Zviri Mukati[Viga][Ratidza]
- 1. Titanics
- 2. Irish Flower Classification
- 3. Boston House Price Prediction
- 4. Waini Quality Testing
- 5. Stock Market Prediction
- 6. Kurudziro yefirimu
- 7. Mutoro Wekukodzera Kufanotaura
- 8. Sentiment Analysis uchishandisa Twitter Data
- 9. Future Sales Prediction
- 10. Fake News Detection
- 11. Makuponi Purchase Prediction
- 12. Mutengi Churn Prediction
- 13. Wallmart Sales Forecasting
- 14. Uber Data Analysis
- 15. Covid-19 Ongororo
- mhedziso
Kudzidza kwemichina chidzidzo chakareruka chekudzidzisa chirongwa chekombuta kana algorithm kuti zvishoma nezvishoma uvandudze pane rimwe basa rinounzwa padanho repamusoro. Kuzivikanwa kwemifananidzo, kuona hutsotsi, masisitimu ekurudziro, uye mamwe maapplication emuchina ekudzidza akatoratidza kuve akakurumbira.
Mabasa eML anoita kuti basa revanhu rive nyore uye rinoshanda, kuchengetedza nguva uye kuve nechokwadi chemhando yepamusoro mhedzisiro. Kunyange Google, injini yekutsvaga ine mukurumbira pasi rose, inoshandisa machine learning.
Kubva pakuongorora muvhunzo wemushandisi uye kushandura mhedzisiro zvichibva pamibairo kusvika pakuratidza zvirikuitika misoro uye kushambadza zvine chekuita nemubvunzo, pane dzakasiyana sarudzo dziripo.
Tekinoroji inonzwisisa uye yekuzvigadzirisa haisi kure mune ramangwana.
Imwe yedzakanakisa nzira dzekutanga ndeyekubata maoko uye kugadzira purojekiti. Naizvozvo, takanyora runyoro rwemapurojekiti gumi neshanu epamusoro ekudzidza evatangi vekutanga kuti utange.
1. Titanic
Izvi zvinowanzoonekwa serimwe remabasa makuru uye anonyanya kunakidza kune chero munhu anofarira kudzidza zvakawanda nezve muchina kudzidza. Iyo Titanic dambudziko ipurojekiti yakakurumbira yekudzidza muchina iyo inoshandawo senzira yakanaka yekuziva iyo Kaggle data sainzi chikuva. Iyo Titanic dataset inoumbwa nedata rechokwadi kubva pakunyura kwechikepe chakashata.
Inosanganisira zvinhu zvakaita sezera remunhu, mamiriro ezvehupfumi, murume kana mukadzi, nhamba yekabhini, chiteshi chengarava, uye zvakanyanya kukosha, kuti vakapona here!
Iyo K-Nearest Neighbor maitiro uye sarudzo yemuti classifier yakatsunga kuburitsa yakanakisa mhedzisiro yeprojekiti iyi. Kana iwe uchitsvaga inokurumidza kupera kwevhiki dambudziko kuti uvandudze yako Machine Kudzidza kugona, iyi iri paKaggle ndeyako.
2. Irish Flower Classification
Vanotangisa vanoda iris maruva categorization chirongwa, uye inzvimbo yakanaka yekutanga kana iwe uri mutsva pakudzidza muchina. Kureba kwema sepals uye petals kunosiyanisa iris maruva kubva kune mamwe marudzi. Chinangwa chepurojekiti iyi ndechekuparadzanisa maruva kuita marudzi matatu: Virginia, setosa, uye Versicolor.
Kune maekisesaizi emhando, chirongwa chinoshandisa Iris maruva dataset, iyo inobatsira vadzidzi mukudzidza zvakakosha zvekubata nenhamba hunhu uye data. Iyo iris ruva dataset idiki diki rinogona kuchengetwa mundangariro pasina kudiwa kwekuyera.
3. Boston House Price Prediction
Mumwe anozivikanwa dataset yevatsva mukudzidza muchina ndiyo data yeBoston Housing. Chinangwa chayo ndechekufanotaura tsika dzepamba munzvimbo dzakasiyana dzeBoston. Zvinosanganisira nhamba dzakakosha dzakadai sezera, mutero wezvivakwa, mwero wekuparwa kwemhosva, uye kunyangwe kuva pedyo nenzvimbo dzemabasa, zvese zvinogona kukanganisa mitengo yedzimba.
