jiz\",\"jiz\",\"jizm\",\"jizm\",\"jizz\",\"jizzed\",\"jock\",\"juggs\",\"jungle bunny\",\"junglebunny\",\"junkie\",\"junky\",\"kafir\",\"kawk\",\"kike\",\"kikes\",\"kill\",\"kinbaku\",\n               \"kinkster\",\"kinky\",\"klan\",\"knob\",\"knob end\",\"knobbing\",\"knobead\",\"knobed\",\"knobend\",\"knobhead\",\"knobjocky\",\"knobjokey\",\"kock\",\"kondum\",\"kondums\",\n               \"kooch\",\"kooches\",\"kootch\",\"kraut\",\"kum\",\"kummer\",\"kumming\",\"kums\",\"kunilingus\",\"kunja\",\"kunt\",\"kwif\",\"kwif\",\"kyke\",\"l3i+ch\",\"l3itch\",\"labia\",\n               \"lameass\",\"lardass\",\"leather restraint\",\"leather straight jacket\",\"lech\",\"lemon party\",\"LEN\",\"leper\",\"lesbian\",\"lesbians\",\"lesbo\",\"lesbos\",\"lez\",\n               \"lezza/lesbo\",\"lezzie\",\"lmao\",\"lmfao\",\"loin\",\"loins\",\"lolita\",\"looney\",\"lovemaking\",\"lube\",\"lust\",\"lusting\",\"lusty\",\"m0f0\",\"m0fo\",\"m45terbate\",\n               \"ma5terb8\",\"ma5terbate\",\"mafugly\",\"mafugly\",\"make me come\",\"male squirting\",\"mams\",\"masochist\",\"massa\",\"masterb8\",\"masterbat*\",\"masterbat3\",\n               \"masterbate\",\"master-bate\",\"master-bate\",\"masterbating\",\"masterbation\",\"masterbations\",\"masturbate\",\"masturbating\",\"masturbation\",\"maxi\",\n               \"mcfagget\",\"menage a trois\",\"menses\",\"menstruate\",\"menstruation\",\"meth\",\"m-fucking\",\"mick\",\"microphallus\",\"middle finger\",\"midget\",\"milf\",\n               \"minge\",\"minger\",\"missionary position\",\"mof0\",\"mofo\",\"mo-fo\",\"molest\",\"mong\",\"moo moo foo foo\",\"moolie\",\"moron\",\"mothafuck\",\"mothafucka\",\n               \"mothafuckas\",\"mothafuckaz\",\"mothafucked\",\"mothafucked\",\"mothafucker\",\"mothafuckers\",\"mothafuckin\",\"mothafucking\",\"mothafucking\",\n               \"mothafuckings\",\"mothafucks\",\"mother fucker\",\"mother fucker\",\"motherfuck\",\"motherfucka\",\"motherfucked\",\"motherfucker\",\"motherfuckers\",\n               \"motherfuckin\",\"motherfucking\",\"motherfuckings\",\"motherfuckka\",\"motherfucks\",\"mound of venus\",\"mr hands\",\"muff\",\"muff diver\",\n               \"muff puff\",\"muff puff\",\"muffdiver\",\"muffdiving\",\"munging\",\"munter\",\"murder\",\"mutha\",\"muthafecker\",\"muthafuckker\",\"muther\",\"mutherfucker\",\n               \"n1gga\",\"n1gger\",\"naked\",\"nambla\",\"napalm\",\"nappy\",\"nawashi\",\"nazi\",\"nazism\",\"need the dick\",\"need the dick\",\"negro\",\"neonazi\",\n               \"nig nog\",\"nigaboo\",\"nigg3r\",\"nigg4h\",\"nigga\",\"niggah\",\"niggas\",\"niggaz\",\"nigger\",\"niggers\",\"niggle\",\"niglet\",\"nig-nog\",\"nimphomania\"\n               ,\"nimrod\",\"ninny\",\"ninnyhammer\",\"nipple\",\"nipples\",\"nob\",\"nob jokey\",\"nobhead\",\"nobjocky\",\"nobjokey\",\"nonce\",\"nsfw images\",\"nude\",\"nudity\",\n               \"numbnuts\",\"nut butter\",\"nut butter\",\"nut sack\",\"nutsack\",\"nutter\",\"nympho\",\"nymphomania\",\"octopussy\",\"old bag\",\"omg\",\"omorashi\",\n               \"one cup two girls\",\"one guy one jar\",\"opiate\",\"opium\",\"orally\",\"organ\",\"orgasim\",\"orgasims\",\"orgasm\",\"orgasmic\",\"orgasms\",\"orgies\",\"orgy\",\"ovary\",\"ovum\",\"ovums\",\"p.u.s.s.y.