{"id":7149,"date":"2025-05-31T07:17:35","date_gmt":"2025-05-31T07:17:35","guid":{"rendered":"https:\/\/pivot.uz\/?p=7149"},"modified":"2025-05-31T07:17:36","modified_gmt":"2025-05-31T07:17:36","slug":"startap-muvaffaqiyatining-formulasi-raqamlar-ortidagi-bashorat","status":"publish","type":"post","link":"https:\/\/pivot.uz\/uz\/startap-muvaffaqiyatining-formulasi-raqamlar-ortidagi-bashorat\/","title":{"rendered":"Startap muvaffaqiyatining formulasi: raqamlar ortidagi bashorat"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Startap muvaffaqiyati \u2014 ko\u2018pincha ichki sezgi, omad yoki ilhomlantiruvchi hikoyalarga bog\u2018lab tasvirlanadi. Ammo agar bu muvaffaqiyat aslida aniq ko\u2018rsatkichlar, texnologik izlar va raqamli signal asosida oldindan bashorat qilinishi mumkin bo\u2018lsa-chi? Va agar sun\u2019iy intellekt hali mahsulot taqdim etilmasdan, media shov-shuvlari paydo bo\u2018lmasidan va daromad ko\u2018zga ko\u2018rinmasdan turib, aynan qaysi startaplar yutuqqa erishishini aniqlay olsa-chi?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Aynan shu savollarga javob topishga uringan ilmiy tadqiqot \u2014 <em>\u201cTo shine or not to shine: Startup success prediction by exploiting technological and venture-capital-related features\u201d<\/em>&nbsp;(2025) \u2014 bu borada katta yangilik bo\u2018ldi. Bir nechta tadqiqotchilar tomonidan olib borilgan va<em>&nbsp;Information &amp; Management<\/em>&nbsp;jurnalida chop etilgan bu tadqiqotda machine learning usullari yordamida startaplarning muvaffaqiyatini g\u2018oyat aniqlik bilan bashorat qilish mumkinligi ko\u2018rsatildi.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>Algoritmlar ko\u2018radigan belgilarni asoschilar ko\u2018rmaydi<\/strong><strong><\/strong><\/h5>\n\n\n\n<p class=\"wp-block-paragraph\">Tadqiqotchilar GitHub\u2019dagi repolar, dasturlash tillari, kod yozish chastotasi, investorlar va moliyalashtirish bosqichlariga oid ochiq ma\u2019lumotlarni tahlil qilgan holda, 2000 dan ortiq startap ustida tajriba o\u2018tkazishdi. Algoritmlar muvaffaqiyatli (IPO yoki muvaffaqiyatli chiqib ketgan) va muvaffaqiyatsiz loyihalarni ajratishga o\u2018rgatildi. Natijalar \u2014 hayratlanarli: modelning aniqligi (AUC) 0.85 ni tashkil etdi. Bu \u2014 startaplarning muvaffaqiyatli bo\u2018lish ehtimolini yuqori ishonchlilik bilan aytib beradigan ko\u2018rsatkichdir.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Bashoratda muhim rol o\u2018ynagan omillar:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Texnologik belgilar:<\/strong>\u00a0kod yozish faolligi va sifati, texnologiya stekidagi xilma-xillik, zamonaviy frameworklardan foydalanish.<\/li>\n\n\n\n<li><strong>VC bilan bog\u2018liq xususiyatlar:<\/strong>\u00a0dastlabki investorlarning obro\u2018si va tarmog\u2018i, moliyalashtirish bosqichlarining hajmi va vaqti, hamda startapning venchur ekotizimidagi o\u2018rni.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Qisqasi: muvaffaqiyat iz qoldiradi \u2014 va algoritmlar bu izlarni o\u2018qiy oladi.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>Nega bu venchur sarmoya uchun muhim<\/strong><strong><\/strong><\/h5>\n\n\n\n<p class=\"wp-block-paragraph\">An\u2019anaviy tarzda venchur kapital qarorlari ko\u2018pincha yumshoq signallarga tayangan: asoschining xarizmasi, shaxsiy aloqalar va \u201cichki sezgi\u201d. Ammo moliyalashtirish bozori tobora raqobatlashib, startaplar global miqyosda ko\u2018payar ekan, VClar katta salohiyatga ega, ammo e\u2019tibordan chetda qolayotgan kompaniyalarni qo\u2018ldan boy berish xavfi bilan yuzma-yuz.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ushbu tadqiqot muqobil yechimni taklif qiladi: data asosida tanlov qilish va filtrdan o\u2018tkazish. Algoritmik modellarni skauting jarayoniga qo\u2018shish orqali investorlar yangi bozorlarda yoki ilgari e\u2019tibor berilmagan sohalarda yashirin talantlarni aniqlashlari mumkin.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Algoritmlar startaplarni minglab raqobatchilar bilan solishtira oladi \u2014 masalan, GitHub faolligi, texnologik signal kuchi yoki investorlar tarkibi bo\u2018yicha. Bu VClarga miqyosli va ob\u2019yektiv tahlil beradi. Eng muhimi, bu model subyektiv qarorlar va media shov-shuvlardan kelib chiqadigan kognitiv xatolarni kamaytirishga yordam beradi.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Bunday tizimlar <strong>dastlabki tanlash bosqichini avtomatlashtirib<\/strong>, investorlarning vaqtini eng istiqbolli nomzodlarga chuqur tahlil qilishga yo\u2018naltiradi.