Iyo dataset iri nyore uye idiki, zvichiita kuti zvive nyore kuyedza nazvo kune manovices. Kuti uone kuti ndezvipi zvinhu zvinopesvedzera mutengo wezvivakwa muBoston, nzira dzekudzoreredza dzinoshandiswa zvakanyanya pamatanho akasiyana. Inzvimbo yakanaka yekudzidzira maitiro ekudzora uye kuongorora kuti anoshanda sei.
4. Waini Quality Testing
Waini chinwiwa chisina kujairika chinodhaka chinoda makore ekuvirisa. Nekuda kweizvozvo, bhodhoro rekare rewaini inodhura uye yemhando yepamusoro waini. Kusarudza bhodhoro rakakodzera rewaini kunoda makore eruzivo rwekuravira waini, uye inogona kuve yekurova-kana-kupotsa maitiro.
Iyo waini yemhando yekuyedza purojekiti inoongorora waini ichishandisa physicochemical bvunzo senge doro level, yakagadziriswa acidity, density, pH, uye zvimwe zvinhu. Iyo purojekiti zvakare inosarudza mhando yewaini yemhando uye huwandu. Nekuda kweizvozvo, kutenga waini kunova mhepo.
5. Stock Market Prediction
Chirongwa ichi chinokatyamadza kuti unoshanda here kana kuti kwete muchikamu chezvemari. Data yemusika wemasheya inodzidzwa zvakanyanya nevadzidzi, mabhizinesi, uye kunyangwe sesosi yemari yechipiri. Kugona kwesaenzi yedata kudzidza uye kuongorora data yakateedzana kwakakosha zvakare. Dhata kubva kumusika wemasheya inzvimbo yakanaka yekutanga.
Chinokosha chekuedza ndechekufanotaura kukosha kweramangwana restock. Izvi zvinobva pakuita kwemusika parizvino pamwe nenhamba dzemakore apfuura. Kaggle anga achiunganidza data pane iyo NIFTY-50 index kubvira 2000, uye parizvino inovandudzwa vhiki nevhiki. Kubva muna Ndira 1, 2000, yanga iine mitengo yemasheya kumasangano anopfuura makumi mashanu.
6. Movie Recommendation
Ndine chokwadi chekuti wakave nekunzwa ikoko mushure mekuona bhaisikopo rakanaka. Wakambonzwa kuda kudzikamisa pfungwa dzako nekunyanyisa-kuona mafirimu akafanana?
Isu tinoziva kuti OTT masevhisi akadai seNetflix akavandudza masisitimu avo ekurudziro zvakanyanya. Semudzidzi wekudzidzira muchina, iwe unofanirwa kunzwisisa kuti maalgorithms akadaro anonangana sei nevatengi zvichienderana nezvavanofarira uye wongororo.
Iyo IMDB dhata yakaiswa paKaggle ingangove imwe yeakazara kwazvo, ichibvumira kurudziro mamodheru kuti aiswe zvichibva pazita remubhaisikopo, chiyero chevatengi, rudzi, uye zvimwe zvinhu. Iyo zvakare inzira yakanaka yekudzidza nezve Yemukati-Yakavakirwa kusefa uye Feature Injiniya.
7. Load Eligibility Prediction
Nyika inotenderera nezvikwereti. Mabhengi'sosi huru yepurofiti inobva mubereko pazvikwereti. Saka ndiwo bhizinesi ravo rakakosha.
Vanhu kana mapoka evanhu vanogona chete kuwedzera hupfumi nekudyara mari mufemu netarisiro yekuiona ichikwira mukukosha mune ramangwana. Pane dzimwe nguva zvakakosha kutsvaga chikwereti kuti ugone kutora njodzi yerudzi urwu uye kunyange kutora mune mamwe mafaro enyika.
Chikwereti chisati chagamuchirwa, mabhanga anowanzo kuve nemaitiro akaomesesa ekutevera. Sezvo zvikwereti chiri chinhu chakakosha muhupenyu hwevanhu vazhinji, kufanotaura kukodzera kwechikwereti icho mumwe munhu anonyorera kunobatsira zvakanyanya, zvichibvumira kuronga zvirinani kunze kwekunge chikwereti chichigamuchirwa kana kurambwa.