\",\"p0rn\",\"paedophile\",\"paki\",\"panooch\",\"pansy\",\"pantie\",\"panties\",\"panty\",\"pawn\",\"pcp\",\"pecker\",\"peckerhead\",\"pedo\",\"pedobear\",\"pedophile\",\"pedophilia\",\"pedophiliac\",\"pee\",\"peepee\",\"pegging\",\"penetrate\",\"penetration\",\"penial\",\"penile\",\"penis\",\"penisbanger\",\"penisfucker\",\"penispuffer\",\"perversion\",\"phallic\",\"phone sex\",\"phonesex\",\"phuck\",\"phuk\",\"phuked\",\"phuking\",\"phukked\",\"phukking\",\"phuks\",\"phuq\",\"piece of shit\",\"pigfucker\",\"pikey\",\"pillowbiter\",\"pimp\",\"pimpis\",\"pinko\",\"piss\",\"piss off\",\"piss pig\",\"pissed\",\"pissed off\",\"pisser\",\"pissers\",\"pisses\",\"pisses\",\"pissflaps\",\"pissin\",\"pissin\",\"pissing\",\"pissoff\",\"pissoff\",\"piss-off\",\"pisspig\",\"playboy\",\"pleasure chest\",\"pms\",\"polack\",\"pole smoker\",\"polesmoker\",\"pollock\",\"ponyplay\",\"poof\",\"poon\",\"poonani\",\"poonany\",\"poontang\",\"poop\",\"poop chute\",\"poopchute\",\"Poopuncher\",\"porch monkey\",\"porchmonkey\",\"porn\",\"porno\",\"pornography\",\"pornos\",\"pot\",\"potty\",\"prick\",\"pricks\",\"prickteaser\",\"prig\",\"prince albert piercing\",\"prod\",\"pron\",\"prostitute\",\"prude\",\"psycho\",\"pthc\",\"pube\",\"pubes\",\"pubic\",\"pubis\",\"punani\",\"punanny\",\"punany\",\"punkass\",\"punky\",\"punta\",\"puss\",\"pusse\",\"pussi\",\"pussies\",\"pussy\",\"pussy fart\",\"pussy fart\",\"pussy palace\",\"pussy palace\",\"pussylicking\",\"pussypounder\",\"pussys\",\"pust\",\"puto\",\"queaf\",\"queaf\",\"queef\",\"queer\",\"queerbait\",\"queerhole\",\"queero\",\"queers\",\"quicky\",\"quim\",\"racy\",\"raghead\",\"raging boner\",\"rape\",\"raped\",\"raper\",\"rapey\",\"raping\",\"rapist\",\"raunch\",\"rectal\",\"rectum\",\"rectus\",\"reefer\",\"reetard\",\"reich\",\"renob\",\"retard\",\"retarded\",\"reverse cowgirl\",\"revue\",\"rimjaw\",\"rimjob\",\"rimming\",\"ritard\",\"rosy palm\",\"rosy palm and her 5 sisters\",\"rtard\",\"r-tard\",\"rubbish\",\"rum\",\"rump\",\"rumprammer\",\"ruski\",\"rusty trombone\",\"s hit\",\"s&m\",\"s.h.i.t.\",\"s.o.b.\",\"s_h_i_t\",\"s0b\",\"sadism\",\"sadist\",\"sambo\",\"sand nigger\",\"sandbar\",\"sandbar\",\"Sandler\",\"sandnigger\",\"sanger\",\"santorum\",\"sausage queen\",\"sausage queen\",\"scag\",\"scantily\",\"scat\",\"schizo\",\"schlong\",\"scissoring\",\"screw\",\"screwed\",\"screwing\",\"scroat\",\"scrog\",\"scrot\",\"scrote\",\"scrotum\",\"scrud\",\"scum\",\"seaman\",\"seamen\",\"seduce\",\"seks\",\"semen\",\"sex\",\"sexo\",\"sexual\",\"sexy\",\"sh!+\",\"sh!t\",\"sh1t\",\"s-h-1-t\",\"shag\",\"shagger\",\"shaggin\",\"shagging\",\"shamedame\",\"shaved beaver\",\"shaved pussy\",\"shemale\",\"shi+\",\"shibari\",\"shirt lifter\",\"shit\",\"s-h-i-t\",\"shit ass\",\"shit fucker\",\"shit fucker\",\"shitass\",\"shitbag\",\"shitbagger\",\"shitblimp\",\"shitbrains\",\"shitbreath\",\"shitcanned\",\"shitcunt\",\"shitdick\",\"shite\",\"shiteater\",\"shited\",\"shitey\",\"shitface\",\"shitfaced\",\"shitfuck\",\"shitfull\",\"shithead\",\"shitheads\",\"shithole\",\"shithouse\",\"shiting\",\"shitings\",\"shits\",\"shitspitter\",\"shitstain\",\"shitt\",\"shitted\",\"shitter\",\"shitters\",\"shitters\",\"shittier\",\"shittiest\",\"shitting\",\"shittings\",\"shitty\",\"shiz\",\"shiznit\",\"shota\",\"shrimping\",\"sissy\",\"skag\",\"skank\",\"skeet\",\"skullfuck\",\"slag\",\"slanteye\",\"slave\",\"sleaze\",\"sleazy\",\"slope\",\"slope\",\"slut\",\"slut bucket\",\"slut bucket\",\"slutbag\",\"slutdumper\",\"slutkiss\",\"sluts\",\"smartass\",\"smartasses\",\"smeg\",\"smegma\",\"smut\",\"smutty\",\"snatch\",\"sniper\",\"snowballing\",\"snuff\",\"s-o-b\",\"sod off\",\"sodom\",\"sodomize\",\"sodomy\",\"son of a bitch\",\"son of a motherless goat\",\"son of a whore\",\"son-of-a-bitch\",\"souse\",\"soused\",\"spac\",\"spade\",\"sperm\",\"spic\",\"spick\",\"spik\",\"spiks\",\"splooge\",\"splooge moose\",\"spooge\",\"spook\",\"spread legs\",\"spunk\",\"stfu\",\"stiffy\",\"stoned\",\"strap on\",\"strapon\",\"strappado\",\"strip\",\"strip club\",\"stroke\",\"stupid\",\"style doggy\",\"suck\",\"suckass\",\"sucked\",\"sucking\",\"sucks\",\"suicide girls\",\"sultry women\",\"sumofabiatch\",\"swastika\",\"swinger\",\"t1t\",\"t1tt1e5\",\"t1tties\",\"taff\",\"taig\",\"tainted love\",\"taking the piss\",\"tampon\",\"tard\",\"tart\",\"taste my\",\"tawdry\",\"tea