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ba\u2019zi VC firmalar allaqachon ichki reyting tizimlaridan foydalanmoqda, lekin bu kabi akademik tadqiqotlar bu yondashuvning ishlashini ilmiy isbot bilan ko\u2018rsatmoqda \u2014 va bu tez orada sohaga yangi standart sifatida kirib kelishi mumkin.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>Ma\u2019lumotga tayanadigan kelajakning xatarlari<\/strong><strong><\/strong><\/h5>\n\n\n\n<p class=\"wp-block-paragraph\">Albatta, hech bir model mukammal emas. Tadqiqotchilarning o\u2018zlari ham tan oladi: algoritmlar tarixiy ma\u2019lumotlar asosida o\u2018qitilgani sababli, ular <strong>mavjud tengsizliklarni mustahkamlashi <\/strong>mumkin \u2014 masalan, faqat elita investorlar bilan bog\u2018langan yoki ma\u2019lum hududlarda joylashgan startaplarni afzal ko\u2018rish ehtimoli mavjud.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Bundan tashqari, startaplar tizimni \u201caldash\u201dga urinishi mumkin: ochiq manbada sayqallangan kodlarni joylash, GitHub faolligini sun\u2019iy oshirish, yoki muvaffaqiyatli startaplarning texnologik strukturasini ko\u2018chirib, o\u2018zlarini kuchli ko\u2018rsatish kabi usullar orqali.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Shunga qaramay, mualliflarning ta\u2019kidlashicha, machine learning inson sezgisini almashtirish uchun emas, balki uni kuchaytirib, birgalikda qaror chiqarish uchun mo\u2018ljallangan. To\u2018g\u2018ri qo\u2018llansa, bu yondashuv inson ko\u2018zi ilg\u2018amaydigan yashirin durdonalarni yuzaga chiqarishi mumkin.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\"><strong>Startaplar va investorlar uchun yangi davr<\/strong><strong><\/strong><\/h5>\n\n\n\n<p class=\"wp-block-paragraph\">Biz ko\u2018pincha garajdagi dahoni romantik tarzda tasavvur qilamiz. Ammo ehtimol haqiqiy burilish nuqtasi garajda emas \u2014 balki asoschilarning qoldirgan ma\u2019lumot izlaridadir.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Endilikda, muvaffaqiyat oldindan bashorat qilinishi va mag\u2018lubiyat erta aniqlanishi mumkin bo\u2018lgan dunyoda, venchur kapital qoidalari tubdan o\u2018zgarish arafasida.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Asoschilar uchun esa xulosa aniq: nima qurayotganingiz, qanday texnologiya bilan qurayotganingiz va ayniqsa \u2014 kim bilan birga qurayotganingiz \u2014 endi nafaqat bozor, balki algoritmlar ko\u2018zida ham taqdiringizni belgilaydi. Investorlar uchun esa \u2014 eng yaxshi kelishuv hayotingizdagi eng katta dataset ichida allaqachon mavjud bo\u2018lishi mumkin. Faqat siz hali uni ko\u2018rmagansiz, xolos.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Tayyorladi: Navro\u2018zaxon Bo\u2018riyeva<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Startap muvaffaqiyati \u2014 ko\u2018pincha ichki sezgi, omad yoki ilhomlantiruvchi hikoyalarga bog\u2018lab tasvirlanadi. Ammo agar bu muvaffaqiyat aslida aniq ko\u2018rsatkichlar, texnologik izlar va raqamli signal asosida oldindan bashorat qilinishi mumkin bo\u2018lsa-chi? Va agar sun\u2019iy intellekt hali mahsulot taqdim etilmasdan, media shov-shuvlari paydo bo\u2018lmasidan va daromad ko\u2018zga ko\u2018rinmasdan turib, aynan qaysi startaplar yutuqqa erishishini aniqlay olsa-chi? Aynan [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":7150,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jnews-multi-image_gallery":[],"jnews_single_post":[],"jnews_primary_category":[],"jnews_override_bookmark_settings":[],"jnews_paywall_metabox":[],"jnews_override_counter":[],"jnews_podcast_option":[],"jnews_podcast_series":[],"footnotes":""},"categories":[116],"tags":[],"jnews-series":[],"class_list":["post-7149","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-startaplar"],"_links":{"self":[{"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/posts\/7149","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/comments?post=7149"}],"version-history":[{"count":1,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/posts\/7149\/revisions"}],"predecessor-version":[{"id":7152,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/posts\/7149\/revisions\/7152"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/media\/7150"}],"wp:attachment":[{"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/media?parent=7149"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/categories?post=7149"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/tags?post=7149"},{"taxonomy":"jnews-series","embeddable":true,"href":"https:\/\/pivot.uz\/uz\/wp-json\/wp\/v2\/jnews-series?post=7149"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}