8. Sentiment Analysis uchishandisa Twitter Data
Thanks kune pasocial media network se Twitter, Facebook, uye Reddit, maonero ekuwedzera uye maitiro ave nyore kwazvo. Ruzivo urwu runoshandiswa kubvisa maonero pane zviitiko, vanhu, mitambo, uye mamwe misoro. Mafungiro ane chekuita nemigodhi yekudzidza muchina ari kushandiswa munzvimbo dzakasiyana siyana, kusanganisira mishandirapamwe yezvematongerwo enyika uye kuongororwa kwechigadzirwa cheAmazon.
Iyi purojekiti ichaita seyakanaka mune yako portfolio! Kuti uone manzwiro uye aspect-based analysis, matekiniki akadai semashini ekutsigira vector, regression, uye classification algorithms anogona kushandiswa zvakanyanya (kutsvaga chokwadi uye maonero).
9. Future Sales Prediction
Mabhizinesi makuru eB2C uye vatengesi vanoda kuziva kuti yakawanda sei chigadzirwa chimwe nechimwe chiri mudura ravo chichatengeswa. Kufanotaura kwekutengesa kunobatsira varidzi vebhizinesi mukuona kuti ndezvipi zvinhu zviri kudiwa zvakanyanya. Kufembera kwakaringana kwekutengesa kuchaderedza zvakanyanya kutambisa uku uchifungawo kuwedzera kwazvinoita pabhajeti remangwana.
Vatengesi vakaita seWalmart, IKEA, Big Basket, uye Big Bazaar vanoshandisa kufanotaura kwekutengesa kufungidzira kudiwa kwechigadzirwa. Iwe unofanirwa kujairana neakasiyana matekiniki ekuchenesa data rakasvibira kuitira kuvaka akadai mapurojekiti eML. Zvakare, kubata kwakanaka kwekudzokororwa kwekuongorora, kunyanya nyore mutsara kudzoreredza, kunodiwa.
Kune aya marudzi emabasa, iwe unozofanirwa kushandisa maraibhurari seDora, Scrubadub, Pandas, NumPy, nevamwe.
10. Fake News Detection
Ndeimwe yekucheka-kumucheto yekudzidza muchina wakanangana nevana vechikoro. Nhau dzenhema dziri kupararira semoto wesango, sezvatinoziva tese. Zvese zviripo pasocial media, kubva pakubatanidza vanhu kusvika pakuverenga nhau dzezuva nezuva.
Nekuda kweizvozvo, kuona nhau dzenhema kwawedzera kuoma mazuva ano. Mazhinji makuru esocial media network, akadai seFacebook neTwitter, atove nemaalgorithms munzvimbo yekuona nhau dzenhema mukutumira uye mafeed.
Kuti uone nhau dzenhema, iyi mhando yepurojekiti yeML inoda kunyatsonzwisiswa kweakawanda nzira dzeNLP uye kurongedza algorithms (PassiveAggressiveClassifier kana Naive Bayes classifier).
11. Makuponi Purchase Prediction
Vatengi vari kuwedzera kufunga nezvekutenga pamhepo apo coronavirus yakarwisa nyika muna 2020. Nekuda kweizvozvo, nzvimbo dzekutenga dzakamanikidzwa kuchinja bhizinesi ravo online.
Vatengi, kune rumwe rutivi, vachiri kutsvaga mibairo mikuru, sezvavaive muzvitoro, uye vari kuwedzera kuvhima makuponi ekuchengetedza epamusoro. Kune kunyange mawebhusaiti akatsaurirwa kugadzira makuponi evatengi vakadaro. Iwe unogona kudzidza nezve migodhi yedata mukudzidza kwemichina, kugadzira mabhawa magirafu, mapai machati, uye histograms yekuona data, uye chimiro cheinjiniya neprojekti iyi.
Kugadzira fungidziro, iwe unogona zvakare kutarisa mune data imputation nzira dzekutarisira NA hunhu uye cosine kufanana kwezvakasiyana.
12. Mutengi Churn Prediction
Vatengi chinhu chakanyanya kukosha chekambani, uye kuvachengeta kwakakosha kune chero bhizinesi rine chinangwa chekusimudzira mari uye kuvaka hukama hurefu hune chinangwa navo.