bagging\",\"teabagging\",\"teat\",\"teets\",\"teez\",\"teste\",\"testee\",\"testes\",\"testical\",\"testicle\",\"testis\",\"threesome\",\"throating\",\"thrust\",\"thug\",\"thundercunt\",\"tied up\",\"tight white\",\"tinkle\",\"tit\",\"tit wank\",\"tit wank\",\"titfuck\",\"titi\",\"tities\",\"tits\",\"titt\",\"tittie5\",\"tittiefucker\",\"titties\",\"titty\",\"tittyfuck\",\"tittyfucker\",\"tittywank\",\"titwank\",\"toke\",\"tongue in a\",\"toots\",\"topless\",\"tosser\",\"towelhead\",\"tramp\",\"tranny\",\"transsexual\",\"trashy\",\"tribadism\",\"trumped\",\"tub girl\",\"tubgirl\",\"turd\",\"tush\",\"tushy\",\"tw4t\",\"twat\",\"twathead\",\"twatlips\",\"twats\",\"twatty\",\"twatwaffle\",\"twink\",\"twinkie\",\"two fingers\",\"two fingers with tongue\",\"two girls one cup\",\"twunt\",\"twunter\",\"ugly\",\"unclefucker\",\"undies\",\"undressing\",\"unwed\",\"upskirt\",\"urethra play\",\"urinal\",\"urine\",\"urophilia\",\"uterus\",\"uzi\",\"v14gra\",\"v1gra\",\"vag\",\"vagina\",\"vajayjay\",\"va-j-j\",\"valium\",\"venus mound\",\"veqtable\",\"viagra\",\"vibrator\",\"violet wand\",\"virgin\",\"vixen\",\"vjayjay\",\"vodka\",\"vomit\",\"vorarephilia\",\"voyeur\",\"vulgar\",\"vulva\",\"w00se\",\"wad\",\"wang\",\"wank\",\"wanker\",\"wankjob\",\"wanky\",\"wazoo\",\"wedgie\",\"weed\",\"weenie\",\"weewee\",\"weiner\",\"weirdo\",\"wench\",\"wet dream\",\"wetback\",\"wh0re\",\"wh0reface\",\"white power\",\"whiz\",\"whoar\",\"whoralicious\",\"whore\",\"whorealicious\",\"whorebag\",\"whored\",\"whoreface\",\"whorehopper\",\"whorehouse\",\"whores\",\"whoring\",\"wigger\",\"willies\",\"willy\",\"window licker\",\"wiseass\",\"wiseasses\",\"wog\",\"womb\",\"wop\",\"wrapping men\",\"wrinkled starfish\",\"wtf\",\"xrated\",\"x-rated\",\"xx\",\"xxx\",\"yaoi\",\"yeasty\",\"yellow showers\",\"yid\",\"yiffy\",\"yobbo\",\"zibbi\",\"zoophilia\",\"zubb\", \"muslim\", \"islam\", \"arab\", \"pakistan\", \"christian\", \"christianity\", \"jew\", \"jewish\", \"hindu\", \"athiest\", \"athiesm\", \n                \"agnostic\", \"religion\", \"race\", \"racist\", \"racism\", \"white\", \"black\", \"african\", \"latin\", \"latino\", \"caucasian\", \n                \"conservative\", \"liberal\", \"democrat\", \"trump\", \"obama\"]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"191a2165549f8a5e22c20b8f3e62305efd93fe21"},"cell_type":"code","source":"swear_words = set(swear_words)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"5a7594a3a8c579f673e5732f408ac9486ed494e6"},"cell_type":"code","source":"def count_swear(text):\n    count = sum([1 for char in text if char in swear_words])\n    return(count)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d6a6a99b0ca5f61f2f578bfadb956978784b06a3"},"cell_type":"markdown","source":"### Now let's apply all those functions and add a few other features along the way:\n1.  nsent: number of sentences\n2. nwords: number of words\n3. nuniquerwords: number of unique words\n4. richness: lexical richness of the question (meaning using less unique words to convey the same message)"},{"metadata":{"trusted":true,"_uuid":"362728ef1078f5bfa78198f482c1bfcfc975c574"},"cell_type":"code","source":"#no. of sentences\ndf_train['sent_tokens'] = df_train['question_text'].apply(lambda x: nltk.tokenize.sent_tokenize(x))\ndf_train['nsent'] = df_train['sent_tokens'].apply(lambda x: len(x)-x.count(''))\n\n#no. of words\ndf_train['nwords'] = df_train['question_text'].apply(lambda x: len([x for x in str(x).split() if x not in string.punctuation]))\n\n#no. of unique words\ndf_train['nuniquewords'] = df_train['clean_text'].apply(lambda x: len(x)-x.count(''))\n\n#% punctuation \ndf_train['punct%'] = df_train['question_text'].apply(lambda x: count_punct(x))\n\n#%upper\ndf_train['upper%'] = df_train['question_text'].apply(lambda x: