CHAKANA SAVDODA MAHSULOT TALABINI BASHORATLASH USULLARI
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https://doi.org/10.5281/zenodo.21337812##article.subject##:
chakana savdo, talab bashorati, M5 bashoratlash, mashinali o‘qitish, chuqur o‘qitish, XGBoost, LightGBM, LSTM, ansambl.##article.abstract##
Chakana savdo tizimlarida mahsulotlarga bo‘lgan talabni oldindan aniqlash, zaxira boshqaruvi va marketing
qarorlarini samarali tashkil etishda muhim ahamiyatga ega. Mazkur maqolada M5 bashoratlash ma’lumotlar bazasi
asosida Walmart do‘konlar tarmog‘idagi mahsulotlar savdosi tahlil qilinib, har bir mahsulot–do‘kon juftligi uchun navbatdagi
oy talab darajasini bashoratlash masalasi ko‘rib chiqilgan. Kunlik savdo ma’lumotlari oylar bo‘yicha agregatsiyalanib,
navbatdagi oy uchun talab miqdori o‘quv to‘plami mediana qiymati asosida yuqori va past talab darajalariga ajratilgan.
Tadqiqotda Logistik regressiya, XGBoost, LightGBM, LSTM va ansambl modellari samaradorligi accuracy, precision,
recall, F1-score va chalkashlik matritsalari orqali baholangan. Eng yaxshi natijani gradiyent busting modellari ta’minladi,
ya’ni XGBoost va ansambl usullari test to‘plamida aniqlik bo‘yicha 85 % va F1-macro ko‘rsatkichi bo‘yicha 83 % natijani
ta’minladi. Olingan natijalar mashinali va chuqur o‘qitish modellarining mahsulot talab kategoriyalarini aniqlashdagi
imkoniyatlari yuqori ekanligini ko‘rsatdi
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