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Data Science module 1 statistics of data science
- 1. Module -3 Deta Wacnzun J4s poOCess conveghng Mappina a dala 8 f ting it eady f analsis . J t involveg movin3 o Combining comdep dola aCCaR,ble g eose to analyse 3et o mace hem lgo Enou a3 DaA munging , tongshn cleanung ,orRnzing buSformig aus data nto desIx fosMa far aualysy to be gecd goY deusim makin .Make auw dta ugable. C o m b i e dala iow Vo9ou sous Co adt cetna bocaôn POAS DALO dasa o xauixd Sormat umdalgtamd busines ontex of dal . Acutomoted Integnoatim tools o s e s {r a Ca -data se amalysis. 5. clean dalk lom NOISe,muging lauves elemetg 6. 4elp busineas USes fdake timey deuisions decistong. S i s O6 Spam ViagRa efence4 Cspelling chaun) .Any mal containtn Yaa efeces Cspelling dhawng . Lemn o s u b j e Clot o exdamohen. pumchiotuon) Sugshn for spam 'tdevdicaim T u y a poobabliue Mdel 3. y -NN, Linea Regpeon.
- 2. hy Lneasn Rogiesgion &k -for leaing spam. isute about uinean ReagoogO) spam fHeaun 3. Cenaide dotaset ag a malic ,whee eoch od CoTCLPendg-to a emoi. d i f l e u 3Ccaiu columns fur eath doydg, heee Viafa' a uolumn 4 Ony emad Contain the 0 d Viagia, Bhon that Column Alled uith value 1 elge assin o alkanalvey one c a put no. o imes e oorld appeal 5. for ineoa ReReO we need training eMal whee be lab eled wth cutcome vasiabde email haue be i.e spam à Aot 6. A humam gooe spam Rg tabe ling tak c a l be d fo dodtig T . n e h Romeeon Wmodo e buil buil 8 An emau h o u t lobel 9 gve to pedict-he labals 9 TaslEA binvy Cofoy not spam, 1 osspam) 10 dn LineA Paspesiom oudcome ig a numbeh amd coninoMs evau Choose a Cntesl value, 3 Pedcled valueg' aboue tt outpud 4 , belous hen outpuut u 'o' 12 J+ donoF uRk beuause o u toD Many Ua9uablep te l0,000 eMoulk wukh der O ,00,aDO W6rde not 1 Thue Camot be t r d tna MaaX n o t invegtsble TR invests ble 13. 4. we could uaut tha D. wuDds, but shl ineas appropale to binasuy o u t c e m e wuDdg, but shil ineaA O U t t L O M A PeRaos wot
- 3. hy k-NN dLD not uskus Spam Haing wute abeu -NN 2 eMcuile aRe paegeuted as Malsu x., uuth Ou06or emoud Owid columng i ? 3 Malux eibue9 ale ether o r 1 depemdung on peence h o t wid neos, basld on 4Fos k-NN, tud e m o as ad < be L usdg thoy both con+aLn. Loo manu dmenSiong g 5. HeI 1,00, Do uode uul have Cornpuhna diShance m OD, oD0 - dimevsional spoce which a s e LDt Compuodh m wk 63uMs Rom ue O dimeusionalilty& ut maka K-NN PooY olgRth m D1gut Recognihon Rappee eath in a 16x16 pixel grid UnwsaP 16x1b qid into 256 dmensiomal space veCctonize ap Py ENN tune Acclay, Confutosn a NaLive Bayes taud -classacahon nethod bosed on bayes tad Exanple Rore dusCasp uuhsL 17 o ppudatian injedad 99 Scck pattewlg tes posdwe 997 healty pabevdg -cst negative GIvOMa potiemt test posiuive,whot g he poobab-lib h a G,iuOA olly sic Ppulodton 0,000 ppl Pahe achuall sKck, 99 haalhy tagt + asdeAuy+ Hee SO% 99 hehy 9900pP sick 0opp 9 egl+ 1leg Peson 1les+ 11ppl f997test 980 PP
- 4. Let ,ybe venug ut probablng px),p(y) POX,9) be join poobabliay wheu both hoppeu Londitional poobasduby whA one haPPRnsive nupths has Aoppend P(xl4) PCy)= PCx,u) = P(u|x) PCX olwe kor P ( u ) , a g M Pa) fo PCya)= PCxly) p) PCx) -Le y e{to euen- "Jam Sid o"sick he to ev egt u potdue + P(Sick+) = Pt |sick) p(sick) PCH) o 99 x 0.0 o.99x o.01)+o.olxo.9a) 5 0 7 Naie Baye Spam At foY Jndiuidual wrdg ug Naie Baye O Cuss O a w d , add& to poobab emoid 8 Spam condla oly one u0nd at afme e han wdicaleg non sPam PCSpam) psobablity of SPa PCha Spa on SRam oebalbului of PCham) 1-P(spam) PCwsdspam) pobotsuly owod in sF P(wc|ham) probabluiy ondd in ham emaul wdd tn ham emanl Apply Bayeg La P(SOYe spam PCspam) PCNOd) PCspam |Wád) =
- 5. PCwod)=P(uusd spam) P(Spam)t PlwBd|ham) plham) NO- O spam emoulg Tot No. o emaulg PCSpam) = No ok Non-spam emauls Tbt No emad Pham) Exap EMployee emais with I500 SPams, 362 ham. Meehng wRd oappeass 6 times in spamM Is3 hmag in ham Pepam) =soo ISbot 3672 P Cham) = I- P (spam) l-o-29 = 0.4| 0 Olo6 PCMeehng |spam) 500 PCmeetins Iham) 53 3672 =0.0yl6 PCspamlmeehing) =(meetins|spam) PCSpam) PCmeebin4) 6-o106 O29 (-ol06)0.29+(oo4l6x o1) 0.09 Cmechin5)=POmeehnalspam)PEpam)+PCmeahng|ham) Phem)
- 6. Aspam HHe tor Combining W8de. .Eath emal Rpejevled b a binay w vechh e i y ü 1 r o , depending on appeRame of h uid 3 e be email ve (or ndep for jh wRd denole Spam c PC|C) poobabity thoak emaul veckor is spam C-) pxlc)=TOic C1-0je) who, paubabdiley hat individuual uad in spam cPobabiluy of u hwuRd spam 4. Take Lo on both tda Lto Conuet produuuh to suu] Log CPCxlc)) = v - Lugoi. -eje ( ) Aog Ojc+L-je) log jc t z - ) Loa-Ojc) log jc + logC-0jc) - j log(i-0j) togjc - log(i-0je)+ logC1-8jc) loC | Cu-oje)+ lo C1-0c) logCpoxl) j i t No who j j- LogCjc/t-0jc)) w . (og(1-8j)
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