count_uppercase(x))\n\n#no. of swear words\ndf_train['swear%'] = df_train['clean_text'].apply(lambda x: count_swear(x))\n\n#lexcial richness\ndf_train['richness'] = ((df_train['nwords'] - df_train['nuniquewords'])/(df_train['nwords']) * 100)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"86116bc3dcbe7defe8533014c21f414e917875d4"},"cell_type":"code","source":"df_train.set_index('qid', inplace=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"66d65d4c31a0a5bc697a347d034870e4a93ee252"},"cell_type":"code","source":"columns_titles = ['question_text', 'sent_tokens', 'clean_text', 'nwords', 'nuniquewords', 'nsent', 'punct%', 'upper%', \n                  'swear%', 'richness', 'target']\ndf_train = df_train.reindex(columns = columns_titles)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"34f390657522edec6ea961c891627238d894240e"},"cell_type":"code","source":"df_train.head()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"a500457b3a80cbdd26283b87d65a0c06ae59c661"},"cell_type":"markdown","source":"### Let's take a brief look at the max value for each of the created features"},{"metadata":{"trusted":true,"_uuid":"984e2959f457ae77efd5b5e7fc13f4545203a0e9","scrolled":false},"cell_type":"code","source":"max_words = df_train['nwords'].max()\nmax_unique_words = df_train['nuniquewords'].max()\nmax_sent = df_train['nsent'].max()\nmax_punct = df_train['punct%'].max()\nmax_upper = df_train['upper%'].max()\nmax_swear = df_train['swear%'].max()\nmin_richness = df_train['richness'].min()\nmax_richness = df_train['richness'].max()\n\nprint('Max no. of words: {}'.format(max_words))\nprint('Max no. of unique words: {}'.format(max_unique_words))\nprint('Max no. of sentences: {}'.format(max_sent))\nprint('Max % of punctuation: {}'.format(max_punct))\nprint('Max % of upper chars: {}'.format(max_upper))\nprint('Max no. of swear words: {}'.format(max_swear))\nprint('Max richness: {}'.format(max_richness))\nprint('Min richness: {}'.format(min_richness))","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"271140a661ab053fa650a2b77dd77a7071593cf5"},"cell_type":"markdown","source":"### Min richness is negative in some instances let's check them below"},{"metadata":{"trusted":true,"scrolled":false,"_uuid":"52d6ff63aa53f9d73843d097e3c486eee8e39290"},"cell_type":"code","source":"df_train[df_train['richness']<0]","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"30332196d67e6681ec98faee9af5f462b3627df1"},"cell_type":"markdown","source":" ### Let's impute richness value for those rows to be zero"},{"metadata":{"trusted":true,"_uuid":"89835c87eea637b120d12a862bc4d0d6a86a1903"},"cell_type":"code","source":"df_train['richness'][df_train['richness']<0] = 0\n\nmin_richness = df_train['richness'].min()\nmax_richness = df_train['richness'].max()\nprint('Max richness: {}'.format(max_richness))\nprint('Min richness: {}'.format(min_richness))","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"79564826df34d50246529dd89977cf46f7a0b0c0"},"cell_type":"markdown","source":"### Drop any N/A values that may be created due to the new features"},{"metadata":{"trusted":true,"_uuid":"47d137275aa52d8161797c7b72207f3915f1b000"},"cell_type":"code","source":"df_train = df_train.dropna()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"9f86b05d521565a4f6291c85ca6fb5e6ee28399d"},"cell_type":"markdown","source":"### Finally let's take a look at the distribution of those features for sincere vs. insincere questions to see if they add any value"},{"metadata":{"trusted":true,"_uuid":"26ff0c89b439d22f57b51e635408c920eec97d9b","scrolled":true},"cell_type":"code","source":"features = ['nwords', 'nuniquewords', 'nsent', 'punct%', 'upper%','swear%', 'richness']\nmax_numbers = [max_words, max_unique_words, max_sent, max_punct, max_upper, max_swear, max_richness]\n\nfor feat, m in