Uyezve, mutengo wekutora mutengi mutsva wakakwira zvakapetwa kashanu pane mutengo wekuchengeta iripo. Mutengi Churn/Attrition idambudziko rinozivikanwa rebhizinesi umo vatengi kana vanyoreri vanomira kuita bhizinesi nesevhisi kana kambani.
Ivo havazovezve mutengi anobhadhara. Mutengi anoonekwa seanoshongedzwa kana yanga iri imwe nguva kubva mutengi apedzisira kutaurirana nekambani. Kuziva kana mutengi achizoita churn, pamwe nekupa nekukurumidza ruzivo rwakanangana nekuchengetedza vatengi, kwakakosha kudzikisa churn.
Uropi hwedu hahukwanisi kutarisira kuchinja kwevatengi kune mamiriyoni evatengi; apa ndipo panogona kubatsira kudzidza muchina.
13. Wallmart Sales Forecasting
Imwe yeanonyanya kuzivikanwa mashandisirwo emuchina kudzidza kufanotaura kwekutengesa, uko kunosanganisira kuona maitiro anokanganisa kutengesa kwechigadzirwa uye kutarisira huwandu hwekutengesa kweramangwana.
Iyo Walmart dataset, ine data yekutengesa kubva kunzvimbo makumi mana neshanu, inoshandiswa muchidzidzo ichi chekudzidza muchina. Kutengesa pachitoro, nechikamu, pavhiki nevhiki kunosanganisirwa mudhata. Chinangwa cheichi chirongwa chekudzidza muchina ndechekutarisira kutengeswa kwedhipatimendi rega rega muchitoro chega chega kuitira kuti vagone kuita zvirinani-inotyairwa nedhata chiteshi optimization uye sarudzo dzekuronga zvigadzirwa.
Kushanda neWalmart dhataset kwakaoma sezvo iine yakasarudzwa yekumaka zviitiko zvine chekuita nekutengesa uye inofanirwa kutariswa.
14. Uber Data Analysis
Kana zvasvika pakuita uye kubatanidza kudzidza kwemichina uye kudzidza kwakadzama mumapurogiramu avo, iyo yakakurumbira yekugovera-sevhisi haisi kure kumashure. Gore rega rega, inoita mabhiriyoni enzendo, ichibvumira vafambi kufamba chero nguva yemasikati kana husiku.
Nekuti ine yakakura kudaro mutengi base, inoda yakasarudzika sevhisi yevatengi kugadzirisa zvichemo zvevatengi nekukurumidza sezvinobvira.
Uber ine dhatabheti yemamiriyoni ekutora-yaanogona kushandisa kuongorora uye kuratidza nzendo dzevatengi kuburitsa ruzivo nekuvandudza ruzivo rwevatengi.
15. Covid-19 Ongororo
COVID-19 yatsvaira pasi rose nhasi, uye kwete mupfungwa yedenda. Nepo nyanzvi dzekurapa dziri kutarisisa kugadzira majekiseni anoshanda uye kubaya nyika, data masayendisiti havasi kure kumashure.
Mhosva nyowani, kuverenga kwemazuva ese, kufa, uye nhamba dzekuyedza zvese zviri kuitwa pachena. Kufanotaura kunoitwa zuva nezuva zvichienderana nekubuda kweSARS kwezana ramakore rapfuura. Kune izvi, iwe unogona kushandisa regression ongororo uye kutsigira vector muchina-wakavakirwa kufanotaura modhi.
mhedziso
Kupfupisa, takakurukura mamwe epamusoro eML mapurojekiti anozokubatsira mukuyedza Machine Kudzidza programming pamwe nekubata mazano ayo uye kuita. Kuziva nzira yekubatanidza Kudzidza kweMachina kunogona kukubatsira kufambira mberi muhunyanzvi hwako sezvo tekinoroji inotora muindasitiri yese.
Paunenge uchidzidza Muchina Kudzidza, isu tinokurudzira kuti udzidzise ako pfungwa uye unyore ako ese algorithms. Kunyora maalgorithms paunenge uchidzidza kwakakosha pane kuita chirongwa, uye zvakare zvinokupa iwe mukana wekunzwisisa zvidzidzo nemazvo.
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