zip(features, max_numbers):\n    bins = np.linspace(0, m, 50)\n    plt.hist(df_train[df_train['target']==1][feat], alpha=0.5, bins = bins, normed=True, label = 'Insincere question')\n    plt.hist(df_train[df_train['target']==0][feat], alpha=0.5, bins = bins, normed=True, label = 'Sincere question')\n    plt.legend(loc='upper right')\n    plt.title(feat)\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"2fcc26b55029533e93af7074867941616bf4f0cb"},"cell_type":"markdown","source":"## Transform features"},{"metadata":{"trusted":true,"_uuid":"3a6c529c3769427f8ab109467952362b1abbf9e1","scrolled":false},"cell_type":"code","source":"features = ['nwords', 'nuniquewords', 'punct%', 'upper%']\nfor feat in features:\n    df_train[feat] = np.log1p(df_train[feat])\n    plt.hist(df_train[feat])\n    plt.title(feat)\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"fdcc97c181f24d10e2af7a482e05fb1d884a64de"},"cell_type":"code","source":"df_train = df_train.dropna()","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"2fc1256012bd567f02efed74450f6ecd35ba3819"},"cell_type":"markdown","source":"# Machine Learning"},{"metadata":{"_uuid":"848e8f2e9e7e6bfe055f92cc1587949f81adc527"},"cell_type":"markdown","source":"### Import Libraries"},{"metadata":{"trusted":true,"_uuid":"384645ffa0caf20cbc75ee06af51c701ae6ea5c3"},"cell_type":"code","source":"from sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport scipy","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"03ad6347c4fe1ed50fa1a362a90e0efab2c835b2"},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.linear_model import RidgeClassifier\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.preprocessing import RobustScaler\nfrom xgboost import XGBClassifier\nfrom sklearn.metrics import f1_score","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"2a70c2775766c4df420a3e624fce7437b62c8b4f"},"cell_type":"markdown","source":"### I will use Tfidf Vectorizer with the clean text function created before"},{"metadata":{"trusted":true,"_uuid":"ec870dc98c1d9e936ce318f9f6bafe18a647fc2e"},"cell_type":"code","source":"tfidf = TfidfVectorizer(analyzer=clean_text)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"1154bab22a04bacfd5a2f46f063a36279e2a6316"},"cell_type":"code","source":"X = df_train.drop(columns=['target', 'clean_text', 'sent_tokens'])\ny = df_train['target']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"317d7a26d38362b629b60b6142e93806acc0e793"},"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"cdf5442093a2ec20f9cb5f212ec4181d2e38afe3"},"cell_type":"markdown","source":"### Scaling features"},{"metadata":{"trusted":true,"_uuid":"7b247d5b858638f9361e7e3b15322ec11cf40f2b"},"cell_type":"code","source":"scaler = StandardScaler()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"8ab490c2812c0bb09e143297caa471cc8882a9ac"},"cell_type":"code","source":"tfidf_fit = tfidf.fit(X_train['question_text'])\n\nX_train_vect = tfidf_fit.transform(X_train['question_text'])\nX_test_vect = tfidf_fit.transform(X_test['question_text'])","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"21192dcbea7d98ffaf6a175dd797487bc60307a5"},"cell_type":"markdown","source":"### TFIDF outputs a sparse matrix, so I will create sparse matricies for all features I created to be added to the sparse matrix output of TFIDF for 'question_text' column"},{"metadata":{"trusted":true,"_uuid":"2203d10325fb504ca9b871db491a47c86fb00dd2"},"cell_type":"code","source":"feature_train = X_train.drop(columns=['question_text'])\nfeature_test = X_test.drop(columns=['question_text'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"b0839092baea6b7407d39d4bf34c90efe5f4fa2f"},"cell_type":"code","source":"feature_train = scaler.fit_transform(feature_train)\nfeature_test = scaler.transform(feature_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"f9fbdea0f3bcf23c384a2c91108537d4b6d96a1d"},"cell_type":"code","source":"X_train_sparse = scipy.sparse.csr_matrix(feature_train)\nX_test_sparse = scipy.sparse.csr_matrix(feature_test)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"718d951b33e76a90644a0a61ddf1bbdfa054b329"},"cell_type":"markdown","source":"### Now let's combine both matricies using hstack"},{"metadata":{"trusted":true,"_uuid":"a85c25c5e23aa07143e8ed3a9f853b8c848f9bb4"},"cell_type":"code","source":"X_train_stacked = scipy.sparse.hstack((X_train_vect, X_train_sparse))\nX_test_stacked = scipy.sparse.hstack((X_test_vect, X_test_sparse))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"01d80916bda368f61d6dbd6e82c5b3a488f26286"},"cell_type":"code","source":"X_train = X_train_stacked\nX_test = X_test_stacked","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"1393753a7f4a7d6a98f1fb567317ed1935f1fa45"},"cell_type":"code","source":"X_all = scipy.sparse.vstack((X_train, X_test))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"77c706472f91b70c65cdc9004815aeec49177f76"},"cell_type":"code","source":"X_all","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"7707a491716761c7ac6abf4992f351e8b8c0b533"},"cell_type":"code","source":"# lg = LogisticRegression()\n# lg_model = lg.fit(X_train, y_train)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"3850c5513d20d8801df9558053dc1f27508fa12d"},"cell_type":"code","source":"# y_pred = lg_model.predict(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"5c90782d288319bfe75066c1a1d6ca5a09c5fab3"},"cell_type":"code","source":"# fscore = f1_score(y_test, y_pred, pos_label= 1, average='binary')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"eca6fb084c4943e69bb08e65f64bc4c5af71c855"},"cell_type":"code","source":"# print('Fscore: {} / Accuracy: {}'.format(fscore, round((y_pred==y_test).sum() / len(y_pred),3)))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"ffb9ffbc854b55151c330a2f502b2980efe4190c"},"cell_type":"code","source":"# rf = RandomForestClassifier(n_jobs=-1)\n# rf_model = rf.fit(X_train, y_train)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"82b0d42780da54058fdf7f62fecc2a807f909731"},"cell_type":"code","source":"# y_pred = rf_model.predict(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"cff91dfc23d6f7228598b05c15fa774f57ac1513"},"cell_type":"code","source":"# fscore = f1_score(y_test, y_pred, pos_label= 1, average='binary')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"e100c125e4b04cb8a92ce5678f65ca52f01f52fb"},"cell_type":"code","source":"# print('Fscore: {} / Accuracy: {}'.format(fscore, round((y_pred==y_test).sum() / len(y_pred),3)))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"3cb260f142cc02828dc1f0fa4a4d058153435f3d"},"cell_type":"code","source":"# #Getting the importances from the rf model\n# importances = rf_model.feature_importances_\n\n# #getting feature names\n# features = list(X.columns)\n\n# feature_importance = sorted(zip(importances, features), reverse=True)\n\n# df_importances = pd.DataFrame(feature_importance, columns = ['importances', 'features'])\n\n# df_importances.head(10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"2b946f034c461baa78bc14442836a3a2e9139776"},"cell_type":"code","source":"# xg = XGBClassifier(n_jobs=-1)\n# xg_model = xg.fit(X_train, y_train)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"3791ee69f28c6c39cef264f6b8401efbf11e3367"},"cell_type":"code","source":"# y_pred = xg_model.predict(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"3d05f92eb6485d102882c406b152a27db965c0cb"},"cell_type":"code","source":"# fscore = f1_score(y_test, y_pred, pos_label= 1, average='binary')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"3c3f6b6d4d3bdc74cccb3b471f10f08df456875f"},"cell_type":"code","source":"# print('Fscore: {} / Accuracy: {}'.format(fscore, round((y_pred==y_test).sum() / len(y_pred),3)))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"97f7fc2c5551fd52acfb8e8c92dced66ddb80b42"},"cell_type":"code","source":"# def xg_iterator(lr, n_estimators, max_depth):\n#     xg = XGBClassifier(learning_rate = lr, n_estimators = n_estimators, max_depth = max_depth, n_jobs= 4)\n#     xg_model = xg.fit(X_train, y_train)\n#     y_pred = xg_model.predict(X_test)\n#     fscore = f1_score(y_test, y_pred, pos_label= 1, average='binary')\n#     print('lr: {} / n_estimators: {} / max_depth: {} ------- Fscore: {} / Accuracy: {}'.format(lr, n_estimators, max_depth, fscore, round((y_pred==y_test).sum() / len(y_pred),3)))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"11ef7a4558736d49e0e3416e568c67d15c7ccc37"},"cell_type":"code","source":"# for lr in [0.1, 0.01]:   \n#     for n_estimators in [10]:\n#         for max_depth in [100, 150]:\n#             xg_iterator(lr, n_estimators, max_depth)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"cc926a38008a442048642aa30ce99100d6af4869"},"cell_type":"code","source":"# def lg_iterator(c):\n#     lg = LogisticRegression(penalty = 'l2', C=c)\n#     lg_model = lg.fit(X_train, y_train)\n#     y_pred = lg_model.predict(X_test)\n#     fscore = f1_score(y_test, y_pred, pos_label= 1, average='binary')\n#     print('C: {} ------- Fscore: {} / Accuracy: {}'.format(c, fscore, round((y_pred==y_test).sum() / len(y_pred),3)))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"023ed3bde372489005b36e35a5b8b4ca5e1aa0e8","scrolled":true},"cell_type":"code","source":"# for c in [10, 20, 30]:   \n#     lg_iterator(c)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"d9ba147c188a04857794cdb94d3414c286d3ec66"},"cell_type":"code","source":"lg = LogisticRegression(penalty = 'l2', C=20)\nlg_model = lg.fit(X_train, y_train)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"60298ac03641dc2c25ef1a8d1edb26a94f6cd984"},"cell_type":"code","source":"# def ridge_iterator(alpha):\n#     ridge = RidgeClassifier(alpha = alpha)\n#     ridge_model = ridge.fit(X_train, y_train)\n#     y_pred = ridge_model.predict(X_test)\n#     fscore = f1_score(y_test, y_pred, pos_label= 1, average='binary')\n#     print('Alpha: {} ------- Fscore: {} / Accuracy: {}'.format(alpha, fscore, round((y_pred==y_test).sum() / len(y_pred),3)))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"5c5034cd7dd4b8440bf9334a8ff47a4dfb551422"},"cell_type":"code","source":"# for alpha in [1, 1.5, 2, 2.5, 3]:   \n#     ridge_iterator(alpha)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"0d3b46e4f32cd2657388a009999b7d079bce3f67"},"cell_type":"markdown","source":"# For test data"},{"metadata":{"trusted":true,"_uuid":"9727ffd11ac707f6494a8b29a050247502061bd5"},"cell_type":"code","source":"df_test['clean_text'] = df_test['question_text'].apply(lambda x: clean_text(x))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"299c9da1bd4e8ec72a967787e3b8a3bf52a57260"},"cell_type":"code","source":"#no. of sentences\ndf_test['sent_tokens'] = df_test['question_text'].apply(lambda x: nltk.tokenize.sent_tokenize(x))\ndf_test['nsent'] = df_test['sent_tokens'].apply(lambda x: len(x)-x.count(''))\n\n#no. of words\ndf_test['nwords'] = df_test['question_text'].apply(lambda x: len([x for x in str(x).split() if x not in string.punctuation]))\n\n#no. of unique words\ndf_test['nuniquewords'] = df_test['clean_text'].apply(lambda x: len(x)-x.count(''))\n\n#% punctuation \ndf_test['punct%'] = df_test['question_text'].apply(lambda x: count_punct(x))\n\n#%upper\ndf_test['upper%'] = df_test['question_text'].apply(lambda x: count_uppercase(x))\n\n#no. of swear words\ndf_test['swear%'] = df_test['question_text'].apply(lambda x: count_swear(x))\n\n#lexcial richness\ndf_test['richness'] = ((df_test['nwords'] - df_test['nuniquewords'])/(df_test['nwords']))*100","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"3144a9f4da5e1e0a3681939f06f41a4a44fac0e2"},"cell_type":"code","source":"df_test.set_index('qid', inplace=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"bd2b342fb2f76800238b88663eaa2d5c3305b7d8"},"cell_type":"code","source":"columns_titles = ['question_text', 'sent_tokens', 'clean_text', 'nwords', 'nuniquewords', 'nsent', 'punct%', 'upper%', \n                  'swear%', 'richness']\ndf_test = df_test.reindex(columns = columns_titles)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"7aacfeaa138a053ed89c1aa318b11d70d4d1cbf5"},"cell_type":"code","source":"df_test.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"2c122a23702127f55110edaee259e4051c6182ca"},"cell_type":"code","source":"df_test = df_test.drop(columns=['clean_text', 'sent_tokens'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"1a5fa2a26ef112abedf7fd5bbaa1f9379f033c0c"},"cell_type":"code","source":"df_test['richness'][df_test['richness']<0] = 0\n\nmin_richness = df_test['richness'].min()\nmax_richness = df_test['richness'].max()\nprint('Max richness: {}'.format(max_richness))\nprint('Min richness: {}'.format(min_richness))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"a24468b870ed9b0d9abdc484c0333d27fcde3985"},"cell_type":"code","source":"features = ['nwords', 'nuniquewords', 'punct%', 'upper%']\nfor feat in features:\n    df_test[feat] = np.log1p(df_test[feat])\n    plt.hist(df_test[feat])\n    plt.title(feat)\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"6a423d0b4286732c7327eb727cec50043c4d47d3"},"cell_type":"code","source":"feature_test = df_test.drop(columns=['question_text'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"d198e80529b85cb4c3a637778fa714fa1b167cd9"},"cell_type":"code","source":"feature_test = scaler.transform(feature_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"3b2e18c5a10e1d736ccc11a310a7534d4e6fc14b"},"cell_type":"code","source":"test = tfidf_fit.transform(df_test['question_text'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"cebc7f1d26ce135363b916700405fdbe3ec52fba"},"cell_type":"code","source":"test_sparse = scipy.sparse.csr_matrix(feature_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"9c9d1b4bf79eef753d47b08e7564f05a75a44524"},"cell_type":"code","source":"test_stacked = scipy.sparse.hstack((test, test_sparse))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"aabb44088b33a4c600e5c7b950ae7659c571d5c3"},"cell_type":"code","source":"pred = lg_model.predict(test_stacked)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"76551c02249fff2b7a2d540fc0ebfee3d0a25e9f"},"cell_type":"code","source":"df_test.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"45d76bc7d11e20ac57c1cf4611e9678bc88ca9ef"},"cell_type":"code","source":"len(pred)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"6ac47c10850a88bc0f11ce3f6cc365ac80b10f06"},"cell_type":"code","source":"submission = pd.DataFrame()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"9b339a431b862276a2f268ae882ac5caf6157201"},"cell_type":"code","source":"submission['qid'] = df_test.index\nsubmission['prediction'] = pred","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"b222aef4bc44bc2d0c5d78c20d0649457ff8be43"},"cell_type":"code","source":"submission.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"3e2295d4aa2573bd24ff97454fdd2174651ec4b6"},"cell_type":"code","source":"submission['prediction'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"6dc7dae45520d25433fd92ac64291e83a8c714e4"},"cell_type":"code","source":"submission.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"14bddf676531b4654c1c62a56485f13661c6e6fc"},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}